5230 lines
864 KiB
Plaintext
5230 lines
864 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 分子过滤分析\n",
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"\n",
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"本notebook用于：\n",
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"1. 读取ring16/temp.csv，添加唯一ID\n",
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"2. 过滤有桥环的分子（rdkit检测到多个环的情况）\n",
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"3. 过滤环上有多酯键的分子\n",
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"4. 过滤环上有酰胺键的分子\n",
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"5. 可视化分析功能\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. 导入必要的库\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"所有模块导入成功!\n"
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]
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}
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],
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"source": [
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"import sys\n",
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"from pathlib import Path\n",
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"\n",
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"# 添加项目根目录到 Python 路径\n",
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"notebook_dir = Path().resolve()\n",
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"project_root = notebook_dir.parent\n",
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"sys.path.insert(0, str(project_root))\n",
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"\n",
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"from rdkit import Chem\n",
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"from rdkit.Chem import Draw\n",
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"from rdkit.Chem.Draw import rdMolDraw2D\n",
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"from IPython.display import SVG, display\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"# 导入项目模块\n",
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"from src.ring_numbering import get_ring_atoms, assign_ring_numbering\n",
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"\n",
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"print(\"所有模块导入成功!\")\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. 读取数据并添加唯一ID\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"原始数据集: 2022 个分子\n",
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"列名: ['IDs', 'molecule_pref_name', 'max_pChEMBL', 'max_pChEMBL_target', '# Target Organisms', 'Target Organisms', '# Known Targets', 'Known Targets', 'target_pref_name', 'smiles']\n",
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"\n",
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"已添加唯一ID列\n",
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"前5行:\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
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" vertical-align: middle;\n",
|
||
" }\n",
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||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
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||
"\n",
|
||
" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
|
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"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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||
" <tr style=\"text-align: right;\">\n",
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||
" <th></th>\n",
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||
" <th>unique_id</th>\n",
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||
" <th>IDs</th>\n",
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||
" <th>molecule_pref_name</th>\n",
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" <th>max_pChEMBL</th>\n",
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||
" <th>max_pChEMBL_target</th>\n",
|
||
" <th># Target Organisms</th>\n",
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||
" <th>Target Organisms</th>\n",
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||
" <th># Known Targets</th>\n",
|
||
" <th>Known Targets</th>\n",
|
||
" <th>target_pref_name</th>\n",
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||
" <th>smiles</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
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||
" <th>0</th>\n",
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||
" <td>mol_00000</td>\n",
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||
" <td>CHEMBL94657</td>\n",
|
||
" <td>PATUPILONE</td>\n",
|
||
" <td>10.67</td>\n",
|
||
" <td>CHEMBL1075590</td>\n",
|
||
" <td>695</td>\n",
|
||
" <td>Sus scrofa, Mus musculus, None, Plasmodium fal...</td>\n",
|
||
" <td>695</td>\n",
|
||
" <td>CHEMBL612519, CHEMBL614129, CHEMBL1075484, CHE...</td>\n",
|
||
" <td>AGS, NCI-H1703, MKN-7, HT-1080, NCI-H226, Lu-6...</td>\n",
|
||
" <td>C/C(=C\\c1csc(C)n1)[C@@H]1C[C@@H]2O[C@]2(C)CCC[...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>mol_00001</td>\n",
|
||
" <td>CHEMBL94657</td>\n",
|
||
" <td>PATUPILONE</td>\n",
|
||
" <td>10.67</td>\n",
|
||
" <td>CHEMBL1075590</td>\n",
|
||
" <td>695</td>\n",
|
||
" <td>Sus scrofa, Mus musculus, None, Plasmodium fal...</td>\n",
|
||
" <td>695</td>\n",
|
||
" <td>CHEMBL612519, CHEMBL614129, CHEMBL1075484, CHE...</td>\n",
|
||
" <td>AGS, NCI-H1703, MKN-7, HT-1080, NCI-H226, Lu-6...</td>\n",
|
||
" <td>C/C(=C\\c1csc(C)n1)[C@@H]1C[C@@H]2O[C@]2(C)CCC[...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
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" <th>2</th>\n",
|
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" <td>mol_00002</td>\n",
|
||
" <td>CHEMBL1554</td>\n",
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" <td>DACTINOMYCIN</td>\n",
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||
" <td>10.10</td>\n",
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||
" <td>CHEMBL614533</td>\n",
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" <td>177</td>\n",
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" <td>Giardia intestinalis, Trypanosoma cruzi, Equus...</td>\n",
|
||
" <td>177</td>\n",
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" <td>CHEMBL388, CHEMBL614151, CHEMBL3577, CHEMBL551...</td>\n",
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" <td>HT-29, CCRF-CEM, WIL2-NS, Unchecked, Caspase-7...</td>\n",
|
||
" <td>Cc1c2oc3c(C)ccc(C(=O)N[C@@H]4C(=O)N[C@H](C(C)C...</td>\n",
|
||
" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>mol_00003</td>\n",
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" <td>CHEMBL1173445</td>\n",
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" <td>LARGAZOLE</td>\n",
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||
" <td>8.80</td>\n",
|
||
" <td>CHEMBL612545</td>\n",
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" <td>45</td>\n",
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||
" <td>Homo sapiens, None</td>\n",
|
||
" <td>45</td>\n",
|
||
" <td>CHEMBL392, CHEMBL3192, CHEMBL3524, CHEMBL5103,...</td>\n",
|
||
" <td>Histone deacetylase 9, Ubiquitin-like modifier...</td>\n",
|
||
" <td>CCCCCCCC(=O)SCC/C=C/[C@@H]1CC(=O)NCc2nc(cs2)C2...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>mol_00004</td>\n",
|
||
" <td>CHEMBL3902498</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>9.37</td>\n",
|
||
" <td>CHEMBL2095194,CHEMBL3991</td>\n",
|
||
" <td>17</td>\n",
|
||
" <td>Homo sapiens, None</td>\n",
|
||
" <td>17</td>\n",
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||
" <td>CHEMBL2820, CHEMBL3991, CHEMBL1801, CHEMBL204,...</td>\n",
|
||
" <td>Coagulation factor X, Kallikrein 1, Coagulatio...</td>\n",
|
||
" <td>Cc1cc2ccc1[C@@H](C)COC(=O)Nc1ccc(S(=O)(=O)C3CC...</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
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||
],
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"text/plain": [
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" unique_id IDs molecule_pref_name max_pChEMBL \\\n",
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"0 mol_00000 CHEMBL94657 PATUPILONE 10.67 \n",
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"1 mol_00001 CHEMBL94657 PATUPILONE 10.67 \n",
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||
"2 mol_00002 CHEMBL1554 DACTINOMYCIN 10.10 \n",
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||
"3 mol_00003 CHEMBL1173445 LARGAZOLE 8.80 \n",
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"4 mol_00004 CHEMBL3902498 NaN 9.37 \n",
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"\n",
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" max_pChEMBL_target # Target Organisms \\\n",
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"0 CHEMBL1075590 695 \n",
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"1 CHEMBL1075590 695 \n",
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||
"2 CHEMBL614533 177 \n",
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"3 CHEMBL612545 45 \n",
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"4 CHEMBL2095194,CHEMBL3991 17 \n",
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"\n",
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||
" Target Organisms # Known Targets \\\n",
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||
"0 Sus scrofa, Mus musculus, None, Plasmodium fal... 695 \n",
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||
"1 Sus scrofa, Mus musculus, None, Plasmodium fal... 695 \n",
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||
"2 Giardia intestinalis, Trypanosoma cruzi, Equus... 177 \n",
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||
"3 Homo sapiens, None 45 \n",
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||
"4 Homo sapiens, None 17 \n",
|
||
"\n",
|
||
" Known Targets \\\n",
|
||
"0 CHEMBL612519, CHEMBL614129, CHEMBL1075484, CHE... \n",
|
||
"1 CHEMBL612519, CHEMBL614129, CHEMBL1075484, CHE... \n",
|
||
"2 CHEMBL388, CHEMBL614151, CHEMBL3577, CHEMBL551... \n",
|
||
"3 CHEMBL392, CHEMBL3192, CHEMBL3524, CHEMBL5103,... \n",
|
||
"4 CHEMBL2820, CHEMBL3991, CHEMBL1801, CHEMBL204,... \n",
|
||
"\n",
|
||
" target_pref_name \\\n",
|
||
"0 AGS, NCI-H1703, MKN-7, HT-1080, NCI-H226, Lu-6... \n",
|
||
"1 AGS, NCI-H1703, MKN-7, HT-1080, NCI-H226, Lu-6... \n",
|
||
"2 HT-29, CCRF-CEM, WIL2-NS, Unchecked, Caspase-7... \n",
|
||
"3 Histone deacetylase 9, Ubiquitin-like modifier... \n",
|
||
"4 Coagulation factor X, Kallikrein 1, Coagulatio... \n",
|
||
"\n",
|
||
" smiles \n",
|
||
"0 C/C(=C\\c1csc(C)n1)[C@@H]1C[C@@H]2O[C@]2(C)CCC[... \n",
|
||
"1 C/C(=C\\c1csc(C)n1)[C@@H]1C[C@@H]2O[C@]2(C)CCC[... \n",
|
||
"2 Cc1c2oc3c(C)ccc(C(=O)N[C@@H]4C(=O)N[C@H](C(C)C... \n",
|
||
"3 CCCCCCCC(=O)SCC/C=C/[C@@H]1CC(=O)NCc2nc(cs2)C2... \n",
|
||
"4 Cc1cc2ccc1[C@@H](C)COC(=O)Nc1ccc(S(=O)(=O)C3CC... "
|
||
]
|
||
},
|
||
"execution_count": 2,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
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||
}
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||
],
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"source": [
|
||
"# 读取CSV文件\n",
|
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"input_file = project_root / 'ring16' / 'temp.csv'\n",
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"df = pd.read_csv(input_file)\n",
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"\n",
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"print(f\"原始数据集: {len(df)} 个分子\")\n",
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"print(f\"列名: {df.columns.tolist()}\")\n",
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"\n",
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"# 添加唯一ID列\n",
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"df['unique_id'] = [f\"mol_{i:05d}\" for i in range(len(df))]\n",
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"df = df[['unique_id'] + [col for col in df.columns if col != 'unique_id']]\n",
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"\n",
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"print(f\"\\n已添加唯一ID列\")\n",
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"print(f\"前5行:\")\n",
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"df.head()\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
|
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"## 3. 定义过滤函数\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"过滤函数定义完成!\n"
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]
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}
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||
],
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"source": [
|
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"def has_bridged_ring(mol):\n",
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" \"\"\"\n",
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" 检测分子是否包含桥环结构\n",
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" 桥环：原子属于多个环，且至少有一个是16元环\n",
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" \"\"\"\n",
|
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" if mol is None:\n",
|
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" return False\n",
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" \n",
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" # 获取所有环信息\n",
|
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" ring_info = mol.GetRingInfo()\n",
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" \n",
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" # 检查是否有原子属于多个环（可能表示桥环）\n",
|
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" for atom in mol.GetAtoms():\n",
|
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" if atom.IsInRing():\n",
|
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" # 获取该原子所属的环数量\n",
|
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" ring_count = ring_info.NumAtomRings(atom.GetIdx())\n",
|
||
" if ring_count > 1:\n",
|
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" # 检查是否至少有一个是16元环\n",
|
||
" atom_rings = ring_info.AtomRings()\n",
|
||
" for ring in atom_rings:\n",
|
||
" if atom.GetIdx() in ring and len(ring) == 16:\n",
|
||
" return True\n",
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" \n",
|
||
" return False\n",
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"\n",
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"\n",
|
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"def has_multiple_lactones_on_ring(mol):\n",
|
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" \"\"\"\n",
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" 检测环上是否有多个内酯键（酯键）\n",
|
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" 参考batch_process_ring16.py的逻辑\n",
|
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" \"\"\"\n",
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" if mol is None:\n",
|
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" return False\n",
|
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" \n",
|
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" # 获取16元环上的所有原子\n",
|
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" ring_atoms = get_ring_atoms(mol)\n",
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" \n",
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" if len(ring_atoms) == 0:\n",
|
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" return False\n",
|
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" \n",
|
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" # 使用SMARTS模式：环上的氧连接到环上的羰基碳\n",
|
||
" # [r16;O] 环上的氧，连接到 [r16;#6] 环上的碳，该碳有双键氧 (=O)\n",
|
||
" lactone_pattern = Chem.MolFromSmarts(\"[r16;#8][r16;#6](=[#8])\")\n",
|
||
" \n",
|
||
" if lactone_pattern is None:\n",
|
||
" return False\n",
|
||
" \n",
|
||
" matches = mol.GetSubstructMatches(lactone_pattern)\n",
|
||
" \n",
|
||
" # 提取环上的内酯碳索引（match[1]是羰基碳）\n",
|
||
" lactone_carbons_on_ring = []\n",
|
||
" for match in matches:\n",
|
||
" if len(match) >= 2:\n",
|
||
" carbonyl_carbon = match[1] # 羰基碳\n",
|
||
" # 确保这个碳确实在16元环上\n",
|
||
" if carbonyl_carbon in ring_atoms:\n",
|
||
" lactone_carbons_on_ring.append(carbonyl_carbon)\n",
|
||
" \n",
|
||
" # 去重\n",
|
||
" lactone_carbons_on_ring = list(set(lactone_carbons_on_ring))\n",
|
||
" \n",
|
||
" # 只有当环上有多个内酯键时才返回True\n",
|
||
" return len(lactone_carbons_on_ring) > 1\n",
|
||
"\n",
|
||
"\n",
|
||
"def has_amide_on_ring(mol):\n",
|
||
" \"\"\"\n",
|
||
" 检测环上是否有酰胺键\n",
|
||
" 酰胺键模式：NC(=O) 或 C(=O)N，且N和C都在环上\n",
|
||
" \"\"\"\n",
|
||
" if mol is None:\n",
|
||
" return False\n",
|
||
" \n",
|
||
" # 获取16元环上的所有原子\n",
|
||
" ring_atoms = get_ring_atoms(mol)\n",
|
||
" \n",
|
||
" if len(ring_atoms) == 0:\n",
|
||
" return False\n",
|
||
" \n",
|
||
" # 模式1: 环上的N连接到环上的C(=O)\n",
|
||
" # [r16;N][r16;C](=[#8])\n",
|
||
" amide_pattern1 = Chem.MolFromSmarts(\"[r16;#7][r16;#6](=[#8])\")\n",
|
||
" \n",
|
||
" # 模式2: 环上的C(=O)连接到环上的N\n",
|
||
" # [r16;C](=[#8])[r16;N]\n",
|
||
" amide_pattern2 = Chem.MolFromSmarts(\"[r16;#6](=[#8])[r16;#7]\")\n",
|
||
" \n",
|
||
" if amide_pattern1 is None or amide_pattern2 is None:\n",
|
||
" return False\n",
|
||
" \n",
|
||
" matches1 = mol.GetSubstructMatches(amide_pattern1)\n",
|
||
" matches2 = mol.GetSubstructMatches(amide_pattern2)\n",
|
||
" \n",
|
||
" # 检查匹配是否在环上\n",
|
||
" for match in matches1:\n",
|
||
" if len(match) >= 2:\n",
|
||
" n_atom = match[0] # N原子\n",
|
||
" c_atom = match[1] # C原子\n",
|
||
" if n_atom in ring_atoms and c_atom in ring_atoms:\n",
|
||
" return True\n",
|
||
" \n",
|
||
" for match in matches2:\n",
|
||
" if len(match) >= 2:\n",
|
||
" c_atom = match[0] # C原子\n",
|
||
" n_atom = match[1] # N原子\n",
|
||
" if c_atom in ring_atoms and n_atom in ring_atoms:\n",
|
||
" return True\n",
|
||
" \n",
|
||
" return False\n",
|
||
"\n",
|
||
"\n",
|
||
"print(\"过滤函数定义完成!\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4. 执行过滤\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"开始过滤...\n",
|
||
"总分子数: 2022\n",
|
||
"\n",
|
||
"过滤完成!\n",
|
||
"\n",
|
||
"============================================================\n",
|
||
"过滤统计结果:\n",
|
||
"============================================================\n",
|
||
"总分子数: 2022\n",
|
||
"SMILES解析失败: 0 (0.00%)\n",
|
||
"含桥环: 777 (38.43%)\n",
|
||
"环上多酯键: 288 (14.24%)\n",
|
||
"环上酰胺键: 79 (3.91%)\n",
|
||
"通过过滤: 878 (43.42%)\n",
|
||
"============================================================\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 初始化过滤结果列\n",
|
||
"df['has_bridge'] = False\n",
|
||
"df['has_multiple_lactones'] = False\n",
|
||
"df['has_amide'] = False\n",
|
||
"df['parse_failed'] = False\n",
|
||
"df['filter_reason'] = ''\n",
|
||
"\n",
|
||
"# 统计信息\n",
|
||
"stats = {\n",
|
||
" 'total': len(df),\n",
|
||
" 'parse_failed': 0,\n",
|
||
" 'bridged': 0,\n",
|
||
" 'multiple_lactones': 0,\n",
|
||
" 'amide': 0,\n",
|
||
" 'passed': 0\n",
|
||
"}\n",
|
||
"\n",
|
||
"print(\"开始过滤...\")\n",
|
||
"print(f\"总分子数: {stats['total']}\")\n",
|
||
"print()\n",
|
||
"\n",
|
||
"# 遍历每个分子进行过滤\n",
|
||
"for idx, row in df.iterrows():\n",
|
||
" smiles = row['smiles']\n",
|
||
" unique_id = row['unique_id']\n",
|
||
" \n",
|
||
" # 解析SMILES\n",
|
||
" mol = Chem.MolFromSmiles(smiles)\n",
|
||
" if mol is None:\n",
|
||
" df.at[idx, 'parse_failed'] = True\n",
|
||
" df.at[idx, 'filter_reason'] = 'parse_failed'\n",
|
||
" stats['parse_failed'] += 1\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 检查桥环\n",
|
||
" if has_bridged_ring(mol):\n",
|
||
" df.at[idx, 'has_bridge'] = True\n",
|
||
" df.at[idx, 'filter_reason'] = 'bridged_ring'\n",
|
||
" stats['bridged'] += 1\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 检查多个内酯键\n",
|
||
" if has_multiple_lactones_on_ring(mol):\n",
|
||
" df.at[idx, 'has_multiple_lactones'] = True\n",
|
||
" df.at[idx, 'filter_reason'] = 'multiple_lactones'\n",
|
||
" stats['multiple_lactones'] += 1\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 检查酰胺键\n",
|
||
" if has_amide_on_ring(mol):\n",
|
||
" df.at[idx, 'has_amide'] = True\n",
|
||
" df.at[idx, 'filter_reason'] = 'amide_on_ring'\n",
|
||
" stats['amide'] += 1\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 通过所有过滤\n",
|
||
" stats['passed'] += 1\n",
|
||
"\n",
|
||
"print(\"过滤完成!\")\n",
|
||
"print()\n",
|
||
"print(\"=\"*60)\n",
|
||
"print(\"过滤统计结果:\")\n",
|
||
"print(\"=\"*60)\n",
|
||
"print(f\"总分子数: {stats['total']}\")\n",
|
||
"print(f\"SMILES解析失败: {stats['parse_failed']} ({stats['parse_failed']/stats['total']*100:.2f}%)\")\n",
|
||
"print(f\"含桥环: {stats['bridged']} ({stats['bridged']/stats['total']*100:.2f}%)\")\n",
|
||
"print(f\"环上多酯键: {stats['multiple_lactones']} ({stats['multiple_lactones']/stats['total']*100:.2f}%)\")\n",
|
||
"print(f\"环上酰胺键: {stats['amide']} ({stats['amide']/stats['total']*100:.2f}%)\")\n",
|
||
"print(f\"通过过滤: {stats['passed']} ({stats['passed']/stats['total']*100:.2f}%)\")\n",
|
||
"print(\"=\"*60)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4.5. 基于SMARTS模式的进一步过滤\n",
|
||
"\n",
|
||
"对过滤后的878个分子，使用SMARTS模式进行匹配，只保留包含特定结构的分子。\n",
|
||
"\n",
|
||
"16大环内酯母核结构目标SMARTS模式：`O=C1CCCCCCCC(=O)OCC/C=C/C=C/1`\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"目标SMILES: O=C1CCCCCCCC(=O)OCC/C=C/C=C/1\n",
|
||
"转换后的SMARTS: [#8]=[#6]1-[#6]-[#6]-[#6]-[#6]-[#6]-[#6]-[#6]-[#6](=[#8])-[#8]-[#6]-[#6]/[#6]=[#6]/[#6]=[#6]/1\n",
|
||
"\n",
|
||
"使用SMARTS模式进行匹配...\n",
|
||
"\n",
|
||
"当前df_filtered中有 878 个分子\n",
|
||
"\n",
|
||
"开始SMARTS匹配过滤...\n",
|
||
"对 878 个已过滤的分子进行匹配\n",
|
||
"\n",
|
||
"============================================================\n",
|
||
"SMARTS匹配过滤结果:\n",
|
||
"============================================================\n",
|
||
"过滤前分子数: 878\n",
|
||
"匹配的分子数: 307 (34.97%)\n",
|
||
"不匹配的分子数: 571 (65.03%)\n",
|
||
"过滤后保留: 307 个分子\n",
|
||
"============================================================\n",
|
||
"\n",
|
||
"前5个匹配的分子:\n",
|
||
"\n",
|
||
"1. mol_00113: CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O)C(N(C)C)C2O)C(CCN2CC(C)CC(C)C2)CC(C)C(=O)/C=C/C(C)=C\\[C@@H]1COC1OC(C)C(O)C(OC)C1OC\n",
|
||
"\n",
|
||
"2. mol_00288: CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]2O[C@H](C)[C@@H](O[C@H]3C[C@@](C)(O)[C@@H](O)[C@H](C)O3)[C@H](N(C)C)[C@H]2O)[C@@H](CC=O)C[C@@H](C)C(=O)/C=C/C(C)=C/[C@@H]1CO[C@@H]1O[C@H](C)[C@@H](O)[C@@H](OC)[C@H]1OC\n",
|
||
"\n",
|
||
"3. mol_00313: CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]2O[C@H](C)[C@@H](O[C@H]3C[C@@](C)(O)[C@@H](O)[C@H](C)O3)[C@H](N(C)C)[C@H]2O)[C@@H](CC=O)C[C@@H](C)C(=O)/C=C/C(C)=C/[C@@H]1CO[C@@H]1O[C@H](C)[C@@H](O)[C@@H](OC)[C@H]1OC\n",
|
||
"\n",
|
||
"4. mol_00314: CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]2O[C@H](C)[C@@H](O[C@H]3C[C@@](C)(O)[C@@H](O)[C@H](C)O3)[C@H](N(C)C)[C@H]2O)[C@@H](CC=O)C[C@@H](C)C(=O)/C=C/C(C)=C/[C@@H]1CO[C@@H]1O[C@H](C)[C@@H](O)[C@@H](OC)[C@H]1OC\n",
|
||
"\n",
|
||
"5. mol_00326: CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]2O[C@H](C)[C@@H](O[C@H]3C[C@@](C)(O)[C@@H](O)[C@H](C)O3)[C@H](N(C)C)[C@H]2O)[C@@H](CC=O)C[C@@H](C)C(=O)/C=C/C(C)=C/[C@@H]1CO[C@@H]1O[C@H](C)[C@@H](O)[C@@H](OC)[C@H]1OC\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 定义目标SMARTS模式\n",
|
||
"# 首先将SMILES转换为SMARTS\n",
|
||
"target_smiles = \"O=C1CCCCCCCC(=O)OCC/C=C/C=C/1\"\n",
|
||
"print(f\"目标SMILES: {target_smiles}\")\n",
|
||
"\n",
|
||
"# 将SMILES转换为SMARTS模式\n",
|
||
"target_mol = Chem.MolFromSmiles(target_smiles)\n",
|
||
"if target_mol is None:\n",
|
||
" print(\"错误: 无法解析目标SMILES\")\n",
|
||
"else:\n",
|
||
" # 将SMILES转换为SMARTS\n",
|
||
" target_smarts = Chem.MolToSmarts(target_mol)\n",
|
||
" print(f\"转换后的SMARTS: {target_smarts}\")\n",
|
||
" \n",
|
||
" # 也可以直接使用SMILES进行匹配（RDKit支持）\n",
|
||
" # 但使用SMARTS更灵活\n",
|
||
" print(f\"\\n使用SMARTS模式进行匹配...\")\n",
|
||
" \n",
|
||
" # 创建SMARTS查询对象\n",
|
||
" pattern = Chem.MolFromSmarts(target_smarts)\n",
|
||
" if pattern is None:\n",
|
||
" # 如果SMARTS转换失败，尝试直接用SMILES\n",
|
||
" print(\"警告: SMARTS转换失败，尝试使用SMILES模式\")\n",
|
||
" pattern = Chem.MolFromSmiles(target_smiles)\n",
|
||
" \n",
|
||
" if pattern is not None:\n",
|
||
" # 确保df_filtered存在\n",
|
||
" if 'df_filtered' not in locals():\n",
|
||
" df_filtered = df[df['filter_reason'] == ''].copy()\n",
|
||
" print(f\"\\n创建df_filtered: {len(df_filtered)} 个分子\")\n",
|
||
" else:\n",
|
||
" print(f\"\\n当前df_filtered中有 {len(df_filtered)} 个分子\")\n",
|
||
" \n",
|
||
" # 添加新的过滤列\n",
|
||
" df_filtered['matches_smarts'] = False\n",
|
||
" \n",
|
||
" # 统计信息\n",
|
||
" smarts_stats = {\n",
|
||
" 'total': len(df_filtered),\n",
|
||
" 'matched': 0,\n",
|
||
" 'not_matched': 0\n",
|
||
" }\n",
|
||
" \n",
|
||
" print(f\"\\n开始SMARTS匹配过滤...\")\n",
|
||
" print(f\"对 {smarts_stats['total']} 个已过滤的分子进行匹配\\n\")\n",
|
||
" \n",
|
||
" # 遍历每个已过滤的分子\n",
|
||
" for idx, row in df_filtered.iterrows():\n",
|
||
" smiles = row['smiles']\n",
|
||
" mol = Chem.MolFromSmiles(smiles)\n",
|
||
" \n",
|
||
" if mol is not None:\n",
|
||
" # 检查是否匹配SMARTS模式\n",
|
||
" if mol.HasSubstructMatch(pattern):\n",
|
||
" df_filtered.at[idx, 'matches_smarts'] = True\n",
|
||
" smarts_stats['matched'] += 1\n",
|
||
" else:\n",
|
||
" smarts_stats['not_matched'] += 1\n",
|
||
" else:\n",
|
||
" smarts_stats['not_matched'] += 1\n",
|
||
" \n",
|
||
" # 只保留匹配的分子\n",
|
||
" df_filtered_smarts = df_filtered[df_filtered['matches_smarts'] == True].copy()\n",
|
||
" \n",
|
||
" print(\"=\"*60)\n",
|
||
" print(\"SMARTS匹配过滤结果:\")\n",
|
||
" print(\"=\"*60)\n",
|
||
" print(f\"过滤前分子数: {smarts_stats['total']}\")\n",
|
||
" print(f\"匹配的分子数: {smarts_stats['matched']} ({smarts_stats['matched']/smarts_stats['total']*100:.2f}%)\")\n",
|
||
" print(f\"不匹配的分子数: {smarts_stats['not_matched']} ({smarts_stats['not_matched']/smarts_stats['total']*100:.2f}%)\")\n",
|
||
" print(f\"过滤后保留: {len(df_filtered_smarts)} 个分子\")\n",
|
||
" print(\"=\"*60)\n",
|
||
" \n",
|
||
" # 显示前几个匹配的分子\n",
|
||
" if len(df_filtered_smarts) > 0:\n",
|
||
" print(f\"\\n前5个匹配的分子:\")\n",
|
||
" for i, (idx, row) in enumerate(df_filtered_smarts.head().iterrows()):\n",
|
||
" print(f\"\\n{i+1}. {row['unique_id']}: {row['smiles']}\")\n",
|
||
" else:\n",
|
||
" print(\"\\n警告: 没有匹配的分子！\")\n",
|
||
" else:\n",
|
||
" print(\"错误: 无法创建SMARTS或SMILES模式\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"df_filtered_smarts.to_csv('../output/ring16_match_smarts.csv')"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4.6. 展示母核结构\n",
|
||
"\n",
|
||
"显示目标SMARTS模式对应的母核分子结构\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"母核SMILES: O=C1CCCCCCCC(=O)OCC/C=C/C=C/1\n",
|
||
"母核SMARTS: [#8]=[#6]1-[#6]-[#6]-[#6]-[#6]-[#6]-[#6]-[#6]-[#6](=[#8])-[#8]-[#6]-[#6]/[#6]=[#6]/[#6]=[#6]/1\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/jpeg": 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Fhbm7u2dnZxcWFnI9jYogNGxTdNy4cTfddJPewwGcfr5McHCw1WrNzMzUfHTQCEFoNK+++mpWVlZgYODEiRP1Hgvg6Fq3bt2qVauzZ8/++OOP1Z3DY/WGRxAayvbt22fOnOni4rJw4UJmigI1cc3pMDxWb3gEoXGUlpbGxsZWVFQ8//zzUVFReg8HMNTEUZ6gMDCC0DhefvnlrKys7t27T5gwQe+xAE7jmgVfeHi4m5vbnj17iouLtR0aNEIQGkRSUtJbb73l6uq6aNEiT09PvYcDOI1rFnyenp7du3evqKhgvoxREYSGcOHCK88/b2+K9u7dW+/RAM6kXbt2LVq0OH369MGDB6s7h9uExkYQGsLLL3+WkfHq4ME0RYFa6NmzJ7cJZUYQOr9t28SsWT4XLkx6/nkPDw+9RwMYdn0ZFlozKoLQyRUXi+HDRUWFePFFQVMUUOc2YXh4uKura1ZWVklJCdfYeAhCJ/ePf4jcXBEUJF56Se+hAM7KXvAlJydXd4KXl1e3bt3Ky8t37dql7dCgBYLQmW3dKt57T7i6ikWLBE1RoLZuuOGG5s2bnzp16vDhw9Wdw+rbBkYQOn9T9KWXRK9eeo8GkGK+DLcJDYkgdFrjx4t9+0R4uNIdBVA37MckM4LQOW3ZIt5/X2mKzp8v3N31Hg1g/PkyPXr0cHFx2b17d2lpqbZDg+oIQidUVKQ0Ra1W8cor4uJNfgBqz5fx9vbu2rVrWVlZVlYWV9tgCEIn9MIL4ocfRI8eyiMTAOpDQEBA06ZN8/Lyjh49Wt05rL5tVAShs9m4UcyerbRDFy0Sbm56jwYwCJPJZJ8vw2P1EiIInUpRkXj8cWGzKU3RsDC9RwMYCvsxSYsgdCrjxon9+0XPniI+Xu+hANJNHI2IiDCbzZmZmeXl5doODeoiCJ3Hhg0iMVF5cJ6mKKBHRdioUaPOnTuXlpYyX8ZgCEJna4pOmCBCQ/UeDWBAnTt39vX1PXLkyPHjx6s7h9W3DYkgdBJjx4oDB5Sm6Lhxeg8FMCaTydSjR49rdkevXjXCGRGEzmD9evHhh0pT9OOPmSkKqIf1ZeREEDq8/HwRE6M0RSdNEiEheo8GkH3iqMlkysjIYL6MkRCEDm/sWHHokIiKoikK6F4RNm7cuGPHjiUlJdnZ2bwdhkEQOrZ168RHHylN0fnzhYuL3qMBDK5Lly6NGjU6dOjQyZMnqzuH/ZiMhyB0YOfPi9hYpSk6ZYoIDtZ7NIDxmc3mGs6XYT8mIyEIHdjf/640Rfv0UbqjADRxzYKPitB4CEJHtXatWLiQpiigsWsWfJGRkSaTKT093WKxaDs0qIUgdEilpZVN0ddfF0FBeo8GkMg1C74mTZp06NChuLg4JydH26FBLQShQ/LwUMrBhx8Wzz6r91AAuQQGBnp7e//000+nT5+u7hxuExoMQeiobrtNLF3KTFFAY2azOezi1i5paWnVncNtQoMhCHVSUSFOnBB794rsbHHqlNIFBeAYrplzVIQGQxBqbsUK8dBDonlz0aKFcv+ve3fh56ccP/KIsr8EAGeYL2MvGa1Wq7ZDgypMNmoRzRw5Iv76V7Fly9XOuece8a9/iSZNtBsVgN/atWtXWFhYp06dfvjhh+quzQ033HDo0KG9e/cGBgZy/ZwdFaFWDhwQ/fpVpqCPj3jqKfHll2L7drFtm1i+XJkj6uGhfOjrr8VNNyldUwA6CQoK8vLyOnDgwJkzZ6o7h9uERkIQasJiUTqfhw4px7feKvbtE+++K+6/X9x4o/K8/F/+IubNE1lZlU9K7NlT+ewEAD24uLiEhYXZbLb09PTqzuE2oZEQhJqYP18p/oRQ9tRdvVr4+1/hnE6dlHuELVoox6tWiZUrtRkagFrfJmRjQmMgCNVnsyn1n90774gGDao9s0ULMW3ar2cCcOyF1pgvYwwEofr27VPankKIrl3FzTdf4+T/+z/h66scbNwozp3TYHQAalER+vv7t2nTJj8/f//+/VxAZ0cQqm/r1sqDW28VJtM1Tvb0FP37KwdWq9ixQ/3BAbiC4OBgT0/Pffv2nT9/vroLRHfUMAhC9eXmVh7UcCul0NDKA1YyBHTi5uYWGhrKfBlJEITqO3u28qBp0xqd36zZ5Z8IQHPsxyQPglB9xcWVB15eNTq/YcPKg8JC1cYEoK63CXv16mU/gWVJnB1BqL5GjSoPiopqdH5BQeVB48aqjQlAXSvCli1btmrV6ty5cwcOHOBqOjWCUH32WaBCiOp3dfmNqtOqPhGA5kJCQjw8PHJzcwuqfjf9HR6rNwaCUH3du1ce7NpVo/MzMioPQkJUGxOAa3B3dw8ODrZarRlV/0n+DhNHjYEgVF+fPpUH33577YXTLlwQSUnKgauruHgHAoBe2I9JEgSh+jp1EuHhysH+/UoWXt2yZcL+3NJdd/06awaAYy+0xnwZp0YQamL06MqDZ58VpaXVnnb2rPjHPyqPn3pKk5EBqH1F2LZt25YtW545c+bgwYNcR+dFEGoiOlrZaEIIkZkpHnjgymunHTsm7rxTHD2qHD/4oPjjH7UZGoDqhIWFubm5ZWdnF1U/5btnz55CiO32VfXhnAhCTbi6iiVLROvWyvGaNaJbNzFxoti8Wfz8s/jpJ6VfGh8vAgNFcrJyQlCQmDNHm3EBuAoPD4/g4OCKiorMzMzqznF1db24SD6r5DsxglArnTsri45evOWg7Ls7aZIYMEC0by8CApQ1SN98U+TnKx+6806xaZPw89NsXACueZvwKt3RoIvbiOZWraQIJ0QQauiGG8TOnWLxYnHbbcLd/Tcf8vIS99yjbEP4v//VdCU2AFrdJrzKfJlBgwYJIa6ylz0cn4nJTvooLFQ2rD91StmPws9PdOig7DsBwMEkJSX17ds3LCzsKk8Turm5WSyWOXPmjBw5UtvRoX4QhABQrZKSEh8fH5vNlp+f36CaXbVvueWW7777zsPD48SJEz4+PlxNp0NrVA8rVyozZUpKdHlxADXn6ekZEhLSvXv348ePV3fOV1995eHhUVpaetddd3FtnREVoR7atROHDyvbDXbtqsvrA6i5iooKFxeXq58zf/78ESNGmM3mbdu23Wh/VgrOgyDUXF6eaNlS+Pgoj8+bqcgBgxg5cuTcuXMDAwPT0tI8ueXvVPhBrDn7POyICFIQMJJ33nknODg4Ozt70qRJeo8F14cg1CkIL87JBmAYHh4e8+fPd3FxmT59+o4dO/QeDq4DQag5ghAwqKioqLFjx1ZUVAwbNqyE2XDOgyDUHEEIGNeUKVNokDodJstoi5kygNFt3769f//+QoitW7cyg9QpUBFqi5kygNHRIHU6BKG26IsCEqBB6lwIQm0RhIAEmEHqXAhCbRGEgBxokDoRJstoiJkygExKS0sjIyOzsrLGjx8/depUvYeDalERaoiZMoBMaJA6C4JQQ/RFAcnQIHUKBKGGCEJAPswgdXzcI9QQuy8BUuIRewdHRaiVvDxlD0IfH9G5s2avCcAR0CB1cAShVpgpA0iMBqkjIwi1wg1CQGLMIHVkBKFWCEJAbjRIHRaTZbTCTBlAejxifwUWi0hKEtu3i1OnxPnzolkz0aKFuPlmERoqtEIQaoI1ZQBcxAzSXxUViVmzxMyZ4swZ8XsdO4qJE8Wjjwqz6p1LWqOaYKYMgIuioqKee+45drEXR46IPn3ESy9VpmCDBiIoSPTuLbp0ES4uyr8cOCCGDhUPPiguXFD7e4cg1AQ3CAH8YvLkybLvYn/+vNL83L1bOe7SRfz730pfNCtL7NghcnPF8eNixgzRuLHy0RUrxEMPCZtN1eEQhJogCAH8ghmkYswYsX+/ci0GDBDJyWLIEOHl9es3SPPm4rnnxNatomVL5a///a/44ANVv324R6gJZsoA+K34+Pg333wzMDAwLS3N09NTosvzww+iWzdhtSo1X1aWaNOm2jPXrxd33KGUg61aiZ9+Eu7uKo2IilB9rCkD4HfkbZB++KGSgkKI0aOvloJCiD/8QQwcqBwcOya++kq9ERGE6mOmDIDqG6Rvvvnmli1bJLpCGzZUHvzf/1375EceqTzYuFG9ERGE6uMGIYDqZ5BardYRI0aUlJRIcZFKSkRGhnLQsKEyTfSaoqIqD3bsUG9QBKHq/p6bO6l37339+qn/UgCctUE6ceJEYUgVFb/566lTorxcObjhhsrHJK6uapeCo0eFaghC1f3n228n7txpCwlR/6UAOPEMUqM1SI8cUZ6Ib9tWmedS5ezZygP70xHX1LChcHX9zSeqgCBUV15e3uHDh318fDqz+xIAGRqkVqtYvVrcd59o315MmqQ8FLhiRZ2+oMmk/Knmo4QEobpSLt4gjIiIMKu/ShAAJ2WQBum5c8qSaZ07Kym4erVwcxODB4u1a8Uzz/x6jq9v5UFBQY2+ZnFxZSu1aVOhGn46axGEkZGRKr8OACfm9A3SlBQxcqRo3Vo8+6z48UfloYgJE8TPP4tly8Ttt//mzGbNKm8N/vxzjYq8gwcrD/z9hWoIQnURhAAM2yAtKREffyx69hS9eom5c0VpqRJ7y5Yp6TVxovDzu8KneHlVThY9f15ZTbSGs+6FUF5CNQShughCAAZskObmivHjlcpv2DCRnq6Ua/HxypIxa9cq7dCrTwe96abKg88/v/YLffZZ5cGAAUI1LLGmory8vJYtW/r4+Jw9e5Z7hABquEmTzWb7/vvv+/fv73BXrKJCfP21eOcdZfEze2MzMlLExYnHHlO2j6ih5GRllwkhRECAsu72pauMXmbPHtGjh3KPsEkTpZXasKFQBxWhipgpA8AgDdJjx0RCghJdgwaJdeuEt7eSf+npSqrFxV1HCtqbnLfdphz8+KP4+9+rvVNYUCCGD6+cKfPUU+qlIEGoLvqiAJy+Qbp5s7I7RPv2Si/055+V9bKnTVPuAiYmivDwWn7NefNEo0bKwdy54q9/vcLD8qmp4tZbxc6dynFYmLJtoZpojarogQceWLFixdKlSx9++GE1XweAoThEg/T8eWWbwHfeUTaIEELZ+eH++5Xi7w9/qHywr46++0488IDyxIUQyoMWAweKkBDh4yNOnxZJSSItrbJSDA5WtmFq106oiSBUUbt27Q4fPpyTk9O1a1c1XweA0ei5SVNKilKoLVkiioqUv7ZurdwCfOopZY2YOn7Z2bNFTIyoWm8yO1t53OJ//7vy+W5uYsQI8cYbvz56qBqCUC0nTpxo0aIFM2UA1EJpaWlkZGRWVlZ8fPy0adO0uIalpcpWR7NmCfuDjCaTUvzFxYk//7lykbO6fNm5c5U7i0KIhx8WS5f+5oSUFOXp+23blDVo8vOVhy5atlRuIg4aJDp0EJogCNXy9ddf33vvvbfccstGNXcPAWBUmjVI9+3bN2fOnJHr13e17wvRtKmIjhZPPPHrgte1s3+/svXg/PnKQtv2xUX/+ldlb/rgYOFgmDWqFmbKAHDkGaRWq3XdunV/+tOfunXr9s9//nOOr6/yLERiojIjZsaM2qeg1aoUf0OGKNNqEhKUFLR/2SNHlD8dLwWFEHUoeHFVBCGAOpo8efLq1auzsrImTpxYjw3SY8eOzZ8/PzEx8fDhwxcXe/F6+OGHHxs1SkmsusjLEwsXijlzKreb8PAQDz6oNFcvW2XN8dAaVQszZQA4WoM0JSVl1qxZn376afnF5/O6dOkSGxs7YsSIZs2a1fHrKncBFy8WFy4of+3cWZnnEhsrmjcXzoAgVMXJkyf9/f2ZKQOgHmeQpqamNriuR9d/kZ+f/+mnn7733nu7du1SbomZzbfddtuYMWPuu+8+U12ehSgoEJ98Ij74oHLTebNZmeQSF6cUgjXZdNdh0BpVRXJyshCiZ8+erKwGoL4apJMmTbreBml2dvbs2bM/+uijwsJCIUTLli2HDRs2atSoG264oU5jyskRCxYoVaB9v9wWLZRVYJ54QrN5nk4bhIcOKYvUbdqkLCJQUKCsL+fnJ6KixN13i9BQYcQg7KXmcukApNqkqX///tOnT//Tn/5UkwZpWVnZihUr5s6du87+0MLFzeDGjBnz8MMPu7m51X4oZWXKLrtz5/5mrdExY5SHIuryZXVn08DJk7aRI22urjYhrvy/e++1ZWfbDOT+++8XQixdulTvgQAwiPj4eCFEYGBgcXHxVU47fPjwhAkT/H7ZAsnHxycuLi4zM7OuL3/kiG3aNFvbtpU/tBs1ssXF2TIybIag/j3CAwfEXXeJffsq/9q9u7LuePPmyso6WVnKUnJWq/Lvvr7iiy/ELbcIQ2CmDAAtH7G3Wq0bNmyYO3fuF198YbFYLv6s7T5y5MjY2Fhvb+/av6rVKjZsUErAL74QF7+sCAxUWqCxscq624ahbs4WFtq6dav8DaJXL9uOHZefsH+/Ug7aT/D2tu3bZ3N+J06csP8iVlFRofdYABhHSkqKm5ub2WzevHlz1T+ePXv27bffDggIsP9I9/DwGDx48Nq1a+v6YmfP2hITbd27V/58dne3DR5sq/uXlbEifOYZZc1W+06Ma9ZceR+Nigpla8clSypP27Sp8t/XrBEvv1zTF2rYUFkT9nqM8vH5IT9fqOD06dNpaWk333zzt99+q8bXByCt8ePHJyQk2GeQ7tmzZ+7cuYsXL75w8aGFNm3ajBgxYvTo0VV90drZvn275/Ll4e+/r2xAL4Qy/2XkSGWNUH9/YVQqhuyZMzYvL+VXiQYNbAcOXO3MggJb+/aVv3ds2VL5j0uWVHtPsT7+16d1a1Uv7NixY1W8tgCkVFJSEnxxcRb/X2LJbDbfd999X3/9dR1bUBcuXFi2bFm/iytiPxAVZTObbbffblu2zFZebjM6NWeN/vvforhYOXjoIWU7x6vw9laWNn/hBeX4o48q1ya/6y5ly8caKipSpjNdj5lmc7H99mR9mzhx4pYtWyLruEYDAPyOh4fHxx9/3KdPnxMnTjRp0iQuLm7kyJFVfdHaycnJmT179qJFi85d3BSpefPm3W+9VVkau2NHWd4BFUP20Ucry6/PPrv2yfv3V54cGGhzcm0vblaSk5Oj90AAGHkGaYcOHc6cOVPrL1JRUbF27dpLn6mPjIxMTEwsKiqySUbNe4RBQWLvXuXgwIFrVIQXA1k0a6Y8m2kyKU8ZXvFuovOsKdOoUaNz587xND0ANZSWlgYEBBw7diwqKiopKel6P/348eOLFi364IMPDh06JITw9PQcPHjw3//+9549e8r5fqm5+8TJk5UHbdpc+2STSdn+0Z6Ip08Lp/XRRx9d3MmyNSkIQCUeHh4zZsywT2yZPXt2zT9x8+bNQ4YMad++/fjx4w8dOtS1a9dp06YdOXLk448/ljYFVQ5C+5xMD4+azuds3LjywL5mj3OyL+XnWpd9LAHgWh555JE+ffoIIZ5//nn7rNGryM/Pnzt3bmho6IABA5YvX261Wu+77761a9dmZ2fHx8c3bdpU8uut5s9rNzdlAovFohR5NVnX9eJq6AoPD+G0KioqhBBqbB4GAJdauXKln59fUVHR2bNnq1uMOzU1NTExccmSJUVFRUKIVq1aDR069Mknn2zfvj0XU5Mg9PVVJnNWVCh/1mQNgvPnf/1Ep3XnnXd++umnR44c0XsgAAxu27Zt9v5T6989DFZaWvrVV19dutZo//79n3nmmQceeKBOa40alJqt0YuTJxVV66tdRVlZ5V6ODRo4yxZWV/TnP//ZXhGeOnVK77EAMLLVq1df3PihxaX/uH///vHjx7dt23bIkCHr1q1r3LhxXFzc7t27N2/ePHjwYFJQ8yCMiqo82Lnz2ienp1c+CBgZKZz5Bpuvr2/Lli2FELt379Z7LACM7ODBg0KIkJAQ+1qj69atGzJkSLdu3RISEk6dOmV/FuLIkSOJiYn2Z/BRHTUj57bbKtdX++QTZavGq1u6tPLg9tsrDzZtEu++q97onmvY8OeLTfN65+npKYTIysq6xShriANwQAUFBUKI2NjYhISEOXPm/HSxqebh4fHggw/GxcXdXvWzFNei5nOEFovy+ODhw8rxunXiD3+o9sxDh0RwsCgsVGrBn36qfNziX/8Sjz2m1tiE6NWyZcrx4+p9/ZiYmPnz56v39QHIzGq1NmrUqLi42N3dvexiO61z585PPPFEdHQ0s0AdqSJ0dRWvvKKs1iqEiI4WGzeKTp2ucNq5c2LIECUFhVB296h66HDAALFsmXqjm+LiUnhxhme9y83Nffnll1NSUtT44gBg33q++OIalhaL5fbbb4+Li3vwwQddXFy4OLWg8u4TNpsYNEisWqUcN28uJk8Wf/ubaNSo8qNlZWL1ahEfXzmbJjBQpKQoO9c7ueLi4sYXn4ksKCiwt0kBoH4tXrx46NChoaGhq1evbteuHZfXUSfL2NeLWbZMXNyuXZw6JZ58Uvj5iR49xB13KFNpmjcXDz5YmYIREUrJ6PwpKITw8vIKDAy0WCz2h+sBoN7Ze04PP/wwKejwQWh/HOLzz8WCBcL+/GZpqcjIUG4Z7tihrClqf2pw6lSxebO4ONnSGCIiIqq+UwGg3tl/vLDLTb3Q5EEFs1kMHy6GDlU6n999J44cEWfOKAuq+fsrOy7ddNP17qnr+CIjIz/++OPU1FS9BwLAgKxWa3p6etXv3KgjDZ/YM5tF797K/yRARQhAPdnZ2YWFhR06dGjuzMuPyNQalVKPHj1cXFx2795dWlqq91gAGA190fpFEKrC29u7a9euZWVlrC8DoN4RhPWLIFSL/SY2twkB1DuCsH4RhGrhNiEANVit1oyMDCGEzFvp1i+CUC1UhADUkJ2dXVBQ0KFDBz8/P65wvSAI1RIREWE2mzMzM8urNhwGgDqjL1rvCEK1eHt7d+7cubS0NCsrS7UXASAdgrDeEYSqd0dZXwZAPSII6x1BqPp8GSaOAqgvzJRRA0GoIipCAPWLmTJqIAhVFBERYTKZMjIymC8DoF7QF1UDQaiixo0bd+rUqaSkJDs7W83XASALglANBKG6eKweQD0iCNVAEKqLx+oB1BdmyqiEIFQXFSGA+sJMGZUQhOqKjIw0mUzp6ekWi0XllwJgcPRFVUIQqqtJkyYdOnQoLi7OyclR+aUAGBxBqBKCUHU8TQigXhCEKiEIVcf6MgDqjpky6iEIVUdFCKDucnJyCgoKbrjhBnZfqncEoUZBmJ6ebrVa1X81AMaUnJwshOjVq5feAzEgglB1zZo1a9++fWFhYW5urvqvBsCYuEGoHoJQC3RHAdQRQageglALzJcBUBfMlFEVQagFKkIAdcFMGVURhNoFYVpaGvNlANQCfVFVEYRa8Pf3b9u2bX5+/g8//KDJCwIwFIJQVQShRrhNCKDWeHZCVQShRrhNCKB2mCmjNoJQI1SEAGqHmTJqIwg1Yl8PIiUlxWazafWaAIyAG4RqIwg10rJly1atWp0/f/7AgQNavSYAIyAI1UYQaofbhABqgZkyaiMItcNtQgDXi5kyGiAItUNFCOB6MVNGAwShDkHIfBkANcQNQg0QhNpp06ZNy5Ytz549e/DgQQ1fFoATIwg1QBBqqmfPnlXf2QBwTQShBghCHbqjqamp2r4sAKdktVrT09OrptpBJQShpuzfzVSEAGqCmTLaIAg1xcRRADVHX1QbBKGm2rdv7+fnd+rUqUOHDmn7ygCcD0GoDYJQazxWD6CGCEJtEIRa4zYhgJpgpoxmCEKtMXEUQE0wU0YzBKHWqAgB1AR9Uc0QhFrr0KFDs2bN8vLyjhw5ovmLA3AaBKFmCEKtmUwm1pcBcE0EoWYIQh1wmxDA1TFTRksEoQ64TQjg6pgpoyWCUAdUhACujr6olghCHXTs2LFJkyZHjx49duyYHq8PwNERhFoiCHVgMpl69OjBNhQAqkMQaokg1AerbwOoDjNlNEYQ6oMVRwFUh5kyGiMI9UFFCKA69EU1RhDqo0uXLt7e3ocPH87JydFpCAAc1KZNm6p+XYYGCEJ9mEwmFxcXIcTy5ct1GgIAB/Xvf/9bCNGgQQO9ByILglA3nTp1EkJ89913+g0BgMOxWCz5+flCiLvvvlvvsciCINRNVFSUEGL37t36DQGAw/nmm29sNpurq2tgYKDeY5EFQaibxx57zGQyHT9+nO4ogCpTp04VQgQEBHBNNEMQ6qZv376hoaFCiGHDhhUXF+s3EACO4ssvv9y8ebMQYvTo0XqPRSImm82m9xjkdfLkyTZt2pSXl//xj39cs2aN3sMBoKeSkpLmzZsXFRWFhoZmZmbyZmiGilBPfn5+b731lslkWr9+vf3JIQDSmjJlSlFRkaur6zfffKP3WORCRai/p59++r333gsPD9+5c6ebm5vewwGgg4yMjBtvvNFisXz11Vf33nsv74GWqAj1l5CQ0Llz54yMDPtNcgCysVgssbGxZWVlTz31FCmoPSpCh7Bly5aBAweazeakpCSWkwBkM2HChMmTJwcEBGRmZnp7e+s9HOlQETqE/v37jx49uuq3Qr2HA0A79m6Q2WxesGABKagLgtBRTJs2jQYpIJvy8vKYmJjy8vIxY8bcfPPNeg9HUrRGHQgNUkA2r7zyymuvvdaxY8eMjAzKQb1QEToQGqSAVNLS0hISEmiK6o4gdCw0SAFJlJWVDRs2rLy8/Nlnnx04cKDew5EarVGHQ4MUkMFLL730xhtvdO3aNT09nR2X9EVF6HBokAKGl5aWNn36dLPZPG/ePFJQdwShI6JBChhYaWnp0KFDy8vLx44dO2DAAL2HA1qjjooGKWBUL7744rRp07p165aWlkY56AioCB0UDVLAkFJTU2fOnElT1KEQhI6LBilg1KbouHHjbrrpJr2Hg0rMGnVoNEgBI4mPj3/zzTcDAwNTU1NpijoOKkKHRoMUMIzt27fPnDnTxcVl0aJFpKBDIQgdHQ1SwABKS0tjY2MrKipeeOGFG2+8Ue/h4DdojToBGqSAsxs3btzMmTO7d++emprq6emp93DwG1SEToAGKeDUkpKS3n77bVdX10WLFpGCDoggdA40SAEnVVJSYm+KxsfH9+7dW+/h4ApojToNGqSAMxo7duxbb71FU9SRURE6DRqkgNPZtm3bO++8Q1PUwRGEzoQGKeBEiouLhw0bVlFR8eKLL9IUdWS0Rp0MDVLAWTz77LOzZs0KCwvbuXOnu7u73sNBtagInQwNUsApbN269b333nN1dZ0/fz4p6OAIQudDgxRwcMXFxcOHD6+oqHjppZd69eql93BwDbRGnRINUsCRjRkz5t133w0PD9+xYwfloOOjInRKNEgBh7Vly5b333+fpqgTIQid1bRp07p06ZKRkTF16lS9xwLgN01Rq9X6yiuvREZGcl2cAq1RJ7Z169YBAwaYzeakpCT+kwMcwVNPPfX+++/36NFjx44dbm5ueg8HNUJF6MT69es3evRoi8USGxtbVlam93AA2W3cuPGDDz5wc3ObP38+KehECELnRoMUcBBFRUWPP/64zWZ79dVXIyIi9B4OrgOtUadHgxRwBKNGjZozZ07Pnj23b99OOehcqAidHg1SQHcbNmxITEx0d3dftGgRKeh0CEIjoEEK6KioqCguLs5ms02YMCE0NJT3wunQGjUIGqSAXkaOHDl37lyaos6LitAgaJACuli/fv2HH37o4eHx8ccf0xR1UgShcdAgBTSWn58fExNjs9kmTZoUEhLC9XdStEYNhQYpoKXHH3983rx5ERERSUlJlIPOi4rQUGiQAppZt27d/PnzaYoaAEFo2AbpG2+8ofdYAMPKz8+PjY212WxTpkwJDg7WezioE1qjBkSDFFBbbGzsRx991KdPn82bN7u4uHDBnRoVoQH169fvqaeeYg1SQCVr165dsGCBh4fH/PnzSUEDIAiNaerUqTRIATWcP3/e3hR9/fXXg4KCuMgGQGvUsGiQAmqIjo5euHBh3759N23aRDloDFSEhkWDFKh3q1evXrhwoaen57x580hBwyAIjYwGKVCPzp8//8QTTwgh3njjDZqiRkJr1OBokAL1ZejQoYsXL6YpajxUhAZHgxSoF6tWrVq8eLGXl9eiRYtoihoMQWh8NEiBOjp37tyoUaPsTdEuXbpwPQ2G1qgUaJACdfG3v/1tyZIl/fr127Rpk9lM/WA0vKNSoEEK1NrKlSuXLFni5eW1cOFCUtCQCEJZ0CAFauH06dNxcXFCiISEBJqiRkVrVCI0SIHr9cgjj3zyySf9+/f//vvvKQeNiopQxgbpHXfcUVhYqPdwAEf32muvffLJJw0bNqQpamxUhHIpLCxs1qxZWVnZzTff/O233+o9HMBx5ebmdu/e3Wq1jh8/furUqXoPByqiIpSLt7f322+/LYT47rvvlixZovdwAMd1xx13WK3WJk2avP7663qPBeoiCKUzatSo8PBwIcSwYcNeeOEFvYcDOJzCwsI+ffocOnTIZDJ9+eWX3Bo0PIJQRmvXrm3fvn1FRcX06dMDAgL27dun94gAR/HJJ5/4+flt375dCDF27NiBAwfqPSKojiCUkZ+f348//vi3v/3NZDL99NNP/fr1+/zzz/UeFKAzi8Uybdq0Rx99tKSkxM3NbdasWTNmzNB7UNACk2Wk9v333z/99NOZmZlCiMGDB8+ePbtZs2Z6DwrQwa5du4YPH56ammoymYKDg9evX+/v7887IQkqQqkNHDgwPT09MTHR29t7+fLlwcHBX3zxhd6DAjRlsVgSEhJ69eqVmpoaEBCwfv36Xbt2kYJSoSKE4scff4yNjd24cSOlIaQtBB9//PGZM2d6e3vrPShojYoQCvsvwlWlYUhIyIoVK7g0kKoQtH//6z0u6ICKEL9x4MCB2NhY+7P2gwcPnjNnTtOmTblGMBgKQVyKihC/0bFjxw0bNiQmJjZs2NB+1/Crr77iGsEwKATxe1SEuLIDBw7ExMR89913lIYwDApBXBEVIWpUGoaEhKxcuZKLBSdFIYiroCLENezfvz8mJub777+3l4aJiYlNmjThqsGJUAji6qgIcQ2dOnXauHFjYmKil5eX/a7hqlWruGpwChSCqAkqQtTUDz/8EBMTs2nTJvsTVzNmzGjUqBGXDw6LQhA1REWImurcufPGjRunTZvm7u4+d+7c0NDQdevWcfnggCgEcV2oCHHd9uzZM3z48J07d1IawgFRCOJ6URHiugUFBW3durWqNAwLC1u/fj3XEbqjEETtUBGi9rKysoYPH56cnMw6jdAdhSBqjYoQtRccHLxt27ZL7xpu2LCBCwqNUQiijqgIUQ92794dHR1NaQjtUQii7qgIUQ9CQkLspaGbm5v9rqF9RydAPRSCqC9UhKhP/HoObfCdhnpERYj6FBoaun379qrSMDw83L6jE1BfKARR76gIocUv7P/85z8bNmzItUb9fl+xoTzqBRUhVBEaGpqUlHTpXUP7jk5A7VAIQj1UhFBXZmbm8OHD09LSzGbziBEjKA1RCxSCUBUVIdQVFhZmv2vo4uJiv2to39EJqAkKQWiAihAaycjIGD58eHp6ur00fOutt7y8vLj6uAoKQWiDihAaCQ8P37FjR1Vp2Lt37x07dnD1cUUUgtASFSG0tmPHjuHDh+/du9fV1fW5556bNGmSh4cHbwOqUAhCY1SE0NqNN96YmpoaHx9vs9kSEhIiIiJ27tzJ2wAKQeiFihC62b59+/Dhw7OzsykNQSEIHVERQjdRUVFpaWlVpWFkZGRycjLvh4S4Iwh9URFCf0lJSdHR0ZSGctq1a1d0dHRKSgqLxUAvVITQX58+fS4tDXv16pWSkqL3oKBdIZiSkhIQELB+/frExERvb28uPTRGRQgHsm3btujo6JycHPtdw8mTJ7u7u+s9KKiCQhCOg4oQDqRv376XlYapqal6Dwr1jEIQjoaKEI5o69at0dHRubm5lIYGQyEIB0RFCEfUr1+/9PT0+Ph4q9WakJDQu3dvSkNnRyEIh0VFCIe2ZcuW6Ojoffv2mc3mG2+8ce3atUymcEb/+c9/4uLizp49y9RQOCCCEI7uwoULI0eOXLx4sRCiQYMGH3zwwfDhw/UeFGrqwoULgwYNWrdunRCiSZMmn3322a233srlg0MhCOEc3nnnnXHjxpWXlyvftSaTr69vt27dRo0aFRkZGRQUZDKZ9B4gfnX06NGFCxf+97//zcnJOX36tNVqta+6vnbtWj8/P64UHA1BCKdx7NixIUOGbN261f6DtYq/v3/kJdq1a6ffGGVks9n279+fkpKSmppq//Ps2bOXnuDr6zt58uSnn35avzECV0MQwvls3779yy+/PHDgQHFx8Y4dO06cOHHpR319fYODg6tykXpRDUePHk35RVJS0qlTpy79aJMmTVq3bt2wYcM+ffoMGjTotttuo2SHIyMIYagfyuSiBhd527Ztp0+fvvSjrVq1ioyMDA4ODgoKsh+oNQ5ABQQhjIZcrLuKiors7Gx77O3ZsyclJeWybqc9+ex69erVqlWrenhVQCcEIQyOXKwJi8WSk5NTVfOlp6cXFRVVl3y9e/du2bKlWm8YoDmCEHIhF6+YfGlpacXFxdUlX1RUlL+/vz5vGKA+ghBSkycXy8vLc3Nzq/7PpqamXrhwobrk69OnD885QB4EIWDMXLws+VJSUkpKSqpLvr59+zZv3ly/wQJ6IgiBGuXizp078/LyHDkXL0u+5OTk0tLS6pKvf//+TZs21W+wgAMhCAFnzcXCwsL09PQ9e/ZkZWXZh1RWVlb1URcXl27dulWNp2fPng0bNlR1PICTIggBp8nFgoKCjIyMqhfNycmpqKio+qirq2vXrl2rXjEiIsLLy6uOrwjIgCAEtMjFxo0bh4SEXG8uXpZ82dnZly4vd1nyRUZGNmjQgLcTuF4EIaBnLnp5ef3hInsuHjx48IsvvrBYLJmZmb9PPjc3ty5dupB8QP0iCAEtHDx48NIJnJctzmk2m319ffPz8y0Wy6X/3qBBg7CwMHuf0750mZubG28YUL8IQkC3evH7779ftGhR1UZFdl5eXgMGDLj77rvta7h4eHjwDgGqIggB/W3dujUjI6N169Z33323u7u73sMB5EIQAgCkZtZ7AAAA6IkgBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAEiNIAQASI0gBABIjSAEAAiZ/T+ZEc/VDKSuYgAAAABJRU5ErkJggg==",
|
||
"text/plain": [
|
||
"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x600>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"母核信息:\n",
|
||
" 原子数: 18\n",
|
||
" 键数: 18\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 展示母核（目标SMARTS模式对应的分子）\n",
|
||
"if 'target_smiles' in locals():\n",
|
||
" print(f\"母核SMILES: {target_smiles}\")\n",
|
||
" print(f\"母核SMARTS: {target_smarts if 'target_smarts' in locals() else 'N/A'}\\n\")\n",
|
||
" \n",
|
||
" # 显示母核分子\n",
|
||
" if 'target_mol' in locals() and target_mol is not None:\n",
|
||
" from rdkit.Chem import AllChem\n",
|
||
" \n",
|
||
" # 确保有2D坐标\n",
|
||
" AllChem.Compute2DCoords(target_mol)\n",
|
||
" \n",
|
||
" # 显示分子\n",
|
||
" img = Draw.MolToImage(target_mol, size=(600, 600))\n",
|
||
" display(img)\n",
|
||
" \n",
|
||
" print(f\"\\n母核信息:\")\n",
|
||
" print(f\" 原子数: {target_mol.GetNumAtoms()}\")\n",
|
||
" print(f\" 键数: {target_mol.GetNumBonds()}\")\n",
|
||
" else:\n",
|
||
" print(\"错误: 无法显示母核分子\")\n",
|
||
"else:\n",
|
||
" print(\"请先运行前面的cell来定义target_smiles\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4.7. 选择3个分子进行SMARTS匹配原子高亮显示\n",
|
||
"\n",
|
||
"从过滤结果中选择3个分子，高亮显示匹配SMARTS模式的原子，用于视觉检查\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"选择了 3 个分子进行SMARTS匹配原子高亮显示:\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"分子 1: mol_00113\n",
|
||
"================================================================================\n",
|
||
"SMILES: CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O)C(N(C)C)C2O)C(CCN2CC(C)CC(C)C2)CC(C)C(=O)/C=C/C(C)=C\\[C@@H]1COC1OC(C)C(O)C(OC)C1OC\n",
|
||
"\n",
|
||
"SMARTS匹配的原子索引: [40, 39, 37, 36, 25, 11, 9, 7, 6, 4, 5, 3, 2, 46, 45, 43, 42, 41]\n",
|
||
"匹配的环上原子位置: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]\n"
|
||
]
|
||
},
|
||
{
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},
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{
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"text": [
|
||
"\n",
|
||
"================================================================================\n",
|
||
"分子 2: mol_00288\n",
|
||
"================================================================================\n",
|
||
"SMILES: CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]2O[C@H](C)[C@@H](O[C@H]3C[C@@](C)(O)[C@@H](O)[C@H](C)O3)[C@H](N(C)C)[C@H]2O)[C@@H](CC=O)C[C@@H](C)C(=O)/C=C/C(C)=C/[C@@H]1CO[C@@H]1O[C@H](C)[C@@H](O)[C@@H](OC)[C@H]1OC\n",
|
||
"\n",
|
||
"SMARTS匹配的原子索引: [43, 42, 40, 39, 35, 11, 9, 7, 6, 4, 5, 3, 2, 49, 48, 46, 45, 44]\n",
|
||
"匹配的环上原子位置: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]\n"
|
||
]
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},
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{
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},
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{
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"name": "stdout",
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"text": [
|
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"\n",
|
||
"================================================================================\n",
|
||
"分子 3: mol_00313\n",
|
||
"================================================================================\n",
|
||
"SMILES: CC[C@H]1OC(=O)C[C@@H](O)[C@H](C)[C@@H](O[C@@H]2O[C@H](C)[C@@H](O[C@H]3C[C@@](C)(O)[C@@H](O)[C@H](C)O3)[C@H](N(C)C)[C@H]2O)[C@@H](CC=O)C[C@@H](C)C(=O)/C=C/C(C)=C/[C@@H]1CO[C@@H]1O[C@H](C)[C@@H](O)[C@@H](OC)[C@H]1OC\n",
|
||
"\n",
|
||
"SMARTS匹配的原子索引: [43, 42, 40, 39, 35, 11, 9, 7, 6, 4, 5, 3, 2, 49, 48, 46, 45, 44]\n",
|
||
"匹配的环上原子位置: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]\n"
|
||
]
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||
},
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{
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"</svg>"
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],
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"text/plain": [
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"<IPython.core.display.SVG object>"
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]
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},
|
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"metadata": {},
|
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"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 选择3个匹配的分子进行高亮显示\n",
|
||
"if 'df_filtered_smarts' in locals() and len(df_filtered_smarts) > 0:\n",
|
||
" # 选择前3个匹配的分子\n",
|
||
" selected_molecules = df_filtered_smarts.head(3)\n",
|
||
" \n",
|
||
" print(f\"选择了 {len(selected_molecules)} 个分子进行SMARTS匹配原子高亮显示:\\n\")\n",
|
||
" \n",
|
||
" # 确保pattern存在\n",
|
||
" if 'pattern' not in locals():\n",
|
||
" if 'target_smarts' in locals():\n",
|
||
" pattern = Chem.MolFromSmarts(target_smarts)\n",
|
||
" elif 'target_smiles' in locals():\n",
|
||
" pattern = Chem.MolFromSmiles(target_smiles)\n",
|
||
" else:\n",
|
||
" pattern = None\n",
|
||
" \n",
|
||
" if pattern is not None:\n",
|
||
" for idx, (mol_idx, row) in enumerate(selected_molecules.iterrows(), 1):\n",
|
||
" print(f\"{'='*80}\")\n",
|
||
" print(f\"分子 {idx}: {row['unique_id']}\")\n",
|
||
" print(f\"{'='*80}\")\n",
|
||
" print(f\"SMILES: {row['smiles']}\\n\")\n",
|
||
" \n",
|
||
" mol = Chem.MolFromSmiles(row['smiles'])\n",
|
||
" if mol is not None:\n",
|
||
" # 获取SMARTS匹配的原子\n",
|
||
" matches = mol.GetSubstructMatches(pattern)\n",
|
||
" \n",
|
||
" if matches:\n",
|
||
" # 使用第一个匹配\n",
|
||
" matched_atoms = list(matches[0])\n",
|
||
" print(f\"SMARTS匹配的原子索引: {matched_atoms}\")\n",
|
||
" \n",
|
||
" # 获取环原子和编号\n",
|
||
" ring_atoms = get_ring_atoms(mol)\n",
|
||
" ring_numbering = assign_ring_numbering(mol)\n",
|
||
" \n",
|
||
" if len(ring_atoms) > 0 and len(ring_numbering) > 0:\n",
|
||
" # 找出匹配的环上原子位置\n",
|
||
" matched_ring_positions = []\n",
|
||
" for atom_idx in matched_atoms:\n",
|
||
" if atom_idx in ring_numbering:\n",
|
||
" matched_ring_positions.append(ring_numbering[atom_idx])\n",
|
||
" \n",
|
||
" print(f\"匹配的环上原子位置: {sorted(matched_ring_positions)}\")\n",
|
||
" \n",
|
||
" # 高亮显示匹配的原子\n",
|
||
" from rdkit.Chem import AllChem\n",
|
||
" from rdkit.Chem.Draw import rdMolDraw2D\n",
|
||
" \n",
|
||
" AllChem.Compute2DCoords(mol)\n",
|
||
" \n",
|
||
" drawer = rdMolDraw2D.MolDraw2DSVG(800, 800)\n",
|
||
" drawer.SetFontSize(6)\n",
|
||
" \n",
|
||
" draw_options = drawer.drawOptions()\n",
|
||
" draw_options.addAtomIndices = True # 显示原子索引\n",
|
||
" \n",
|
||
" # 高亮匹配的原子（红色）\n",
|
||
" highlight_atoms = matched_atoms\n",
|
||
" atom_colors = {}\n",
|
||
" for atom_idx in matched_atoms:\n",
|
||
" atom_colors[atom_idx] = (1.0, 0.3, 0.3) # 红色高亮\n",
|
||
" \n",
|
||
" # 高亮环上的原子（浅蓝色）\n",
|
||
" if len(ring_atoms) > 0:\n",
|
||
" for atom_idx in ring_atoms:\n",
|
||
" if atom_idx not in atom_colors:\n",
|
||
" atom_colors[atom_idx] = (0.8, 0.9, 1.0) # 浅蓝色\n",
|
||
" highlight_atoms = list(set(highlight_atoms + ring_atoms))\n",
|
||
" \n",
|
||
" drawer.DrawMolecule(mol, \n",
|
||
" highlightAtoms=highlight_atoms,\n",
|
||
" highlightAtomColors=atom_colors)\n",
|
||
" \n",
|
||
" drawer.FinishDrawing()\n",
|
||
" svg = drawer.GetDrawingText()\n",
|
||
" display(SVG(svg))\n",
|
||
" else:\n",
|
||
" print(\"警告: 未找到SMARTS匹配\")\n",
|
||
" else:\n",
|
||
" print(\"错误: 无法解析SMILES\")\n",
|
||
" print()\n",
|
||
" else:\n",
|
||
" print(\"错误: 无法创建SMARTS模式\")\n",
|
||
"else:\n",
|
||
" print(\"请先运行前面的cell来生成df_filtered_smarts\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4.8. 对选定分子进行碎片裂解\n",
|
||
"\n",
|
||
"对选定的3个分子进行碎片裂解，只对**没有匹配SMARTS的环上位置**进行侧链断链。\n",
|
||
"\n",
|
||
"参考 `fragment_dataclass.py` 和 `test_align_two_molecules.ipynb` 中的断裂方式。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 24,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"✓ 函数定义完成\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 导入必要的模块\n",
|
||
"from src.fragment_dataclass import Fragment, MoleculeFragments\n",
|
||
"from src.ring_visualization import (\n",
|
||
" get_macrolactone_numbering,\n",
|
||
" identify_side_chains, \n",
|
||
" extract_side_chain_fragment,\n",
|
||
" cleave_side_chain_at_position\n",
|
||
")\n",
|
||
"from src.ring_numbering import get_ring_position\n",
|
||
"from rdkit.Chem import Descriptors\n",
|
||
"\n",
|
||
"def cleave_side_chains_excluding_smarts(\n",
|
||
" mol: Chem.Mol,\n",
|
||
" parent_smiles: str,\n",
|
||
" parent_id: str,\n",
|
||
" ring_numbering: dict,\n",
|
||
" smarts_pattern: Chem.Mol,\n",
|
||
" cleave_all: bool = False\n",
|
||
") -> MoleculeFragments:\n",
|
||
" \"\"\"\n",
|
||
" 对分子进行侧链断裂，排除匹配SMARTS模式的环上位置。\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" mol: RDKit分子对象\n",
|
||
" parent_smiles: 母分子的SMILES字符串\n",
|
||
" parent_id: 母分子ID\n",
|
||
" ring_numbering: 环原子编号字典 {atom_idx: position}\n",
|
||
" smarts_pattern: SMARTS模式分子对象\n",
|
||
" cleave_all: 如果为True，断裂所有侧链；如果为False，只断裂未匹配SMARTS的位置\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" MoleculeFragments对象，包含所有断裂的碎片\n",
|
||
" \"\"\"\n",
|
||
" # 创建MoleculeFragments对象\n",
|
||
" mol_fragments = MoleculeFragments(\n",
|
||
" parent_id=parent_id,\n",
|
||
" parent_smiles=parent_smiles,\n",
|
||
" ring_numbering={str(k): v for k, v in ring_numbering.items()}\n",
|
||
" )\n",
|
||
" \n",
|
||
" # 获取SMARTS匹配的原子\n",
|
||
" matches = mol.GetSubstructMatches(smarts_pattern)\n",
|
||
" matched_atoms = set()\n",
|
||
" if matches:\n",
|
||
" matched_atoms = set(matches[0]) # 使用第一个匹配\n",
|
||
" \n",
|
||
" # 找出匹配SMARTS的环上原子位置（只包括真正匹配的环原子）\n",
|
||
" matched_ring_positions = set()\n",
|
||
" ring_atoms = list(ring_numbering.keys())\n",
|
||
" ring_atoms_set = set(ring_atoms)\n",
|
||
" \n",
|
||
" # 只考虑匹配的原子中属于环的原子\n",
|
||
" for atom_idx in matched_atoms:\n",
|
||
" if atom_idx in ring_atoms_set and atom_idx in ring_numbering:\n",
|
||
" matched_ring_positions.add(ring_numbering[atom_idx])\n",
|
||
" \n",
|
||
" print(f\" 匹配SMARTS的环上位置: {sorted(matched_ring_positions)}\")\n",
|
||
" print(f\" 匹配SMARTS的环上原子索引: {sorted([idx for idx in matched_atoms if idx in ring_atoms_set])}\")\n",
|
||
" \n",
|
||
" # 识别所有侧链\n",
|
||
" side_chains = identify_side_chains(mol, ring_atoms)\n",
|
||
" \n",
|
||
" fragment_counter = 0\n",
|
||
" \n",
|
||
" # 遍历每个侧链\n",
|
||
" for ring_atom_idx, side_chain_start_idx in side_chains:\n",
|
||
" # 获取该侧链连接的环原子位置\n",
|
||
" cleavage_position = get_ring_position(ring_numbering, ring_atom_idx)\n",
|
||
" \n",
|
||
" if cleavage_position is None:\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 如果cleave_all=False，跳过匹配SMARTS的位置\n",
|
||
" if not cleave_all and cleavage_position in matched_ring_positions:\n",
|
||
" print(f\" 跳过位置 {cleavage_position}（匹配SMARTS）\")\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 提取侧链碎片\n",
|
||
" fragment_smiles = extract_side_chain_fragment(\n",
|
||
" mol, ring_atom_idx, side_chain_start_idx, ring_atoms\n",
|
||
" )\n",
|
||
" \n",
|
||
" if fragment_smiles is None:\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 创建碎片分子对象以获取属性\n",
|
||
" frag_mol = Chem.MolFromSmiles(fragment_smiles)\n",
|
||
" if frag_mol is None:\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 创建Fragment对象\n",
|
||
" fragment = Fragment(\n",
|
||
" fragment_smiles=fragment_smiles,\n",
|
||
" parent_smiles=parent_smiles,\n",
|
||
" cleavage_position=cleavage_position,\n",
|
||
" fragment_id=f\"{parent_id}_frag_{fragment_counter}\",\n",
|
||
" parent_id=parent_id,\n",
|
||
" atom_count=frag_mol.GetNumAtoms(),\n",
|
||
" molecular_weight=round(Descriptors.MolWt(frag_mol), 2)\n",
|
||
" )\n",
|
||
" \n",
|
||
" mol_fragments.add_fragment(fragment)\n",
|
||
" fragment_counter += 1\n",
|
||
" print(f\" ✓ 位置 {cleavage_position}: {fragment_smiles}\")\n",
|
||
" \n",
|
||
" return mol_fragments\n",
|
||
"\n",
|
||
"\n",
|
||
"print(\"✓ 函数定义完成\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4.9. 选择一个分子进行可视化\n",
|
||
"\n",
|
||
"选择一个分子，使用正确的编号方法（`get_macrolactone_numbering`）进行可视化\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 25,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"选择的分子: mol_00113\n",
|
||
"SMILES: CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O)C(N(C)C)C2O)C(CCN2CC(C)CC(C)C2)CC(C)C(=O)/C=C/C(C)=C\\[C@@H]1COC1OC(C)C(O)C(OC)C1OC\n",
|
||
"\n",
|
||
"✓ 编号成功\n",
|
||
" 环原子数: 16\n",
|
||
" 编号范围: 1-16\n",
|
||
" 羰基碳位置: 1\n",
|
||
" 酯键氧位置: 2\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/svg+xml": [
|
||
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" baseProfile=\"full\" xml:space=\"preserve\" width=\"800px\" height=\"800px\" viewBox=\"0 0 800 800\">\n",
|
||
"<!-- END OF HEADER -->\n",
|
||
"<rect style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"800.0\" height=\"800.0\" x=\"0.0\" y=\"0.0\"> </rect>\n",
|
||
"<path class=\"bond-2 atom-2 atom-3\" d=\"M 472.5,416.3 L 481.4,421.0 L 451.0,458.8 L 444.5,451.0 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-60 atom-2 atom-46\" d=\"M 485.0,373.5 L 495.1,374.4 L 481.4,421.0 L 472.5,416.3 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-3 atom-3 atom-4\" d=\"M 444.5,451.0 L 451.0,458.8 L 408.4,482.1 L 405.4,472.4 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-5 atom-4 atom-6\" d=\"M 405.4,472.4 L 408.4,482.1 L 360.1,487.3 L 361.0,477.2 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-6 atom-6 atom-7\" d=\"M 318.2,464.6 L 361.0,477.2 L 360.1,487.3 L 313.5,473.6 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-8 atom-7 atom-9\" d=\"M 283.5,436.7 L 318.2,464.6 L 313.5,473.6 L 275.7,443.2 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-10 atom-9 atom-11\" d=\"M 262.1,397.5 L 283.5,436.7 L 275.7,443.2 L 252.5,400.6 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-24 atom-11 atom-25\" d=\"M 247.3,352.3 L 257.3,353.2 L 262.1,397.5 L 252.5,400.6 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-35 atom-25 atom-36\" d=\"M 260.9,305.7 L 269.9,310.4 L 257.3,353.2 L 247.3,352.3 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-36 atom-36 atom-37\" d=\"M 291.4,267.9 L 297.9,275.7 L 269.9,310.4 L 260.9,305.7 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-38 atom-37 atom-39\" d=\"M 334.0,244.6 L 337.0,254.3 L 297.9,275.7 L 291.4,267.9 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-40 atom-39 atom-41\" d=\"M 383.3,239.3 L 380.3,249.6 L 337.0,254.3 L 334.0,244.6 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-41 atom-41 atom-42\" d=\"M 383.3,239.3 L 420.4,269.2 L 415.7,278.2 L 380.3,249.6 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-42 atom-42 atom-43\" d=\"M 420.4,269.2 L 466.1,282.6 L 459.4,291.0 L 415.7,278.2 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-44 atom-43 atom-45\" d=\"M 466.1,282.6 L 489.9,326.1 L 480.2,329.2 L 459.4,291.0 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-45 atom-45 atom-46\" d=\"M 489.9,326.1 L 495.1,374.4 L 485.0,373.5 L 480.2,329.2 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<ellipse cx=\"476.9\" cy=\"418.6\" rx=\"9.3\" ry=\"9.3\" class=\"atom-2\" style=\"fill:#CCE5FF;fill-rule:evenodd;stroke:#CCE5FF;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
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|
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|
||
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|
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|
||
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|
||
"<ellipse cx=\"257.3\" cy=\"399.0\" rx=\"9.3\" ry=\"9.3\" class=\"atom-11\" style=\"fill:#CCE5FF;fill-rule:evenodd;stroke:#CCE5FF;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<ellipse cx=\"252.3\" cy=\"352.8\" rx=\"9.3\" ry=\"9.3\" class=\"atom-25\" style=\"fill:#CCE5FF;fill-rule:evenodd;stroke:#CCE5FF;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<ellipse cx=\"265.4\" cy=\"308.1\" rx=\"9.3\" ry=\"9.3\" class=\"atom-36\" style=\"fill:#CCE5FF;fill-rule:evenodd;stroke:#CCE5FF;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<ellipse cx=\"294.6\" cy=\"271.8\" rx=\"9.3\" ry=\"9.3\" class=\"atom-37\" style=\"fill:#CCE5FF;fill-rule:evenodd;stroke:#CCE5FF;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<ellipse cx=\"335.5\" cy=\"249.5\" rx=\"9.3\" ry=\"9.3\" class=\"atom-39\" style=\"fill:#CCE5FF;fill-rule:evenodd;stroke:#CCE5FF;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<ellipse cx=\"381.8\" cy=\"244.5\" rx=\"9.3\" ry=\"9.3\" class=\"atom-41\" style=\"fill:#CCE5FF;fill-rule:evenodd;stroke:#CCE5FF;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<ellipse cx=\"418.1\" cy=\"273.7\" rx=\"9.3\" ry=\"9.3\" class=\"atom-42\" style=\"fill:#CCE5FF;fill-rule:evenodd;stroke:#CCE5FF;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<ellipse cx=\"462.7\" cy=\"286.8\" rx=\"9.3\" ry=\"9.3\" class=\"atom-43\" style=\"fill:#CCE5FF;fill-rule:evenodd;stroke:#CCE5FF;stroke-width:1.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"</svg>"
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],
|
||
"text/plain": [
|
||
"<IPython.core.display.SVG object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
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"text/plain": [
|
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"<IPython.core.display.SVG object>"
|
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]
|
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},
|
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"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"✓ 分子信息已保存，可用于后续断裂操作\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 选择一个分子进行可视化（使用get_macrolactone_numbering）\n",
|
||
"if 'selected_molecules' in locals() and len(selected_molecules) > 0:\n",
|
||
" # 选择第一个分子\n",
|
||
" selected_mol_idx = 0\n",
|
||
" selected_row = selected_molecules.iloc[selected_mol_idx]\n",
|
||
" \n",
|
||
" print(f\"选择的分子: {selected_row['unique_id']}\")\n",
|
||
" print(f\"SMILES: {selected_row['smiles']}\\n\")\n",
|
||
" \n",
|
||
" mol = Chem.MolFromSmiles(selected_row['smiles'])\n",
|
||
" if mol is not None:\n",
|
||
" # 使用get_macrolactone_numbering进行编号（与test_align_two_molecules.ipynb一致）\n",
|
||
" ring_atoms, ring_numbering, ordered_atoms, carbonyl_carbon, ester_oxygen = \\\n",
|
||
" get_macrolactone_numbering(mol, ring_size=16)\n",
|
||
" \n",
|
||
" if ring_numbering is not None:\n",
|
||
" print(f\"✓ 编号成功\")\n",
|
||
" print(f\" 环原子数: {len(ring_atoms)}\")\n",
|
||
" print(f\" 编号范围: 1-{len(ring_numbering)}\")\n",
|
||
" print(f\" 羰基碳位置: {ring_numbering.get(carbonyl_carbon, 'N/A')}\")\n",
|
||
" print(f\" 酯键氧位置: {ring_numbering.get(ester_oxygen, 'N/A')}\")\n",
|
||
" \n",
|
||
" # 可视化\n",
|
||
" from src.ring_visualization import draw_mol_with_ring_numbering\n",
|
||
" svg = draw_mol_with_ring_numbering(\n",
|
||
" mol,\n",
|
||
" ring_numbering=ring_numbering,\n",
|
||
" ring_atoms=ring_atoms,\n",
|
||
" size=(800, 800),\n",
|
||
" title=selected_row['unique_id']\n",
|
||
" )\n",
|
||
" display(SVG(svg))\n",
|
||
" \n",
|
||
" # 保存到变量供后续使用\n",
|
||
" selected_mol_for_cleavage = mol\n",
|
||
" selected_ring_atoms = ring_atoms\n",
|
||
" selected_ring_numbering = ring_numbering\n",
|
||
" selected_parent_smiles = selected_row['smiles']\n",
|
||
" selected_parent_id = selected_row['unique_id']\n",
|
||
" \n",
|
||
" print(f\"\\n✓ 分子信息已保存，可用于后续断裂操作\")\n",
|
||
" else:\n",
|
||
" print(\"✗ 编号失败\")\n",
|
||
" else:\n",
|
||
" print(\"✗ 无法解析SMILES\")\n",
|
||
"else:\n",
|
||
" print(\"请先运行前面的cell来选择分子\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 4.10. 对选定分子进行碎片裂解\n",
|
||
"\n",
|
||
"使用正确的编号方法对选定的分子进行碎片裂解，只断裂未匹配SMARTS的位置\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 27,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"处理分子: mol_00113\n",
|
||
"================================================================================\n",
|
||
"SMILES: CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O)C(N(C)C)C2O)C(CCN2CC(C)CC(C)C2)CC(C)C(=O)/C=C/C(C)=C\\[C@@H]1COC1OC(C)C(O)C(OC)C1OC\n",
|
||
"\n",
|
||
"检测到的侧链连接位置:\n",
|
||
" 位置 16: 环原子2(C) -> 侧链原子1(C)\n",
|
||
" 位置 15: 环原子46(C) -> 侧链原子47(C)\n",
|
||
" 位置 13: 环原子43(C) -> 侧链原子44(C)\n",
|
||
" 位置 10: 环原子39(C) -> 侧链原子40(O)\n",
|
||
" 位置 9: 环原子37(C) -> 侧链原子38(C)\n",
|
||
" 位置 7: 环原子25(C) -> 侧链原子26(C)\n",
|
||
" 位置 6: 环原子11(C) -> 侧链原子12(O)\n",
|
||
" 位置 5: 环原子9(C) -> 侧链原子10(C)\n",
|
||
" 位置 4: 环原子7(C) -> 侧链原子8(O)\n",
|
||
" 位置 1: 环原子4(C) -> 侧链原子5(O)\n",
|
||
" ✓ 位置 4: *O (原子数: 2)\n",
|
||
" ✓ 位置 5: *C (原子数: 2)\n",
|
||
" ✓ 位置 6: *OC1OC(C)C(O)C(N(C)C)C1O (原子数: 14)\n",
|
||
" ✓ 位置 7: *CCN1CC(C)CC(C)C1 (原子数: 11)\n",
|
||
" ✓ 位置 9: *C (原子数: 2)\n",
|
||
" ✓ 位置 10: *=O (原子数: 2)\n",
|
||
" ✓ 位置 13: *C (原子数: 2)\n",
|
||
" ✓ 位置 15: *COC1OC(C)C(O)C(OC)C1OC (原子数: 15)\n",
|
||
" ✓ 位置 16: *CC (原子数: 3)\n",
|
||
"\n",
|
||
"断裂结果:\n",
|
||
" 总碎片数: 9\n",
|
||
" 断裂位置: [4, 5, 6, 7, 9, 10, 13, 15, 16]\n",
|
||
"\n",
|
||
"碎片详情:\n",
|
||
" - 位置 4: *O\n",
|
||
" 原子数: 2, 分子量: 17.01\n",
|
||
" - 位置 5: *C\n",
|
||
" 原子数: 2, 分子量: 15.04\n",
|
||
" - 位置 6: *OC1OC(C)C(O)C(N(C)C)C1O\n",
|
||
" 原子数: 14, 分子量: 190.22\n",
|
||
" - 位置 7: *CCN1CC(C)CC(C)C1\n",
|
||
" 原子数: 11, 分子量: 140.25\n",
|
||
" - 位置 9: *C\n",
|
||
" 原子数: 2, 分子量: 15.04\n",
|
||
" - 位置 10: *=O\n",
|
||
" 原子数: 2, 分子量: 16.0\n",
|
||
" - 位置 13: *C\n",
|
||
" 原子数: 2, 分子量: 15.04\n",
|
||
" - 位置 15: *COC1OC(C)C(O)C(OC)C1OC\n",
|
||
" 原子数: 15, 分子量: 205.23\n",
|
||
" - 位置 16: *CC\n",
|
||
" 原子数: 3, 分子量: 29.06\n",
|
||
"\n",
|
||
"碎片可视化:\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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"text/plain": [
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"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=400x400>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"碎片 1 (位置 4): *O\n"
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]
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},
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{
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"data": {
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAGQCAIAAAAP3aGbAAAHj0lEQVR4nO3cvY9N7R6A4T3mDCYm0YhJJgwZjSGn8CdIdBoNEQol0fiIVqFQqDSKiVIiUSFRik4UMiQnk/hIEKZydAqJYMxJTKM8efNu3nu7rnI1vyeruLP2s5+1xlZXVwcABet+9wIA/l+CBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkCFYQIZgARmCBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkCFYQIZgARmCBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkCFYQIZgARmCBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkCFYQIZgARmCBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkCFYQIZgARmCBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkCFYQIZgARmCBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkCFYQIZgARmCBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkCFYQIZgARmCBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkCFYQIZgARmCBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkCFYQIZgARmCBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkCFYQIZgARmCBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkCFYQIZgARmCBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkCFYQIZgARmCBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkCFYQIZgARmCBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkCFYQIZgARmCBWQIFpAhWECGYAEZggVkCBaQIVhAhmABGYIFZAgWkPGvXzDj3bt3Hz9+HAwGu3fvXr9+/S+YCIyksdXV1WHPOHTo0OPHj8fGxl6+fDk1NTXsccCo+hU/CWdnZweDwfT0tFoB/7hgffny5ecHtx07dgwGg127dg1jFvDnGEqwbt26deDAgXv37q1la9u2bT8Ha3l5+eLFixcuXBjGaGCEDWXT/cGDB8+fPz958uT8/PzZs2e3b9++Fqxnz54tLCzcvXv327dvU1NTly9f3rBhwzAWAIykv3/TfXV19fDhw4uLi1++fFm7Mjc39+bNm9nZ2eXl5bUr4+Pjc3NzCwsL8/Pzf+90YIQN61/Cr1+/vnjx4j8/3L9//8OHD5OTk0ePHt2zZ8/evXt37969cePGYcwFRtiwzmFNTEz8+4e5ubnbt2/v3Lnz7du3W7duPX78+JAmAiNvuMcaFhcXT5w4MTExcefOnZmZmStXrly/fn2oE4ERNsRgPXny5NixY58+fTp9+vT09PS5c+cGg8GlS5du3bo1vKHACBvWHtbS0tKRI0c+fvy4ZcuWR48eTU1Nrays7N+//9WrV+Pj4wsLCwcPHhzGXGCEDesJa2xsbOvWrYPB4Ny5c2sH3MfHx8+fP7928H3tZBbAP+Vdws+fP1+7du3MmTMTExNrV75//3716tVTp05t2rRpSEOBEfYrXn7+y27cuHHz5s3fvQqGZfPmza9fv3Z/R9XDhw8nJyd7n5f5y96/f7+0tPS7V8GwzMzMPH361P0dVSsrK3/WE9Z/f/jdq2BYVldXv3375v6Oqn379q1bt+4PChbAz3wiGcgQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsIEOwgAzBAjIEC8gQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsIEOwgAzBAjIEC8gQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsIEOwgAzBAjIEC8gQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsIEOwgAzBAjIEC8gQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsIEOwgAzBAjIEC8gQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsIEOwgAzBAjIEC8gQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsIEOwgAzBAjIEC8gQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsIEOwgAzBAjIEC8gQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsIEOwgAzBAjIEC8gQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsIEOwgAzBAjIEC8gQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsIEOwgAzBAjIEC8gQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsIEOwgAzBAjIEC8gQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsIEOwgAzBAjIEC8gQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsIEOwgAzBAjIEC8gQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsIEOwgAzBAjIEC8gQLCBDsIAMwQIyBAvIECwgQ7CADMECMgQLyBAsYFDxP9fI1p1wN/stAAAAAElFTkSuQmCC",
|
||
"text/plain": [
|
||
"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=400x400>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"碎片 2 (位置 5): *C\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
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||
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",
|
||
"text/plain": [
|
||
"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=400x400>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"碎片 3 (位置 6): *OC1OC(C)C(O)C(N(C)C)C1O\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
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"image/jpeg": 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",
|
||
"text/plain": [
|
||
"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=400x400>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"碎片 4 (位置 7): *CCN1CC(C)CC(C)C1\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
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"image/jpeg": 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{
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"碎片 5 (位置 9): *C\n"
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]
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},
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|
||
"text/plain": [
|
||
"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=400x400>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"碎片 6 (位置 10): *=O\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
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{
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"text": [
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"碎片 7 (位置 13): *C\n"
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]
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},
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{
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",
|
||
"text/plain": [
|
||
"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=400x400>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"碎片 8 (位置 15): *COC1OC(C)C(O)C(OC)C1OC\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/jpeg": 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",
|
||
"text/plain": [
|
||
"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=400x400>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"碎片 9 (位置 16): *CC\n",
|
||
"\n",
|
||
"================================================================================\n",
|
||
"碎片数据已保存在变量 'mol_fragments' 中\n",
|
||
"可以使用以下代码保存到文件:\n",
|
||
" mol_fragments.to_json_file('output/fragments/mol_00113_fragments.json')\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 对选定的分子进行碎片裂解（使用与test_align_two_molecules.ipynb一致的断裂方式）\n",
|
||
"if 'selected_mol_for_cleavage' in locals() and 'selected_ring_numbering' in locals():\n",
|
||
" print(f\"{'='*80}\")\n",
|
||
" print(f\"处理分子: {selected_parent_id}\")\n",
|
||
" print(f\"{'='*80}\")\n",
|
||
" print(f\"SMILES: {selected_parent_smiles}\\n\")\n",
|
||
" \n",
|
||
" # 创建MoleculeFragments对象\n",
|
||
" mol_fragments = MoleculeFragments(\n",
|
||
" parent_id=selected_parent_id,\n",
|
||
" parent_smiles=selected_parent_smiles,\n",
|
||
" ring_numbering={str(k): v for k, v in selected_ring_numbering.items()}\n",
|
||
" )\n",
|
||
" \n",
|
||
" # 识别所有侧链\n",
|
||
" side_chains = identify_side_chains(selected_mol_for_cleavage, selected_ring_atoms)\n",
|
||
" \n",
|
||
" print(f\"检测到的侧链连接位置:\")\n",
|
||
" side_chain_positions = []\n",
|
||
" for ring_atom_idx, side_start_idx in side_chains:\n",
|
||
" position = selected_ring_numbering.get(ring_atom_idx)\n",
|
||
" if position:\n",
|
||
" side_chain_positions.append(position)\n",
|
||
" ring_atom = selected_mol_for_cleavage.GetAtomWithIdx(ring_atom_idx)\n",
|
||
" side_atom = selected_mol_for_cleavage.GetAtomWithIdx(side_start_idx)\n",
|
||
" print(f\" 位置 {position}: 环原子{ring_atom_idx}({ring_atom.GetSymbol()}) -> 侧链原子{side_start_idx}({side_atom.GetSymbol()})\")\n",
|
||
" \n",
|
||
" # 断裂所有侧链（位置3-16，参考test_align_two_molecules.ipynb的做法）\n",
|
||
" fragment_counter = 0\n",
|
||
" for position in range(3, 17): # 位置3-16（跳过位置1和2，因为它们是酯基）\n",
|
||
" fragments = cleave_side_chain_at_position(\n",
|
||
" selected_mol_for_cleavage, \n",
|
||
" selected_ring_atoms, \n",
|
||
" selected_ring_numbering, \n",
|
||
" position\n",
|
||
" )\n",
|
||
" \n",
|
||
" for frag_smiles in fragments:\n",
|
||
" if frag_smiles:\n",
|
||
" # 创建碎片分子对象以获取属性\n",
|
||
" frag_mol = Chem.MolFromSmiles(frag_smiles)\n",
|
||
" if frag_mol:\n",
|
||
" # 创建Fragment对象\n",
|
||
" fragment = Fragment(\n",
|
||
" fragment_smiles=frag_smiles,\n",
|
||
" parent_smiles=selected_parent_smiles,\n",
|
||
" cleavage_position=position,\n",
|
||
" fragment_id=f\"{selected_parent_id}_frag_{fragment_counter}\",\n",
|
||
" parent_id=selected_parent_id,\n",
|
||
" atom_count=frag_mol.GetNumAtoms(),\n",
|
||
" molecular_weight=round(Descriptors.MolWt(frag_mol), 2)\n",
|
||
" )\n",
|
||
" \n",
|
||
" mol_fragments.add_fragment(fragment)\n",
|
||
" fragment_counter += 1\n",
|
||
" print(f\" ✓ 位置 {position}: {frag_smiles} (原子数: {frag_mol.GetNumAtoms()})\")\n",
|
||
" \n",
|
||
" print(f\"\\n断裂结果:\")\n",
|
||
" print(f\" 总碎片数: {len(mol_fragments.fragments)}\")\n",
|
||
" print(f\" 断裂位置: {sorted(mol_fragments.get_cleavage_positions())}\")\n",
|
||
" \n",
|
||
" # 显示每个碎片的信息\n",
|
||
" if len(mol_fragments.fragments) > 0:\n",
|
||
" print(f\"\\n碎片详情:\")\n",
|
||
" for frag in mol_fragments.fragments:\n",
|
||
" print(f\" - 位置 {frag.cleavage_position}: {frag.fragment_smiles}\")\n",
|
||
" print(f\" 原子数: {frag.atom_count}, 分子量: {frag.molecular_weight}\")\n",
|
||
" \n",
|
||
" # 可视化所有碎片\n",
|
||
" print(f\"\\n碎片可视化:\")\n",
|
||
" for i, frag in enumerate(mol_fragments.fragments, 1):\n",
|
||
" frag_mol = Chem.MolFromSmiles(frag.fragment_smiles)\n",
|
||
" if frag_mol:\n",
|
||
" from rdkit.Chem import AllChem\n",
|
||
" AllChem.Compute2DCoords(frag_mol)\n",
|
||
" img = Draw.MolToImage(frag_mol, size=(400, 400))\n",
|
||
" display(img)\n",
|
||
" print(f\"碎片 {i} (位置 {frag.cleavage_position}): {frag.fragment_smiles}\")\n",
|
||
" else:\n",
|
||
" print(\"\\n⚠️ 没有找到可断裂的侧链\")\n",
|
||
" print(\"可能原因:\")\n",
|
||
" print(\" 1. 该分子没有侧链\")\n",
|
||
" print(\" 2. 所有侧链都在位置1和2（酯基位置）\")\n",
|
||
" \n",
|
||
" # 保存碎片数据\n",
|
||
" print(f\"\\n{'='*80}\")\n",
|
||
" print(f\"碎片数据已保存在变量 'mol_fragments' 中\")\n",
|
||
" print(f\"可以使用以下代码保存到文件:\")\n",
|
||
" print(f\" mol_fragments.to_json_file('output/fragments/{selected_parent_id}_fragments.json')\")\n",
|
||
"else:\n",
|
||
" print(\"请先运行前面的cell来选择并可视化分子\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 5. 保存过滤后的数据\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"过滤后的分子数: 878\n",
|
||
"过滤掉的分子数: 1144\n",
|
||
"\n",
|
||
"✓ 完整结果（含过滤信息）已保存: /home/zly/project/macro_split/ring16/temp_with_filter_info.csv\n",
|
||
"✓ 过滤后的数据已保存: /home/zly/project/macro_split/ring16/temp_filtered.csv\n",
|
||
"\n",
|
||
"过滤后的数据前5行:\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>unique_id</th>\n",
|
||
" <th>IDs</th>\n",
|
||
" <th>molecule_pref_name</th>\n",
|
||
" <th>max_pChEMBL</th>\n",
|
||
" <th>max_pChEMBL_target</th>\n",
|
||
" <th># Target Organisms</th>\n",
|
||
" <th>Target Organisms</th>\n",
|
||
" <th># Known Targets</th>\n",
|
||
" <th>Known Targets</th>\n",
|
||
" <th>target_pref_name</th>\n",
|
||
" <th>smiles</th>\n",
|
||
" <th>has_bridge</th>\n",
|
||
" <th>has_multiple_lactones</th>\n",
|
||
" <th>has_amide</th>\n",
|
||
" <th>parse_failed</th>\n",
|
||
" <th>filter_reason</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>44</th>\n",
|
||
" <td>mol_00044</td>\n",
|
||
" <td>CHEMBL372255</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>8.05</td>\n",
|
||
" <td>CHEMBL614075,CHEMBL614075</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>Homo sapiens</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>CHEMBL614075, CHEMBL614509, CHEMBL613858</td>\n",
|
||
" <td>1A9, 1A9/ptx-10, 1A9/ptx-22</td>\n",
|
||
" <td>CSc1nc(/C=C(\\C)[C@@H]2C/C=C\\CCC[C@H](C)[C@H](O...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td></td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>54</th>\n",
|
||
" <td>mol_00054</td>\n",
|
||
" <td>CHEMBL1515216</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>5.85</td>\n",
|
||
" <td>CHEMBL1947</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>Mus musculus, Homo sapiens</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>CHEMBL1075138, CHEMBL2524, CHEMBL1293231, CHEM...</td>\n",
|
||
" <td>Nuclear receptor ROR-gamma, Alpha-galactosidas...</td>\n",
|
||
" <td>CO/C1=C\\C(C)=C\\[C@@H](C)[C@@H](O)[C@@H](C)C/C(...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td></td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>74</th>\n",
|
||
" <td>mol_00074</td>\n",
|
||
" <td>CHEMBL2106399</td>\n",
|
||
" <td>KITASAMYCIN</td>\n",
|
||
" <td>6.20</td>\n",
|
||
" <td>CHEMBL319</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>None, Rattus norvegicus</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>CHEMBL612545, CHEMBL319</td>\n",
|
||
" <td>Alpha-1a adrenergic receptor, Unchecked</td>\n",
|
||
" <td>CO[C@@H]1[C@@H](O[C@@H]2O[C@H](C)[C@@H](O[C@H]...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td></td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>80</th>\n",
|
||
" <td>mol_00080</td>\n",
|
||
" <td>CHEMBL2106399</td>\n",
|
||
" <td>KITASAMYCIN</td>\n",
|
||
" <td>6.20</td>\n",
|
||
" <td>CHEMBL319</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>None, Rattus norvegicus</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>CHEMBL612545, CHEMBL319</td>\n",
|
||
" <td>Alpha-1a adrenergic receptor, Unchecked</td>\n",
|
||
" <td>CO[C@@H]1[C@@H](O[C@@H]2O[C@H](C)[C@@H](O[C@H]...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td></td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>83</th>\n",
|
||
" <td>mol_00083</td>\n",
|
||
" <td>CHEMBL1326015</td>\n",
|
||
" <td>NaN</td>\n",
|
||
" <td>5.40</td>\n",
|
||
" <td>CHEMBL1293227</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>Homo sapiens</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>CHEMBL1293227, CHEMBL340</td>\n",
|
||
" <td>Cytochrome P450 3A4, Ubiquitin carboxyl-termin...</td>\n",
|
||
" <td>CO[C@@H]1[C@H](OC(C)=O)CC(=O)O[C@H](C)C/C=C\\C=...</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td>False</td>\n",
|
||
" <td></td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" unique_id IDs molecule_pref_name max_pChEMBL \\\n",
|
||
"44 mol_00044 CHEMBL372255 NaN 8.05 \n",
|
||
"54 mol_00054 CHEMBL1515216 NaN 5.85 \n",
|
||
"74 mol_00074 CHEMBL2106399 KITASAMYCIN 6.20 \n",
|
||
"80 mol_00080 CHEMBL2106399 KITASAMYCIN 6.20 \n",
|
||
"83 mol_00083 CHEMBL1326015 NaN 5.40 \n",
|
||
"\n",
|
||
" max_pChEMBL_target # Target Organisms Target Organisms \\\n",
|
||
"44 CHEMBL614075,CHEMBL614075 6 Homo sapiens \n",
|
||
"54 CHEMBL1947 5 Mus musculus, Homo sapiens \n",
|
||
"74 CHEMBL319 4 None, Rattus norvegicus \n",
|
||
"80 CHEMBL319 4 None, Rattus norvegicus \n",
|
||
"83 CHEMBL1293227 4 Homo sapiens \n",
|
||
"\n",
|
||
" # Known Targets Known Targets \\\n",
|
||
"44 6 CHEMBL614075, CHEMBL614509, CHEMBL613858 \n",
|
||
"54 5 CHEMBL1075138, CHEMBL2524, CHEMBL1293231, CHEM... \n",
|
||
"74 4 CHEMBL612545, CHEMBL319 \n",
|
||
"80 4 CHEMBL612545, CHEMBL319 \n",
|
||
"83 4 CHEMBL1293227, CHEMBL340 \n",
|
||
"\n",
|
||
" target_pref_name \\\n",
|
||
"44 1A9, 1A9/ptx-10, 1A9/ptx-22 \n",
|
||
"54 Nuclear receptor ROR-gamma, Alpha-galactosidas... \n",
|
||
"74 Alpha-1a adrenergic receptor, Unchecked \n",
|
||
"80 Alpha-1a adrenergic receptor, Unchecked \n",
|
||
"83 Cytochrome P450 3A4, Ubiquitin carboxyl-termin... \n",
|
||
"\n",
|
||
" smiles has_bridge \\\n",
|
||
"44 CSc1nc(/C=C(\\C)[C@@H]2C/C=C\\CCC[C@H](C)[C@H](O... False \n",
|
||
"54 CO/C1=C\\C(C)=C\\[C@@H](C)[C@@H](O)[C@@H](C)C/C(... False \n",
|
||
"74 CO[C@@H]1[C@@H](O[C@@H]2O[C@H](C)[C@@H](O[C@H]... False \n",
|
||
"80 CO[C@@H]1[C@@H](O[C@@H]2O[C@H](C)[C@@H](O[C@H]... False \n",
|
||
"83 CO[C@@H]1[C@H](OC(C)=O)CC(=O)O[C@H](C)C/C=C\\C=... False \n",
|
||
"\n",
|
||
" has_multiple_lactones has_amide parse_failed filter_reason \n",
|
||
"44 False False False \n",
|
||
"54 False False False \n",
|
||
"74 False False False \n",
|
||
"80 False False False \n",
|
||
"83 False False False "
|
||
]
|
||
},
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 创建过滤后的DataFrame（只包含通过过滤的分子）\n",
|
||
"df_filtered = df[df['filter_reason'] == ''].copy()\n",
|
||
"\n",
|
||
"print(f\"过滤后的分子数: {len(df_filtered)}\")\n",
|
||
"print(f\"过滤掉的分子数: {len(df) - len(df_filtered)}\")\n",
|
||
"\n",
|
||
"# 保存完整结果（包含过滤信息）\n",
|
||
"output_file_all = project_root / 'ring16' / 'temp_with_filter_info.csv'\n",
|
||
"df.to_csv(output_file_all, index=False)\n",
|
||
"print(f\"\\n✓ 完整结果（含过滤信息）已保存: {output_file_all}\")\n",
|
||
"\n",
|
||
"# 保存过滤后的数据\n",
|
||
"output_file_filtered = project_root / 'ring16' / 'temp_filtered.csv'\n",
|
||
"df_filtered.to_csv(output_file_filtered, index=False)\n",
|
||
"print(f\"✓ 过滤后的数据已保存: {output_file_filtered}\")\n",
|
||
"\n",
|
||
"# 显示过滤后的前几行\n",
|
||
"print(f\"\\n过滤后的数据前5行:\")\n",
|
||
"df_filtered.head()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 6. 可视化函数定义\n",
|
||
"\n",
|
||
"参考test_align_two_molecules.ipynb的可视化代码，定义用于显示环上原子编号的函数\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"可视化函数定义完成!\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def draw_mol_with_ring_numbering(mol, ring_atoms, ring_numbering, size=(800, 800), title=\"\"):\n",
|
||
" \"\"\"\n",
|
||
" 绘制分子，只显示环上原子的编号（SVG格式），编号以小字显示在原子旁边\n",
|
||
" 参考test_align_two_molecules.ipynb的可视化代码\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" mol: RDKit分子对象\n",
|
||
" ring_atoms: 环上原子的索引列表\n",
|
||
" ring_numbering: 字典，映射原子索引到环位置编号\n",
|
||
" size: 图像大小 (width, height)\n",
|
||
" title: 图像标题\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" SVG字符串\n",
|
||
" \"\"\"\n",
|
||
" drawer = rdMolDraw2D.MolDraw2DSVG(size[0], size[1])\n",
|
||
" \n",
|
||
" # 设置字体大小\n",
|
||
" drawer.SetFontSize(0.8) # 原子标签字体大小\n",
|
||
" \n",
|
||
" # 设置绘制选项\n",
|
||
" draw_options = drawer.drawOptions()\n",
|
||
" draw_options.addAtomIndices = False # 不显示所有原子编号\n",
|
||
" \n",
|
||
" # 高亮环上的原子\n",
|
||
" highlight_atoms = list(ring_atoms)\n",
|
||
" atom_colors = {}\n",
|
||
" for atom_idx in ring_atoms:\n",
|
||
" atom_colors[atom_idx] = (0.8, 0.9, 1.0) # 浅蓝色高亮\n",
|
||
" \n",
|
||
" # 创建分子的副本并设置环上原子的注释（编号以小字显示，不替换原子类型）\n",
|
||
" mol_copy = Chem.Mol(mol)\n",
|
||
" for atom_idx in ring_atoms:\n",
|
||
" if atom_idx in ring_numbering:\n",
|
||
" atom = mol_copy.GetAtomWithIdx(atom_idx)\n",
|
||
" # 使用atomNote属性来显示编号（以小字形式显示在原子旁边，不替换原子类型）\n",
|
||
" atom.SetProp(\"atomNote\", str(ring_numbering[atom_idx]))\n",
|
||
" \n",
|
||
" # 绘制分子\n",
|
||
" drawer.DrawMolecule(mol_copy,\n",
|
||
" highlightAtoms=highlight_atoms,\n",
|
||
" highlightAtomColors=atom_colors)\n",
|
||
" \n",
|
||
" drawer.FinishDrawing()\n",
|
||
" svg = drawer.GetDrawingText()\n",
|
||
" \n",
|
||
" return svg\n",
|
||
"\n",
|
||
"\n",
|
||
"print(\"可视化函数定义完成!\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 7. 可视化示例\n",
|
||
"\n",
|
||
"使用可视化函数查看过滤后的分子\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"示例分子:\n",
|
||
" Unique ID: mol_00044\n",
|
||
" Name: nan\n",
|
||
" SMILES: CSc1nc(/C=C(\\C)[C@@H]2C/C=C\\CCC[C@H](C)[C@H](O)[C@@H](C)C(=O)C(C)(C)[C@@H](O)CC(=O)O2)cs1\n",
|
||
"\n",
|
||
"环原子数: 16\n",
|
||
"编号数: 16\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[15:20:25] The new font size 0.8 is below the current minimum (6).\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/svg+xml": [
|
||
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" baseProfile=\"full\" xml:space=\"preserve\" width=\"800px\" height=\"800px\" viewBox=\"0 0 800 800\">\n",
|
||
"<!-- END OF HEADER -->\n",
|
||
"<rect style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"800.0\" height=\"800.0\" x=\"0.0\" y=\"0.0\"> </rect>\n",
|
||
"<path class=\"bond-8 atom-8 atom-9\" d=\"M 382.3,388.6 L 394.7,391.6 L 392.7,447.5 L 380.1,449.5 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-34 atom-8 atom-31\" d=\"M 407.6,333.2 L 417.9,340.7 L 394.7,391.6 L 382.3,388.6 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-9 atom-9 atom-10\" d=\"M 392.7,447.5 L 412.2,499.9 L 401.4,506.6 L 380.1,449.5 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-10 atom-10 atom-11\" d=\"M 412.2,499.9 L 450.3,540.9 L 442.9,551.2 L 401.4,506.6 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-11 atom-11 atom-12\" d=\"M 450.3,540.9 L 501.2,564.2 L 498.3,576.6 L 442.9,551.2 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"bond-12 atom-12 atom-13\" d=\"M 501.2,564.2 L 557.1,566.2 L 559.2,578.8 L 498.3,576.6 Z\" style=\"fill:#CCE5FF;fill-rule:evenodd;fill-opacity:1;stroke:#CCE5FF;stroke-width:0.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
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"<path class=\"note\" d=\"M 402.7 508.2 Q 403.5 508.5, 403.9 509.0 Q 404.3 509.5, 404.3 510.3 Q 404.3 511.0, 403.9 511.6 Q 403.6 512.1, 402.9 512.4 Q 402.3 512.7, 401.4 512.7 Q 400.6 512.7, 399.9 512.4 Q 399.2 512.1, 398.7 511.4 L 399.4 510.8 Q 399.9 511.3, 400.3 511.5 Q 400.8 511.8, 401.4 511.8 Q 402.2 511.8, 402.7 511.4 Q 403.1 511.0, 403.1 510.3 Q 403.1 509.5, 402.6 509.1 Q 402.2 508.7, 401.1 508.7 L 400.5 508.7 L 400.5 507.9 L 401.1 507.9 Q 402.0 507.8, 402.5 507.5 Q 402.9 507.1, 402.9 506.3 Q 402.9 505.8, 402.5 505.5 Q 402.1 505.1, 401.5 505.1 Q 400.8 505.1, 400.3 505.4 Q 399.9 505.6, 399.6 506.3 L 398.8 505.8 Q 399.1 505.1, 399.8 504.7 Q 400.5 504.2, 401.5 504.2 Q 402.7 504.2, 403.4 504.8 Q 404.1 505.4, 404.1 506.3 Q 404.1 507.0, 403.7 507.5 Q 403.4 507.9, 402.7 508.2 \" fill=\"#000000\"/>\n",
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"<path class=\"note\" d=\"M 408.2 318.2 Q 408.9 318.2, 409.5 318.5 Q 410.1 318.8, 410.4 319.4 Q 410.7 320.0, 410.7 320.7 Q 410.7 321.5, 410.3 322.1 Q 410.0 322.8, 409.3 323.1 Q 408.7 323.5, 407.9 323.5 Q 406.4 323.5, 405.7 322.5 Q 405.0 321.5, 405.0 319.5 Q 405.0 317.3, 405.9 316.1 Q 406.8 315.0, 408.6 315.0 Q 409.1 315.0, 409.5 315.1 Q 409.9 315.2, 410.4 315.5 L 409.9 316.3 Q 409.3 315.9, 408.6 315.9 Q 407.4 315.9, 406.8 316.7 Q 406.2 317.4, 406.1 319.0 Q 406.6 318.6, 407.1 318.4 Q 407.6 318.2, 408.2 318.2 M 407.9 322.5 Q 408.4 322.5, 408.7 322.3 Q 409.1 322.0, 409.3 321.6 Q 409.5 321.2, 409.5 320.7 Q 409.5 320.0, 409.1 319.6 Q 408.7 319.1, 408.0 319.1 Q 407.5 319.1, 407.0 319.3 Q 406.5 319.5, 406.1 319.9 Q 406.2 321.3, 406.6 321.9 Q 407.0 322.5, 407.9 322.5 \" fill=\"#000000\"/>\n",
|
||
"</svg>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.SVG object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 导入编号函数\n",
|
||
"from src.ring_numbering import assign_ring_numbering\n",
|
||
"\n",
|
||
"# 选择一个过滤后的分子进行可视化示例\n",
|
||
"if len(df_filtered) > 0:\n",
|
||
" # 选择第一个通过过滤的分子\n",
|
||
" sample_idx = df_filtered.index[0]\n",
|
||
" sample_row = df_filtered.iloc[0]\n",
|
||
" \n",
|
||
" print(f\"示例分子:\")\n",
|
||
" print(f\" Unique ID: {sample_row['unique_id']}\")\n",
|
||
" print(f\" Name: {sample_row.get('molecule_pref_name', 'N/A')}\")\n",
|
||
" print(f\" SMILES: {sample_row['smiles']}\")\n",
|
||
" \n",
|
||
" # 解析分子\n",
|
||
" mol = Chem.MolFromSmiles(sample_row['smiles'])\n",
|
||
" if mol is not None:\n",
|
||
" # 获取环原子和编号\n",
|
||
" ring_atoms = get_ring_atoms(mol)\n",
|
||
" ring_numbering = assign_ring_numbering(mol)\n",
|
||
" \n",
|
||
" if len(ring_atoms) > 0 and len(ring_numbering) > 0:\n",
|
||
" print(f\"\\n环原子数: {len(ring_atoms)}\")\n",
|
||
" print(f\"编号数: {len(ring_numbering)}\")\n",
|
||
" \n",
|
||
" # 可视化\n",
|
||
" svg = draw_mol_with_ring_numbering(\n",
|
||
" mol, \n",
|
||
" ring_atoms, \n",
|
||
" ring_numbering, \n",
|
||
" size=(800, 800), \n",
|
||
" title=sample_row['unique_id']\n",
|
||
" )\n",
|
||
" display(SVG(svg))\n",
|
||
" else:\n",
|
||
" print(\"无法获取环信息或编号\")\n",
|
||
" else:\n",
|
||
" print(\"无法解析SMILES\")\n",
|
||
"else:\n",
|
||
" print(\"没有通过过滤的分子可供可视化\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 8. 显示单个分子的函数\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"显示单个分子的函数定义完成!\n",
|
||
"\n",
|
||
"使用方法: display_molecule_by_index(df_filtered, 0) # 显示第0个分子\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def display_molecule_by_index(df_filtered, index):\n",
|
||
" \"\"\"\n",
|
||
" 根据索引显示指定的分子\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" df_filtered: 过滤后的DataFrame\n",
|
||
" index: 行索引（可以是整数索引或DataFrame的index值）\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" None (在Jupyter中显示分子)\n",
|
||
" \"\"\"\n",
|
||
" if len(df_filtered) == 0:\n",
|
||
" print(\"没有可显示的分子\")\n",
|
||
" return\n",
|
||
" \n",
|
||
" # 获取指定行的数据\n",
|
||
" if isinstance(index, int):\n",
|
||
" # 如果是整数，使用iloc\n",
|
||
" if index < 0 or index >= len(df_filtered):\n",
|
||
" print(f\"索引 {index} 超出范围 (0-{len(df_filtered)-1})\")\n",
|
||
" return\n",
|
||
" sample_row = df_filtered.iloc[index]\n",
|
||
" else:\n",
|
||
" # 如果是DataFrame的index值，使用loc\n",
|
||
" if index not in df_filtered.index:\n",
|
||
" print(f\"索引 {index} 不存在\")\n",
|
||
" return\n",
|
||
" sample_row = df_filtered.loc[index]\n",
|
||
" \n",
|
||
" print(f\"分子信息:\")\n",
|
||
" print(f\" Unique ID: {sample_row['unique_id']}\")\n",
|
||
" print(f\" Name: {sample_row.get('molecule_pref_name', 'N/A')}\")\n",
|
||
" print(f\" SMILES: {sample_row['smiles']}\")\n",
|
||
" \n",
|
||
" # 解析分子\n",
|
||
" mol = Chem.MolFromSmiles(sample_row['smiles'])\n",
|
||
" if mol is None:\n",
|
||
" print(\"无法解析SMILES\")\n",
|
||
" return\n",
|
||
" \n",
|
||
" # 获取环原子和编号\n",
|
||
" ring_atoms = get_ring_atoms(mol)\n",
|
||
" ring_numbering = assign_ring_numbering(mol)\n",
|
||
" \n",
|
||
" if len(ring_atoms) == 0 or len(ring_numbering) == 0:\n",
|
||
" print(\"无法获取环信息或编号\")\n",
|
||
" return\n",
|
||
" \n",
|
||
" print(f\"\\n环原子数: {len(ring_atoms)}\")\n",
|
||
" print(f\"编号数: {len(ring_numbering)}\")\n",
|
||
" \n",
|
||
" # 可视化\n",
|
||
" svg = draw_mol_with_ring_numbering(\n",
|
||
" mol, \n",
|
||
" ring_atoms, \n",
|
||
" ring_numbering, \n",
|
||
" size=(800, 800), \n",
|
||
" title=sample_row['unique_id']\n",
|
||
" )\n",
|
||
" display(SVG(svg))\n",
|
||
"\n",
|
||
"\n",
|
||
"print(\"显示单个分子的函数定义完成!\")\n",
|
||
"print(f\"\\n使用方法: display_molecule_by_index(df_filtered, 0) # 显示第0个分子\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 9. 批量保存所有分子的PNG图片\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"批量保存函数定义完成!\n",
|
||
"\n",
|
||
"使用方法: save_all_molecules_as_png(df_filtered)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def save_all_molecules_as_png(df_filtered, output_dir=None, dpi=600, size=(2000, 2000)):\n",
|
||
" \"\"\"\n",
|
||
" 将df_filtered中所有分子的图片保存为PNG格式\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" df_filtered: 过滤后的DataFrame\n",
|
||
" output_dir: 输出目录路径，如果为None则使用 project_root / 'output' / 'filtered_molecules_png'\n",
|
||
" dpi: 图片分辨率，默认600\n",
|
||
" size: 图片大小 (width, height)，默认(2000, 2000)\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" 保存成功的分子数量\n",
|
||
" \"\"\"\n",
|
||
" from rdkit.Chem.Draw import rdMolDraw2D\n",
|
||
" from rdkit.Chem import AllChem\n",
|
||
" from rdkit import Geometry\n",
|
||
" \n",
|
||
" # 设置输出目录\n",
|
||
" if output_dir is None:\n",
|
||
" output_dir = project_root / 'output' / 'filtered_molecules_png'\n",
|
||
" else:\n",
|
||
" output_dir = Path(output_dir)\n",
|
||
" \n",
|
||
" # 创建输出目录\n",
|
||
" output_dir.mkdir(parents=True, exist_ok=True)\n",
|
||
" \n",
|
||
" print(f\"开始保存分子图片...\")\n",
|
||
" print(f\"输出目录: {output_dir}\")\n",
|
||
" print(f\"DPI: {dpi}\")\n",
|
||
" print(f\"图片大小: {size}\")\n",
|
||
" print()\n",
|
||
" \n",
|
||
" success_count = 0\n",
|
||
" failed_count = 0\n",
|
||
" \n",
|
||
" # 遍历所有分子\n",
|
||
" for idx, row in df_filtered.iterrows():\n",
|
||
" unique_id = row['unique_id']\n",
|
||
" smiles = row['smiles']\n",
|
||
" \n",
|
||
" try:\n",
|
||
" # 解析分子\n",
|
||
" mol = Chem.MolFromSmiles(smiles)\n",
|
||
" if mol is None:\n",
|
||
" print(f\" ✗ {unique_id}: SMILES解析失败\")\n",
|
||
" failed_count += 1\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 确保有2D坐标\n",
|
||
" AllChem.Compute2DCoords(mol)\n",
|
||
" \n",
|
||
" # 获取环原子和编号（添加错误处理）\n",
|
||
" try:\n",
|
||
" ring_atoms = get_ring_atoms(mol)\n",
|
||
" ring_numbering = assign_ring_numbering(mol)\n",
|
||
" except Exception as ring_error:\n",
|
||
" print(f\" ✗ {unique_id}: 环信息获取失败 - {str(ring_error)}\")\n",
|
||
" failed_count += 1\n",
|
||
" continue\n",
|
||
" \n",
|
||
" if len(ring_atoms) == 0 or len(ring_numbering) == 0:\n",
|
||
" print(f\" ✗ {unique_id}: 无法获取环信息或编号\")\n",
|
||
" failed_count += 1\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 创建PNG绘制器\n",
|
||
" # 如果使用默认DPI（72），直接使用size参数\n",
|
||
" # 如果使用高DPI，通过调整图片大小来实现\n",
|
||
" if dpi == 72:\n",
|
||
" # 默认DPI，直接使用指定的大小\n",
|
||
" actual_width = size[0]\n",
|
||
" actual_height = size[1]\n",
|
||
" scale_factor = 1.0\n",
|
||
" else:\n",
|
||
" # 高DPI，计算scale factor: dpi/72 (72是默认DPI)\n",
|
||
" scale_factor = dpi / 72.0\n",
|
||
" actual_width = int(size[0] * scale_factor)\n",
|
||
" actual_height = int(size[1] * scale_factor)\n",
|
||
" \n",
|
||
" drawer = rdMolDraw2D.MolDraw2DCairo(actual_width, actual_height)\n",
|
||
" \n",
|
||
" # 设置字体大小（避免太小或太大导致警告）\n",
|
||
" # 根据scale_factor调整字体大小，但限制在合理范围内\n",
|
||
" # RDKit字体大小范围通常是6-40\n",
|
||
" base_font_size = 12.0\n",
|
||
" font_size = max(6.0, min(40.0, base_font_size * scale_factor))\n",
|
||
" drawer.SetFontSize(font_size)\n",
|
||
" \n",
|
||
" # 设置绘制选项\n",
|
||
" draw_options = drawer.drawOptions()\n",
|
||
" draw_options.addAtomIndices = False\n",
|
||
" \n",
|
||
" # 计算分子的边界框用于设置scale\n",
|
||
" # 获取分子的2D坐标范围\n",
|
||
" conf = mol.GetConformer()\n",
|
||
" if conf.GetNumAtoms() > 0:\n",
|
||
" coords = [conf.GetAtomPosition(i) for i in range(conf.GetNumAtoms())]\n",
|
||
" xs = [c.x for c in coords]\n",
|
||
" ys = [c.y for c in coords]\n",
|
||
" min_x, max_x = min(xs), max(xs)\n",
|
||
" min_y, max_y = min(ys), max(ys)\n",
|
||
" \n",
|
||
" # 添加一些边距\n",
|
||
" margin = 0.2\n",
|
||
" min_x -= margin\n",
|
||
" max_x += margin\n",
|
||
" min_y -= margin\n",
|
||
" max_y += margin\n",
|
||
" \n",
|
||
" # 设置scale（使用正确的参数格式）\n",
|
||
" minv = Geometry.Point2D(min_x, min_y)\n",
|
||
" maxv = Geometry.Point2D(max_x, max_y)\n",
|
||
" drawer.SetScale(actual_width, actual_height, minv, maxv)\n",
|
||
" \n",
|
||
" # 高亮环上的原子\n",
|
||
" highlight_atoms = list(ring_atoms)\n",
|
||
" atom_colors = {}\n",
|
||
" for atom_idx in ring_atoms:\n",
|
||
" atom_colors[atom_idx] = (0.8, 0.9, 1.0) # 浅蓝色高亮\n",
|
||
" \n",
|
||
" # 创建分子的副本并设置环上原子的注释\n",
|
||
" mol_copy = Chem.Mol(mol)\n",
|
||
" for atom_idx in ring_atoms:\n",
|
||
" if atom_idx in ring_numbering:\n",
|
||
" atom = mol_copy.GetAtomWithIdx(atom_idx)\n",
|
||
" atom.SetProp(\"atomNote\", str(ring_numbering[atom_idx]))\n",
|
||
" \n",
|
||
" # 绘制分子\n",
|
||
" drawer.DrawMolecule(mol_copy,\n",
|
||
" highlightAtoms=highlight_atoms,\n",
|
||
" highlightAtomColors=atom_colors)\n",
|
||
" \n",
|
||
" drawer.FinishDrawing()\n",
|
||
" \n",
|
||
" # 保存PNG\n",
|
||
" output_file = output_dir / f\"{unique_id}.png\"\n",
|
||
" drawer.WriteDrawingText(str(output_file))\n",
|
||
" \n",
|
||
" success_count += 1\n",
|
||
" if (success_count + failed_count) % 100 == 0:\n",
|
||
" print(f\" 进度: {success_count + failed_count}/{len(df_filtered)} (成功: {success_count}, 失败: {failed_count})\")\n",
|
||
" \n",
|
||
" except Exception as e:\n",
|
||
" print(f\" ✗ {unique_id}: 处理失败 - {str(e)}\")\n",
|
||
" failed_count += 1\n",
|
||
" \n",
|
||
" print()\n",
|
||
" print(\"=\"*60)\n",
|
||
" print(\"保存完成!\")\n",
|
||
" print(\"=\"*60)\n",
|
||
" print(f\"总分子数: {len(df_filtered)}\")\n",
|
||
" print(f\"成功保存: {success_count}\")\n",
|
||
" print(f\"失败: {failed_count}\")\n",
|
||
" print(f\"输出目录: {output_dir}\")\n",
|
||
" print(\"=\"*60)\n",
|
||
" \n",
|
||
" return success_count\n",
|
||
"\n",
|
||
"\n",
|
||
"print(\"批量保存函数定义完成!\")\n",
|
||
"print(f\"\\n使用方法: save_all_molecules_as_png(df_filtered)\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 10. 执行批量保存\n",
|
||
"\n",
|
||
"运行下面的代码来保存所有过滤后的分子图片\n",
|
||
"\n",
|
||
"**使用默认DPI（72）：**\n",
|
||
"```python\n",
|
||
"save_all_molecules_as_png(df_filtered, dpi=72) # 或直接省略dpi参数，默认就是72\n",
|
||
"```\n",
|
||
"\n",
|
||
"**使用高DPI（600）：**\n",
|
||
"```python\n",
|
||
"save_all_molecules_as_png(df_filtered, dpi=600)\n",
|
||
"```\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"开始保存分子图片...\n",
|
||
"输出目录: /home/zly/project/macro_split/output/filtered_molecules_png\n",
|
||
"DPI: 72\n",
|
||
"图片大小: (2000, 2000)\n",
|
||
"\n",
|
||
" 进度: 100/878 (成功: 100, 失败: 0)\n",
|
||
" 进度: 200/878 (成功: 200, 失败: 0)\n",
|
||
" 进度: 300/878 (成功: 300, 失败: 0)\n",
|
||
" 进度: 400/878 (成功: 400, 失败: 0)\n",
|
||
" 进度: 500/878 (成功: 500, 失败: 0)\n",
|
||
" 进度: 600/878 (成功: 600, 失败: 0)\n",
|
||
" 进度: 700/878 (成功: 700, 失败: 0)\n",
|
||
" 进度: 800/878 (成功: 800, 失败: 0)\n",
|
||
"\n",
|
||
"============================================================\n",
|
||
"保存完成!\n",
|
||
"============================================================\n",
|
||
"总分子数: 878\n",
|
||
"成功保存: 878\n",
|
||
"失败: 0\n",
|
||
"输出目录: /home/zly/project/macro_split/output/filtered_molecules_png\n",
|
||
"============================================================\n",
|
||
"\n",
|
||
"✓ 已成功保存 878 个分子的PNG图片（默认DPI=72）\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 执行批量保存\n",
|
||
"# 注意：这可能需要一些时间，取决于分子数量\n",
|
||
"\n",
|
||
"# 方式1：使用默认DPI（72）- 更快，文件更小\n",
|
||
"if 'df_filtered' in locals() and len(df_filtered) > 0:\n",
|
||
" success_count = save_all_molecules_as_png(df_filtered, dpi=72)\n",
|
||
" print(f\"\\n✓ 已成功保存 {success_count} 个分子的PNG图片（默认DPI=72）\")\n",
|
||
"else:\n",
|
||
" print(\"请先运行前面的cell来生成df_filtered\")\n",
|
||
"\n",
|
||
"# 方式2：使用高DPI（600）- 更清晰，但文件更大，处理更慢\n",
|
||
"# 取消下面的注释来使用高DPI\n",
|
||
"# if 'df_filtered' in locals() and len(df_filtered) > 0:\n",
|
||
"# success_count = save_all_molecules_as_png(df_filtered, dpi=600)\n",
|
||
"# print(f\"\\n✓ 已成功保存 {success_count} 个分子的PNG图片（高DPI=600）\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 11. 分析失败的分子\n",
|
||
"\n",
|
||
"提取失败的分子并创建可视化分析函数来诊断失败原因\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"正在识别失败的分子...\n",
|
||
"\n",
|
||
"失败的分子数量: 0\n",
|
||
"\n",
|
||
"错误类型统计:\n",
|
||
"没有失败的分子\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 提取失败的分子\n",
|
||
"# 通过尝试保存过程来识别哪些分子失败了\n",
|
||
"\n",
|
||
"failed_molecules = []\n",
|
||
"\n",
|
||
"print(\"正在识别失败的分子...\")\n",
|
||
"print()\n",
|
||
"\n",
|
||
"for idx, row in df_filtered.iterrows():\n",
|
||
" unique_id = row['unique_id']\n",
|
||
" smiles = row['smiles']\n",
|
||
" \n",
|
||
" try:\n",
|
||
" # 解析分子\n",
|
||
" mol = Chem.MolFromSmiles(smiles)\n",
|
||
" if mol is None:\n",
|
||
" failed_molecules.append({\n",
|
||
" 'unique_id': unique_id,\n",
|
||
" 'smiles': smiles,\n",
|
||
" 'error': 'SMILES解析失败',\n",
|
||
" 'index': idx\n",
|
||
" })\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 尝试获取环信息\n",
|
||
" try:\n",
|
||
" ring_atoms = get_ring_atoms(mol)\n",
|
||
" ring_numbering = assign_ring_numbering(mol)\n",
|
||
" \n",
|
||
" if len(ring_atoms) == 0 or len(ring_numbering) == 0:\n",
|
||
" failed_molecules.append({\n",
|
||
" 'unique_id': unique_id,\n",
|
||
" 'smiles': smiles,\n",
|
||
" 'error': '无法获取环信息或编号',\n",
|
||
" 'ring_atoms_count': len(ring_atoms),\n",
|
||
" 'ring_numbering_count': len(ring_numbering),\n",
|
||
" 'index': idx\n",
|
||
" })\n",
|
||
" except Exception as e:\n",
|
||
" error_msg = str(e)\n",
|
||
" failed_molecules.append({\n",
|
||
" 'unique_id': unique_id,\n",
|
||
" 'smiles': smiles,\n",
|
||
" 'error': error_msg,\n",
|
||
" 'index': idx\n",
|
||
" })\n",
|
||
" except Exception as e:\n",
|
||
" failed_molecules.append({\n",
|
||
" 'unique_id': unique_id,\n",
|
||
" 'smiles': smiles,\n",
|
||
" 'error': f'处理失败: {str(e)}',\n",
|
||
" 'index': idx\n",
|
||
" })\n",
|
||
"\n",
|
||
"# 创建失败的分子DataFrame\n",
|
||
"df_failed = pd.DataFrame(failed_molecules)\n",
|
||
"\n",
|
||
"print(f\"失败的分子数量: {len(df_failed)}\")\n",
|
||
"print(f\"\\n错误类型统计:\")\n",
|
||
"if len(df_failed) > 0:\n",
|
||
" error_counts = df_failed['error'].value_counts()\n",
|
||
" print(error_counts)\n",
|
||
" \n",
|
||
" # 保存失败的分子到CSV\n",
|
||
" failed_output_file = project_root / 'ring16' / 'temp_failed_molecules.csv'\n",
|
||
" df_failed.to_csv(failed_output_file, index=False)\n",
|
||
" print(f\"\\n✓ 失败的分子已保存到: {failed_output_file}\")\n",
|
||
" \n",
|
||
" print(f\"\\n前5个失败的分子:\")\n",
|
||
" df_failed.head()\n",
|
||
"else:\n",
|
||
" print(\"没有失败的分子\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 12. 失败分子可视化分析函数\n",
|
||
"\n",
|
||
"创建一个函数来可视化失败的分子并诊断问题\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"失败分子分析函数定义完成!\n",
|
||
"\n",
|
||
"使用方法: analyze_failed_molecule(df_failed, index=0)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def analyze_failed_molecule(df_failed, index=0, show_diagnosis=True):\n",
|
||
" \"\"\"\n",
|
||
" 分析失败的分子，可视化并诊断失败原因\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" df_failed: 失败的分子DataFrame\n",
|
||
" index: 要分析的分子索引（默认0）\n",
|
||
" show_diagnosis: 是否显示诊断信息（默认True）\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" None (在Jupyter中显示分子和诊断信息)\n",
|
||
" \"\"\"\n",
|
||
" if len(df_failed) == 0:\n",
|
||
" print(\"没有失败的分子可供分析\")\n",
|
||
" return\n",
|
||
" \n",
|
||
" if index < 0 or index >= len(df_failed):\n",
|
||
" print(f\"索引 {index} 超出范围 (0-{len(df_failed)-1})\")\n",
|
||
" return\n",
|
||
" \n",
|
||
" # 获取失败的分子信息\n",
|
||
" failed_row = df_failed.iloc[index]\n",
|
||
" unique_id = failed_row['unique_id']\n",
|
||
" smiles = failed_row['smiles']\n",
|
||
" error = failed_row['error']\n",
|
||
" \n",
|
||
" print(\"=\"*80)\n",
|
||
" print(f\"分析失败的分子 #{index + 1}/{len(df_failed)}\")\n",
|
||
" print(\"=\"*80)\n",
|
||
" print(f\"Unique ID: {unique_id}\")\n",
|
||
" print(f\"Error: {error}\")\n",
|
||
" print(f\"SMILES: {smiles}\")\n",
|
||
" print()\n",
|
||
" \n",
|
||
" # 解析分子\n",
|
||
" mol = Chem.MolFromSmiles(smiles)\n",
|
||
" if mol is None:\n",
|
||
" print(\"❌ 无法解析SMILES\")\n",
|
||
" return\n",
|
||
" \n",
|
||
" # 显示基本分子信息\n",
|
||
" print(\"分子基本信息:\")\n",
|
||
" print(f\" 原子数: {mol.GetNumAtoms()}\")\n",
|
||
" print(f\" 键数: {mol.GetNumBonds()}\")\n",
|
||
" print()\n",
|
||
" \n",
|
||
" # 诊断步骤\n",
|
||
" if show_diagnosis:\n",
|
||
" print(\"诊断步骤:\")\n",
|
||
" print(\"-\" * 80)\n",
|
||
" \n",
|
||
" # 步骤1: 检查是否有环\n",
|
||
" ring_info = mol.GetRingInfo()\n",
|
||
" num_rings = ring_info.NumRings()\n",
|
||
" print(f\"1. 环的数量: {num_rings}\")\n",
|
||
" \n",
|
||
" if num_rings > 0:\n",
|
||
" ring_sizes = [len(ring) for ring in ring_info.AtomRings()]\n",
|
||
" print(f\" 环的大小: {ring_sizes}\")\n",
|
||
" \n",
|
||
" # 检查是否有16元环\n",
|
||
" has_16_ring = 16 in ring_sizes\n",
|
||
" print(f\" 是否有16元环: {has_16_ring}\")\n",
|
||
" \n",
|
||
" if has_16_ring:\n",
|
||
" # 步骤2: 尝试获取16元环原子\n",
|
||
" try:\n",
|
||
" ring_atoms = get_ring_atoms(mol)\n",
|
||
" print(f\"2. 16元环原子数: {len(ring_atoms)}\")\n",
|
||
" print(f\" 16元环原子索引: {ring_atoms}\")\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"2. ❌ 获取16元环原子失败: {str(e)}\")\n",
|
||
" ring_atoms = []\n",
|
||
" \n",
|
||
" if len(ring_atoms) > 0:\n",
|
||
" # 步骤3: 查找内酯碳\n",
|
||
" try:\n",
|
||
" from src.ring_numbering import find_lactone_carbon\n",
|
||
" lactone_c = find_lactone_carbon(mol)\n",
|
||
" print(f\"3. 内酯碳索引: {lactone_c}\")\n",
|
||
" \n",
|
||
" if lactone_c is not None:\n",
|
||
" print(f\" 内酯碳是否在16元环中: {lactone_c in ring_atoms}\")\n",
|
||
" \n",
|
||
" if lactone_c not in ring_atoms:\n",
|
||
" print(f\" ⚠️ 问题: 内酯碳({lactone_c})不在16元环原子列表中!\")\n",
|
||
" print(f\" 这可能是导致'Start atom is not in the ring'错误的原因\")\n",
|
||
" \n",
|
||
" # 检查内酯碳在哪个环中\n",
|
||
" lactone_rings = []\n",
|
||
" for i, ring in enumerate(ring_info.AtomRings()):\n",
|
||
" if lactone_c in ring:\n",
|
||
" lactone_rings.append((i, len(ring)))\n",
|
||
" print(f\" 内酯碳所在的环: {lactone_rings}\")\n",
|
||
" else:\n",
|
||
" print(f\" ⚠️ 问题: 未找到内酯碳!\")\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"3. ❌ 查找内酯碳失败: {str(e)}\")\n",
|
||
" \n",
|
||
" # 步骤4: 尝试分配编号\n",
|
||
" try:\n",
|
||
" ring_numbering = assign_ring_numbering(mol)\n",
|
||
" print(f\"4. 环编号数量: {len(ring_numbering)}\")\n",
|
||
" if len(ring_numbering) > 0:\n",
|
||
" print(f\" 环编号: {ring_numbering}\")\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"4. ❌ 分配环编号失败: {str(e)}\")\n",
|
||
" print(f\" 这是导致保存失败的错误!\")\n",
|
||
" else:\n",
|
||
" print(\" ⚠️ 问题: 分子中没有16元环!\")\n",
|
||
" else:\n",
|
||
" print(\" ⚠️ 问题: 分子中没有任何环!\")\n",
|
||
" \n",
|
||
" print(\"-\" * 80)\n",
|
||
" print()\n",
|
||
" \n",
|
||
" # 可视化分子\n",
|
||
" print(\"分子可视化:\")\n",
|
||
" try:\n",
|
||
" # 尝试高亮16元环原子（如果找到）\n",
|
||
" try:\n",
|
||
" ring_atoms = get_ring_atoms(mol)\n",
|
||
" highlight_atoms = list(ring_atoms) if len(ring_atoms) > 0 else []\n",
|
||
" \n",
|
||
" # 尝试高亮内酯碳\n",
|
||
" try:\n",
|
||
" from src.ring_numbering import find_lactone_carbon\n",
|
||
" lactone_c = find_lactone_carbon(mol)\n",
|
||
" if lactone_c is not None:\n",
|
||
" highlight_atoms.append(lactone_c)\n",
|
||
" except:\n",
|
||
" pass\n",
|
||
" \n",
|
||
" # 设置高亮颜色\n",
|
||
" atom_colors = {}\n",
|
||
" for atom_idx in highlight_atoms:\n",
|
||
" atom_colors[atom_idx] = (0.8, 0.9, 1.0) # 浅蓝色\n",
|
||
" \n",
|
||
" # 如果有内酯碳，用不同颜色\n",
|
||
" try:\n",
|
||
" from src.ring_numbering import find_lactone_carbon\n",
|
||
" lactone_c = find_lactone_carbon(mol)\n",
|
||
" if lactone_c is not None:\n",
|
||
" atom_colors[lactone_c] = (1.0, 0.8, 0.8) # 浅红色\n",
|
||
" except:\n",
|
||
" pass\n",
|
||
" \n",
|
||
" drawer = rdMolDraw2D.MolDraw2DSVG(800, 800)\n",
|
||
" drawer.SetFontSize(0.8)\n",
|
||
" draw_options = drawer.drawOptions()\n",
|
||
" draw_options.addAtomIndices = True # 显示原子索引\n",
|
||
" \n",
|
||
" drawer.DrawMolecule(mol, \n",
|
||
" highlightAtoms=highlight_atoms if highlight_atoms else None,\n",
|
||
" highlightAtomColors=atom_colors if atom_colors else None)\n",
|
||
" drawer.FinishDrawing()\n",
|
||
" svg = drawer.GetDrawingText()\n",
|
||
" display(SVG(svg))\n",
|
||
" except Exception as e:\n",
|
||
" # 如果高亮失败，只显示基本分子\n",
|
||
" img = Draw.MolToImage(mol, size=(800, 800))\n",
|
||
" display(img)\n",
|
||
" print(f\"注意: 高亮显示失败，使用基本可视化: {str(e)}\")\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"❌ 可视化失败: {str(e)}\")\n",
|
||
" \n",
|
||
" print(\"=\"*80)\n",
|
||
"\n",
|
||
"\n",
|
||
"print(\"失败分子分析函数定义完成!\")\n",
|
||
"print(f\"\\n使用方法: analyze_failed_molecule(df_failed, index=0)\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 13. 执行失败分子分析\n",
|
||
"\n",
|
||
"选择一个失败的分子进行分析，查看失败原因\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"请先运行前面的单元格来生成df_failed\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 分析第一个失败的分子\n",
|
||
"if 'df_failed' in locals() and len(df_failed) > 0:\n",
|
||
" print(f\"共有 {len(df_failed)} 个失败的分子\")\n",
|
||
" print(f\"\\n开始分析第一个失败的分子...\\n\")\n",
|
||
" analyze_failed_molecule(df_failed, index=0, show_diagnosis=True)\n",
|
||
" \n",
|
||
" print(f\"\\n提示: 可以使用以下命令分析其他失败的分子:\")\n",
|
||
" print(f\" analyze_failed_molecule(df_failed, index=1) # 分析第2个\")\n",
|
||
" print(f\" analyze_failed_molecule(df_failed, index=5) # 分析第6个\")\n",
|
||
" print(f\" # 等等...\")\n",
|
||
"else:\n",
|
||
" print(\"请先运行前面的单元格来生成df_failed\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 14. 测试修复后的编号函数\n",
|
||
"\n",
|
||
"重新加载修复后的`find_lactone_carbon`函数，测试失败的分子是否能正确识别环上的内酯碳\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"✓ 已重新加载修复后的ring_numbering模块\n",
|
||
"请先运行前面的单元格来生成df_failed\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 重新导入修复后的函数（需要重新加载模块）\n",
|
||
"import importlib\n",
|
||
"import sys\n",
|
||
"\n",
|
||
"# 如果模块已经在sys.modules中，需要重新加载\n",
|
||
"if 'src.ring_numbering' in sys.modules:\n",
|
||
" importlib.reload(sys.modules['src.ring_numbering'])\n",
|
||
"\n",
|
||
"from src.ring_numbering import find_lactone_carbon, get_ring_atoms, assign_ring_numbering\n",
|
||
"\n",
|
||
"print(\"✓ 已重新加载修复后的ring_numbering模块\")\n",
|
||
"\n",
|
||
"# 测试失败的分子\n",
|
||
"if 'df_failed' in locals() and len(df_failed) > 0:\n",
|
||
" test_smiles = df_failed.iloc[0]['smiles']\n",
|
||
" print(f\"\\n测试分子: {df_failed.iloc[0]['unique_id']}\")\n",
|
||
" print(f\"SMILES: {test_smiles}\\n\")\n",
|
||
" \n",
|
||
" mol = Chem.MolFromSmiles(test_smiles)\n",
|
||
" if mol is not None:\n",
|
||
" # 获取16元环原子\n",
|
||
" ring_atoms = get_ring_atoms(mol)\n",
|
||
" print(f\"16元环原子数: {len(ring_atoms)}\")\n",
|
||
" print(f\"16元环原子索引: {sorted(ring_atoms)}\\n\")\n",
|
||
" \n",
|
||
" # 使用修复后的函数查找内酯碳\n",
|
||
" lactone_c = find_lactone_carbon(mol)\n",
|
||
" print(f\"修复后的内酯碳索引: {lactone_c}\")\n",
|
||
" \n",
|
||
" if lactone_c is not None:\n",
|
||
" print(f\"内酯碳是否在16元环中: {lactone_c in ring_atoms}\")\n",
|
||
" \n",
|
||
" if lactone_c in ring_atoms:\n",
|
||
" print(\"✓ 成功！内酯碳现在在16元环中\")\n",
|
||
" \n",
|
||
" # 尝试分配编号\n",
|
||
" try:\n",
|
||
" ring_numbering = assign_ring_numbering(mol)\n",
|
||
" if len(ring_numbering) > 0:\n",
|
||
" print(f\"\\n✓ 成功分配环编号！\")\n",
|
||
" print(f\"编号范围: 1-{len(ring_numbering)}\")\n",
|
||
" print(f\"内酯碳位置: {ring_numbering.get(lactone_c, 'N/A')}\")\n",
|
||
" \n",
|
||
" # 可视化\n",
|
||
" svg = draw_mol_with_ring_numbering(\n",
|
||
" mol, \n",
|
||
" ring_atoms, \n",
|
||
" ring_numbering, \n",
|
||
" size=(800, 800), \n",
|
||
" title=f\"{df_failed.iloc[0]['unique_id']} (修复后)\"\n",
|
||
" )\n",
|
||
" display(SVG(svg))\n",
|
||
" else:\n",
|
||
" print(\"❌ 编号分配失败（返回空字典）\")\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"❌ 编号分配失败: {str(e)}\")\n",
|
||
" else:\n",
|
||
" print(\"❌ 内酯碳仍然不在16元环中\")\n",
|
||
" else:\n",
|
||
" print(\"❌ 未找到内酯碳\")\n",
|
||
" else:\n",
|
||
" print(\"❌ 无法解析SMILES\")\n",
|
||
"else:\n",
|
||
" print(\"请先运行前面的单元格来生成df_failed\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 15. 测试 MacrolactoneFragmenter 类\n",
|
||
"\n",
|
||
"使用新封装的 `MacrolactoneFragmenter` 类来处理大环内酯。\n",
|
||
"\n",
|
||
"这个类提供了更高级的接口，支持：\n",
|
||
"- 自动编号和断裂\n",
|
||
"- 可视化（SVG + PNG保存）\n",
|
||
"- 批量处理CSV\n",
|
||
"- 数据导出（JSON、DataFrame）\n",
|
||
"- 预留数据库接口（SQLModel）\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"MacrolactoneFragmenter 已初始化\n",
|
||
" 环大小: 16\n",
|
||
" SMARTS模式: O=C1CCCCCCCC(=O)OCC/C=C/C=C/1...\n",
|
||
"\n",
|
||
"✓ 准备就绪！\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 导入新的MacrolactoneFragmenter类\n",
|
||
"from src.macrolactone_fragmenter import MacrolactoneFragmenter\n",
|
||
"\n",
|
||
"# 创建16元环大环内酯断裂器\n",
|
||
"fragmenter = MacrolactoneFragmenter(ring_size=16)\n",
|
||
"\n",
|
||
"print(f\"MacrolactoneFragmenter 已初始化\")\n",
|
||
"print(f\" 环大小: {fragmenter.ring_size}\")\n",
|
||
"print(f\" SMARTS模式: {fragmenter.smarts_pattern[:50]}...\")\n",
|
||
"print(f\"\\n✓ 准备就绪！\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 15.1. 单个分子处理示例\n",
|
||
"\n",
|
||
"使用 `process_molecule()` 方法一站式处理：编号 + 断裂所有侧链\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"================================================================================\n",
|
||
"处理分子: mol_00113\n",
|
||
"================================================================================\n",
|
||
"SMILES: CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O)C(N(C)C)C2O)C(CCN2CC(C)CC(C)C2)CC(C)C(=O)/C=C/C(C)=C\\[C@@H]1COC1OC(C)C(O)C(OC)C1OC\n",
|
||
"\n",
|
||
"\n",
|
||
"断裂结果:\n",
|
||
" 总碎片数: 9\n",
|
||
" 断裂位置: [4, 5, 6, 7, 9, 10, 13, 15, 16]\n",
|
||
"\n",
|
||
"前5个碎片:\n",
|
||
" 1. 位置 4: *O\n",
|
||
" 原子数: 2, 分子量: 17.01\n",
|
||
" 2. 位置 5: *C\n",
|
||
" 原子数: 2, 分子量: 15.04\n",
|
||
" 3. 位置 6: *OC1OC(C)C(O)C(N(C)C)C1O\n",
|
||
" 原子数: 14, 分子量: 190.22\n",
|
||
" 4. 位置 7: *CCN1CC(C)CC(C)C1\n",
|
||
" 原子数: 11, 分子量: 140.25\n",
|
||
" 5. 位置 9: *C\n",
|
||
" 原子数: 2, 分子量: 15.04\n",
|
||
"\n",
|
||
"✓ 处理完成！结果保存在变量 'result' 中\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 使用之前选择的分子\n",
|
||
"if 'selected_parent_smiles' in locals() and 'selected_parent_id' in locals():\n",
|
||
" test_smiles = selected_parent_smiles\n",
|
||
" test_id = selected_parent_id\n",
|
||
"else:\n",
|
||
" # 如果没有之前的选择，使用df_filtered_smarts的第一个分子\n",
|
||
" if 'df_filtered_smarts' in locals() and len(df_filtered_smarts) > 0:\n",
|
||
" test_smiles = df_filtered_smarts.iloc[0]['smiles']\n",
|
||
" test_id = df_filtered_smarts.iloc[0]['unique_id']\n",
|
||
" else:\n",
|
||
" test_smiles = None\n",
|
||
" test_id = None\n",
|
||
"\n",
|
||
"if test_smiles:\n",
|
||
" print(f\"{'='*80}\")\n",
|
||
" print(f\"处理分子: {test_id}\")\n",
|
||
" print(f\"{'='*80}\")\n",
|
||
" print(f\"SMILES: {test_smiles}\\n\")\n",
|
||
" \n",
|
||
" # 一站式处理\n",
|
||
" result = fragmenter.process_molecule(test_smiles, parent_id=test_id)\n",
|
||
" \n",
|
||
" print(f\"\\n断裂结果:\")\n",
|
||
" print(f\" 总碎片数: {len(result.fragments)}\")\n",
|
||
" print(f\" 断裂位置: {sorted(result.get_cleavage_positions())}\")\n",
|
||
" \n",
|
||
" if len(result.fragments) > 0:\n",
|
||
" print(f\"\\n前5个碎片:\")\n",
|
||
" for i, frag in enumerate(result.fragments[:5], 1):\n",
|
||
" print(f\" {i}. 位置 {frag.cleavage_position}: {frag.fragment_smiles}\")\n",
|
||
" print(f\" 原子数: {frag.atom_count}, 分子量: {frag.molecular_weight}\")\n",
|
||
" \n",
|
||
" print(f\"\\n✓ 处理完成！结果保存在变量 'result' 中\")\n",
|
||
"else:\n",
|
||
" print(\"请先运行前面的cell来选择分子\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
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"metadata": {},
|
||
"source": [
|
||
"### 15.2. 可视化分子\n",
|
||
"\n",
|
||
"使用 `visualize_molecule()` 方法可视化分子（带环编号）\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
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"metadata": {},
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"outputs": [
|
||
{
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"name": "stdout",
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"output_type": "stream",
|
||
"text": [
|
||
"可视化分子: mol_00113\n",
|
||
"SMILES: CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O)C(N(C)C)C2O)C(CCN2CC(C)CC(C)C2)CC(C)C(=O)/C=C/C(C)=C\\[C@@H]1COC1OC(C)C(O)C(OC)C1OC\n",
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||
"\n"
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]
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},
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"<path class=\"note\" d=\"M 259.6 304.3 Q 260.2 304.6, 260.5 305.0 Q 260.9 305.3, 260.9 306.0 Q 260.9 306.6, 260.6 307.0 Q 260.3 307.4, 259.8 307.7 Q 259.3 307.9, 258.6 307.9 Q 257.5 307.9, 256.8 307.4 Q 256.2 306.9, 256.2 306.0 Q 256.2 305.5, 256.5 305.1 Q 256.8 304.6, 257.3 304.3 Q 256.9 304.1, 256.7 303.7 Q 256.4 303.4, 256.4 302.8 Q 256.4 302.0, 257.0 301.6 Q 257.6 301.1, 258.5 301.1 Q 259.5 301.1, 260.1 301.6 Q 260.6 302.0, 260.6 302.8 Q 260.6 303.3, 260.3 303.7 Q 260.1 304.0, 259.6 304.3 M 258.5 301.8 Q 258.0 301.8, 257.7 302.1 Q 257.4 302.4, 257.4 302.8 Q 257.4 303.2, 257.6 303.4 Q 257.8 303.6, 258.1 303.8 Q 258.4 303.9, 258.9 304.1 Q 259.3 303.8, 259.5 303.5 Q 259.7 303.2, 259.7 302.8 Q 259.7 302.4, 259.4 302.1 Q 259.1 301.8, 258.5 301.8 M 258.6 307.2 Q 259.2 307.2, 259.6 306.9 Q 259.9 306.5, 259.9 306.0 Q 259.9 305.6, 259.7 305.4 Q 259.5 305.2, 259.2 305.1 Q 259.0 304.9, 258.4 304.8 L 258.0 304.6 Q 257.5 304.9, 257.3 305.2 Q 257.1 305.6, 257.1 306.0 Q 257.1 306.5, 257.5 306.9 Q 257.9 307.2, 258.6 307.2 \" fill=\"#000000\"/>\n",
|
||
"<path class=\"note\" d=\"M 286.8 268.5 Q 288.0 268.5, 288.6 269.3 Q 289.1 270.1, 289.1 271.7 Q 289.1 273.5, 288.4 274.4 Q 287.7 275.3, 286.3 275.3 Q 285.9 275.3, 285.5 275.2 Q 285.2 275.1, 284.8 274.9 L 285.2 274.3 Q 285.7 274.5, 286.3 274.5 Q 287.2 274.5, 287.7 273.9 Q 288.1 273.3, 288.2 272.1 Q 287.9 272.4, 287.4 272.6 Q 287.0 272.7, 286.6 272.7 Q 286.0 272.7, 285.5 272.5 Q 285.1 272.2, 284.8 271.8 Q 284.6 271.3, 284.6 270.7 Q 284.6 270.1, 284.9 269.6 Q 285.1 269.1, 285.6 268.8 Q 286.1 268.5, 286.8 268.5 M 285.5 270.7 Q 285.5 271.3, 285.8 271.6 Q 286.2 272.0, 286.7 272.0 Q 287.1 272.0, 287.5 271.8 Q 287.9 271.7, 288.2 271.4 Q 288.2 270.3, 287.8 269.8 Q 287.5 269.3, 286.8 269.3 Q 286.4 269.3, 286.1 269.5 Q 285.8 269.7, 285.7 270.0 Q 285.5 270.3, 285.5 270.7 \" fill=\"#000000\"/>\n",
|
||
"<path class=\"note\" d=\"M 335.8 258.2 L 337.3 258.2 L 337.3 253.2 L 335.7 253.7 L 335.5 253.2 L 337.5 252.3 L 338.1 252.4 L 338.1 258.2 L 339.4 258.2 L 339.4 258.9 L 335.8 258.9 L 335.8 258.2 \" fill=\"#000000\"/>\n",
|
||
"<path class=\"note\" d=\"M 342.7 259.0 Q 341.5 259.0, 340.9 258.1 Q 340.3 257.2, 340.3 255.6 Q 340.3 254.0, 340.9 253.1 Q 341.5 252.3, 342.7 252.3 Q 343.9 252.3, 344.5 253.1 Q 345.1 254.0, 345.1 255.6 Q 345.1 257.2, 344.5 258.1 Q 343.9 259.0, 342.7 259.0 M 342.7 258.2 Q 343.4 258.2, 343.8 257.6 Q 344.2 256.9, 344.2 255.6 Q 344.2 254.3, 343.8 253.7 Q 343.4 253.0, 342.7 253.0 Q 342.0 253.0, 341.6 253.7 Q 341.2 254.3, 341.2 255.6 Q 341.2 256.9, 341.6 257.6 Q 342.0 258.2, 342.7 258.2 \" fill=\"#000000\"/>\n",
|
||
"<path class=\"note\" d=\"M 380.0 239.6 L 381.4 239.6 L 381.4 234.7 L 379.8 235.2 L 379.6 234.6 L 381.6 233.7 L 382.3 233.8 L 382.3 239.6 L 383.6 239.6 L 383.6 240.3 L 380.0 240.3 L 380.0 239.6 \" fill=\"#000000\"/>\n",
|
||
"<path class=\"note\" d=\"M 384.7 239.6 L 386.2 239.6 L 386.2 234.7 L 384.6 235.2 L 384.4 234.6 L 386.4 233.7 L 387.0 233.8 L 387.0 239.6 L 388.3 239.6 L 388.3 240.3 L 384.7 240.3 L 384.7 239.6 \" fill=\"#000000\"/>\n",
|
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"<path class=\"note\" d=\"M 417.4 269.4 L 418.8 269.4 L 418.8 264.5 L 417.2 265.0 L 417.0 264.4 L 419.1 263.5 L 419.7 263.6 L 419.7 269.4 L 421.0 269.4 L 421.0 270.1 L 417.4 270.1 L 417.4 269.4 \" fill=\"#000000\"/>\n",
|
||
"<path class=\"note\" d=\"M 421.8 264.8 Q 422.1 264.2, 422.7 263.8 Q 423.2 263.5, 424.0 263.5 Q 425.0 263.5, 425.6 264.0 Q 426.1 264.6, 426.1 265.5 Q 426.1 266.5, 425.4 267.4 Q 424.7 268.3, 423.2 269.4 L 426.3 269.4 L 426.3 270.1 L 421.8 270.1 L 421.8 269.5 Q 423.0 268.6, 423.8 268.0 Q 424.5 267.3, 424.9 266.8 Q 425.2 266.2, 425.2 265.6 Q 425.2 264.9, 424.9 264.6 Q 424.6 264.2, 424.0 264.2 Q 423.5 264.2, 423.2 264.4 Q 422.8 264.7, 422.5 265.1 L 421.8 264.8 \" fill=\"#000000\"/>\n",
|
||
"<path class=\"note\" d=\"M 452.4 275.1 L 453.9 275.1 L 453.9 270.2 L 452.3 270.7 L 452.1 270.1 L 454.1 269.2 L 454.7 269.3 L 454.7 275.1 L 456.0 275.1 L 456.0 275.8 L 452.4 275.8 L 452.4 275.1 \" fill=\"#000000\"/>\n",
|
||
"<path class=\"note\" d=\"M 460.0 272.4 Q 460.6 272.6, 461.0 273.0 Q 461.3 273.4, 461.3 274.0 Q 461.3 274.6, 461.0 275.0 Q 460.7 275.5, 460.2 275.7 Q 459.7 275.9, 459.0 275.9 Q 458.3 275.9, 457.8 275.7 Q 457.3 275.4, 456.8 274.9 L 457.4 274.4 Q 457.8 274.8, 458.1 275.0 Q 458.5 275.2, 459.0 275.2 Q 459.6 275.2, 460.0 274.9 Q 460.3 274.6, 460.3 274.0 Q 460.3 273.4, 460.0 273.1 Q 459.6 272.8, 458.8 272.8 L 458.3 272.8 L 458.3 272.1 L 458.7 272.1 Q 459.4 272.1, 459.8 271.8 Q 460.2 271.4, 460.2 270.9 Q 460.2 270.4, 459.9 270.2 Q 459.6 269.9, 459.0 269.9 Q 458.5 269.9, 458.1 270.1 Q 457.8 270.3, 457.5 270.8 L 456.9 270.5 Q 457.1 269.9, 457.7 269.5 Q 458.2 269.2, 459.0 269.2 Q 460.0 269.2, 460.6 269.6 Q 461.1 270.1, 461.1 270.9 Q 461.1 271.4, 460.8 271.8 Q 460.6 272.2, 460.0 272.4 \" fill=\"#000000\"/>\n",
|
||
"<path class=\"note\" d=\"M 487.9 327.9 L 489.3 327.9 L 489.3 323.0 L 487.7 323.5 L 487.5 322.9 L 489.5 322.0 L 490.2 322.1 L 490.2 327.9 L 491.5 327.9 L 491.5 328.7 L 487.9 328.7 L 487.9 327.9 \" fill=\"#000000\"/>\n",
|
||
"<path class=\"note\" d=\"M 496.6 326.4 L 497.4 326.4 L 497.4 327.1 L 496.6 327.1 L 496.6 328.7 L 495.8 328.7 L 495.8 327.1 L 492.3 327.1 L 492.3 326.6 L 495.2 322.1 L 496.6 322.1 L 496.6 326.4 M 493.4 326.4 L 495.8 326.4 L 495.8 322.7 L 493.4 326.4 \" fill=\"#000000\"/>\n",
|
||
"<path class=\"note\" d=\"M 478.3 374.6 L 479.7 374.6 L 479.7 369.7 L 478.1 370.2 L 477.9 369.6 L 479.9 368.7 L 480.6 368.8 L 480.6 374.6 L 481.9 374.6 L 481.9 375.3 L 478.3 375.3 L 478.3 374.6 \" fill=\"#000000\"/>\n",
|
||
"<path class=\"note\" d=\"M 485.1 371.2 Q 485.7 371.2, 486.1 371.5 Q 486.6 371.7, 486.9 372.2 Q 487.2 372.6, 487.2 373.2 Q 487.2 373.9, 486.8 374.4 Q 486.5 374.9, 486.0 375.2 Q 485.4 375.4, 484.8 375.4 Q 484.2 375.4, 483.6 375.2 Q 483.1 375.0, 482.7 374.5 L 483.3 374.0 Q 483.6 374.3, 484.0 374.5 Q 484.4 374.7, 484.8 374.7 Q 485.4 374.7, 485.8 374.3 Q 486.2 373.9, 486.2 373.3 Q 486.2 372.6, 485.8 372.2 Q 485.4 371.9, 484.8 371.9 Q 484.2 371.9, 483.6 372.2 L 483.0 371.9 L 483.4 368.7 L 486.8 368.7 L 486.7 369.5 L 484.1 369.5 L 483.9 371.4 Q 484.5 371.2, 485.1 371.2 \" fill=\"#000000\"/>\n",
|
||
"</svg>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.SVG object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"<IPython.core.display.SVG object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"if 'result' in locals():\n",
|
||
" print(f\"可视化分子: {result.parent_id}\")\n",
|
||
" print(f\"SMILES: {result.parent_smiles}\\n\")\n",
|
||
" \n",
|
||
" # 生成SVG并显示\n",
|
||
" svg = fragmenter.visualize_molecule(\n",
|
||
" result.parent_smiles,\n",
|
||
" show_numbering=True,\n",
|
||
" size=(800, 800),\n",
|
||
" title=result.parent_id\n",
|
||
" )\n",
|
||
" display(SVG(svg))\n",
|
||
" \n",
|
||
" # 可选：保存为PNG\n",
|
||
" # output_png = project_root / \"output\" / \"test_fragmenter\" / f\"{result.parent_id}_numbered.png\"\n",
|
||
" # svg = fragmenter.visualize_molecule(\n",
|
||
" # result.parent_smiles,\n",
|
||
" # show_numbering=True,\n",
|
||
" # save_path=output_png,\n",
|
||
" # dpi=600\n",
|
||
" # )\n",
|
||
" # print(f\"\\n✓ PNG已保存到: {output_png}\")\n",
|
||
"else:\n",
|
||
" print(\"请先运行前面的cell来处理分子\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 15.3. 可视化碎片\n",
|
||
"\n",
|
||
"使用 `visualize_all_fragments()` 方法可视化所有碎片\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"显示碎片 (最多5个):\n",
|
||
"\n",
|
||
"\n",
|
||
"============================================================\n",
|
||
"Fragment 1/5\n",
|
||
"Position: 4\n",
|
||
"SMILES: *O\n",
|
||
"Atoms: 2, MW: 17.01\n",
|
||
"============================================================\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/svg+xml": [
|
||
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" baseProfile=\"full\" xml:space=\"preserve\" width=\"400px\" height=\"400px\" viewBox=\"0 0 400 400\">\n",
|
||
"<!-- END OF HEADER -->\n",
|
||
"<rect style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"400.0\" height=\"400.0\" x=\"0.0\" y=\"0.0\"> </rect>\n",
|
||
"<path class=\"bond-0 atom-0 atom-1\" d=\"M 75.3,199.8 L 180.4,199.8\" style=\"fill:none;fill-rule:evenodd;stroke:#191919;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-0 atom-0 atom-1\" d=\"M 180.4,199.8 L 285.5,199.8\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"atom-0\" d=\"M 55.2 196.7 L 59.3 192.4 L 53.8 191.3 L 54.6 188.5 L 59.7 191.0 L 59.0 185.5 L 62.0 185.4 L 61.2 191.0 L 66.3 188.6 L 67.2 191.3 L 61.6 192.3 L 65.6 196.6 L 63.2 198.4 L 60.4 193.1 L 57.6 198.4 L 55.2 196.7 \" fill=\"#191919\"/>\n",
|
||
"<path class=\"atom-1\" d=\"M 293.6 199.9 Q 293.6 193.1, 297.0 189.3 Q 300.3 185.5, 306.6 185.5 Q 312.9 185.5, 316.3 189.3 Q 319.6 193.1, 319.6 199.9 Q 319.6 206.8, 316.2 210.7 Q 312.8 214.6, 306.6 214.6 Q 300.4 214.6, 297.0 210.7 Q 293.6 206.8, 293.6 199.9 M 306.6 211.4 Q 310.9 211.4, 313.3 208.5 Q 315.6 205.6, 315.6 199.9 Q 315.6 194.3, 313.3 191.5 Q 310.9 188.7, 306.6 188.7 Q 302.3 188.7, 299.9 191.5 Q 297.6 194.3, 297.6 199.9 Q 297.6 205.6, 299.9 208.5 Q 302.3 211.4, 306.6 211.4 \" fill=\"#FF0000\"/>\n",
|
||
"<path class=\"atom-1\" d=\"M 324.0 185.8 L 327.9 185.8 L 327.9 197.9 L 342.4 197.9 L 342.4 185.8 L 346.2 185.8 L 346.2 214.1 L 342.4 214.1 L 342.4 201.1 L 327.9 201.1 L 327.9 214.1 L 324.0 214.1 L 324.0 185.8 \" fill=\"#FF0000\"/>\n",
|
||
"</svg>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.SVG object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"============================================================\n",
|
||
"Fragment 2/5\n",
|
||
"Position: 5\n",
|
||
"SMILES: *C\n",
|
||
"Atoms: 2, MW: 15.04\n",
|
||
"============================================================\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/svg+xml": [
|
||
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" baseProfile=\"full\" xml:space=\"preserve\" width=\"400px\" height=\"400px\" viewBox=\"0 0 400 400\">\n",
|
||
"<!-- END OF HEADER -->\n",
|
||
"<rect style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"400.0\" height=\"400.0\" x=\"0.0\" y=\"0.0\"> </rect>\n",
|
||
"<path class=\"bond-0 atom-0 atom-1\" d=\"M 52.5,207.2 L 212.3,207.2\" style=\"fill:none;fill-rule:evenodd;stroke:#191919;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-0 atom-0 atom-1\" d=\"M 212.3,207.2 L 372.0,207.2\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"atom-0\" d=\"M 29.3 204.0 L 33.5 199.8 L 28.0 198.7 L 28.8 195.8 L 33.9 198.4 L 33.2 192.8 L 36.2 192.8 L 35.3 198.4 L 40.5 196.0 L 41.4 198.7 L 35.8 199.7 L 39.8 204.0 L 37.3 205.8 L 34.6 200.5 L 31.8 205.8 L 29.3 204.0 \" fill=\"#191919\"/>\n",
|
||
"</svg>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.SVG object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"============================================================\n",
|
||
"Fragment 3/5\n",
|
||
"Position: 6\n",
|
||
"SMILES: *OC1OC(C)C(O)C(N(C)C)C1O\n",
|
||
"Atoms: 14, MW: 190.22\n",
|
||
"============================================================\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/svg+xml": [
|
||
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" baseProfile=\"full\" xml:space=\"preserve\" width=\"400px\" height=\"400px\" viewBox=\"0 0 400 400\">\n",
|
||
"<!-- END OF HEADER -->\n",
|
||
"<rect style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"400.0\" height=\"400.0\" x=\"0.0\" y=\"0.0\"> </rect>\n",
|
||
"<path class=\"bond-0 atom-0 atom-1\" d=\"M 27.2,243.2 L 51.2,254.8\" style=\"fill:none;fill-rule:evenodd;stroke:#191919;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-0 atom-0 atom-1\" d=\"M 51.2,254.8 L 75.1,266.4\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-1 atom-1 atom-2\" d=\"M 97.5,264.3 L 120.3,248.8\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-1 atom-1 atom-2\" d=\"M 120.3,248.8 L 143.1,233.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-2 atom-2 atom-3\" d=\"M 143.1,233.4 L 141.1,205.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-2 atom-2 atom-3\" d=\"M 141.1,205.4 L 139.0,177.4\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-3 atom-3 atom-4\" d=\"M 149.3,157.4 L 172.1,142.0\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-3 atom-3 atom-4\" d=\"M 172.1,142.0 L 194.9,126.5\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-4 atom-4 atom-5\" d=\"M 194.9,126.5 L 190.0,58.1\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-5 atom-4 atom-6\" d=\"M 194.9,126.5 L 256.6,156.5\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-6 atom-6 atom-7\" d=\"M 256.6,156.5 L 279.4,141.0\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-6 atom-6 atom-7\" d=\"M 279.4,141.0 L 302.2,125.6\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-7 atom-6 atom-8\" d=\"M 256.6,156.5 L 261.5,224.9\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-8 atom-8 atom-9\" d=\"M 261.5,224.9 L 287.6,237.5\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-8 atom-8 atom-9\" d=\"M 287.6,237.5 L 313.6,250.2\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-9 atom-9 atom-10\" d=\"M 324.1,266.8 L 326.1,295.0\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-9 atom-9 atom-10\" d=\"M 326.1,295.0 L 328.1,323.2\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
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"<path class=\"bond-10 atom-9 atom-11\" d=\"M 332.8,248.3 L 356.4,232.3\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-10 atom-9 atom-11\" d=\"M 356.4,232.3 L 380.0,216.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-11 atom-8 atom-12\" d=\"M 261.5,224.9 L 204.8,263.3\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-12 atom-12 atom-13\" d=\"M 204.8,263.3 L 206.8,291.5\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-12 atom-12 atom-13\" d=\"M 206.8,291.5 L 208.8,319.7\" style=\"fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-13 atom-12 atom-2\" d=\"M 204.8,263.3 L 143.1,233.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"atom-0\" d=\"M 20.9 239.7 L 23.8 236.8 L 20.0 236.1 L 20.6 234.1 L 24.1 235.9 L 23.6 232.0 L 25.6 232.0 L 25.0 235.8 L 28.6 234.2 L 29.2 236.1 L 25.3 236.8 L 28.1 239.7 L 26.4 240.9 L 24.5 237.3 L 22.6 240.9 L 20.9 239.7 \" fill=\"#191919\"/>\n",
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"<path class=\"atom-1\" d=\"M 77.4 271.9 Q 77.4 267.2, 79.7 264.6 Q 82.0 262.0, 86.3 262.0 Q 90.6 262.0, 92.9 264.6 Q 95.2 267.2, 95.2 271.9 Q 95.2 276.6, 92.9 279.3 Q 90.5 282.0, 86.3 282.0 Q 82.0 282.0, 79.7 279.3 Q 77.4 276.6, 77.4 271.9 M 86.3 279.8 Q 89.2 279.8, 90.8 277.8 Q 92.5 275.8, 92.5 271.9 Q 92.5 268.1, 90.8 266.2 Q 89.2 264.2, 86.3 264.2 Q 83.3 264.2, 81.7 266.1 Q 80.1 268.1, 80.1 271.9 Q 80.1 275.8, 81.7 277.8 Q 83.3 279.8, 86.3 279.8 \" fill=\"#FF0000\"/>\n",
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|
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"<path class=\"atom-13\" d=\"M 221.6 322.1 L 224.2 322.1 L 224.2 330.4 L 234.2 330.4 L 234.2 322.1 L 236.8 322.1 L 236.8 341.6 L 234.2 341.6 L 234.2 332.6 L 224.2 332.6 L 224.2 341.6 L 221.6 341.6 L 221.6 322.1 \" fill=\"#FF0000\"/>\n",
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"</svg>"
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],
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"text/plain": [
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"<IPython.core.display.SVG object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"============================================================\n",
|
||
"Fragment 4/5\n",
|
||
"Position: 7\n",
|
||
"SMILES: *CCN1CC(C)CC(C)C1\n",
|
||
"Atoms: 11, MW: 140.25\n",
|
||
"============================================================\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
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"image/svg+xml": [
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"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" baseProfile=\"full\" xml:space=\"preserve\" width=\"400px\" height=\"400px\" viewBox=\"0 0 400 400\">\n",
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"<!-- END OF HEADER -->\n",
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"<rect style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"400.0\" height=\"400.0\" x=\"0.0\" y=\"0.0\"> </rect>\n",
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"<path class=\"bond-0 atom-0 atom-1\" d=\"M 31.8,224.8 L 62.2,216.2\" style=\"fill:none;fill-rule:evenodd;stroke:#191919;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-0 atom-0 atom-1\" d=\"M 62.2,216.2 L 92.7,207.7\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-1 atom-1 atom-2\" d=\"M 92.7,207.7 L 143.2,257.1\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
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"<path class=\"bond-2 atom-2 atom-3\" d=\"M 143.2,257.1 L 172.2,248.9\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-2 atom-2 atom-3\" d=\"M 172.2,248.9 L 201.2,240.8\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-3 atom-3 atom-4\" d=\"M 221.5,197.7 L 228.6,169.7\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-4 atom-4 atom-5\" d=\"M 228.6,169.7 L 296.6,150.7\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
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"<path class=\"bond-5 atom-5 atom-6\" d=\"M 296.6,150.7 L 314.1,82.3\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
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"<path class=\"bond-6 atom-5 atom-7\" d=\"M 296.6,150.7 L 347.1,200.0\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
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"<path class=\"bond-7 atom-7 atom-8\" d=\"M 347.1,200.0 L 329.5,268.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
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"<path class=\"bond-8 atom-8 atom-9\" d=\"M 329.5,268.4 L 380.0,317.7\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
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"<path class=\"bond-9 atom-8 atom-10\" d=\"M 329.5,268.4 L 261.6,287.4\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
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"<path class=\"bond-10 atom-10 atom-3\" d=\"M 261.6,287.4 L 241.3,267.6\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-10 atom-10 atom-3\" d=\"M 241.3,267.6 L 221.0,247.7\" style=\"fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
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"<path d=\"M 91.2,208.1 L 92.7,207.7 L 95.2,210.2\" style=\"fill:none;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
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"<path d=\"M 140.6,254.6 L 143.2,257.1 L 144.6,256.7\" style=\"fill:none;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
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"<path d=\"M 228.3,171.1 L 228.6,169.7 L 232.0,168.7\" style=\"fill:none;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
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"<path d=\"M 344.5,197.5 L 347.1,200.0 L 346.2,203.4\" style=\"fill:none;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
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"<path d=\"M 265.0,286.4 L 261.6,287.4 L 260.6,286.4\" style=\"fill:none;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
"<path class=\"atom-0\" d=\"M 21.0 224.5 L 23.9 221.5 L 20.0 220.7 L 20.6 218.7 L 24.2 220.5 L 23.7 216.6 L 25.8 216.6 L 25.2 220.5 L 28.8 218.8 L 29.5 220.7 L 25.5 221.5 L 28.3 224.5 L 26.6 225.7 L 24.7 222.0 L 22.7 225.7 L 21.0 224.5 \" fill=\"#191919\"/>\n",
|
||
"<path class=\"atom-3\" d=\"M 206.7 228.1 L 213.3 238.6 Q 213.9 239.7, 214.9 241.6 Q 216.0 243.5, 216.0 243.6 L 216.0 228.1 L 218.7 228.1 L 218.7 248.0 L 216.0 248.0 L 208.9 236.5 Q 208.1 235.1, 207.2 233.6 Q 206.4 232.0, 206.1 231.5 L 206.1 248.0 L 203.5 248.0 L 203.5 228.1 L 206.7 228.1 \" fill=\"#0000FF\"/>\n",
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"</svg>"
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"text/plain": [
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"<IPython.core.display.SVG object>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
|
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"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"============================================================\n",
|
||
"Fragment 5/5\n",
|
||
"Position: 9\n",
|
||
"SMILES: *C\n",
|
||
"Atoms: 2, MW: 15.04\n",
|
||
"============================================================\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/svg+xml": [
|
||
"<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:rdkit=\"http://www.rdkit.org/xml\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" version=\"1.1\" baseProfile=\"full\" xml:space=\"preserve\" width=\"400px\" height=\"400px\" viewBox=\"0 0 400 400\">\n",
|
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"<!-- END OF HEADER -->\n",
|
||
"<rect style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"400.0\" height=\"400.0\" x=\"0.0\" y=\"0.0\"> </rect>\n",
|
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"<path class=\"bond-0 atom-0 atom-1\" d=\"M 52.5,207.2 L 212.3,207.2\" style=\"fill:none;fill-rule:evenodd;stroke:#191919;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
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"<path class=\"bond-0 atom-0 atom-1\" d=\"M 212.3,207.2 L 372.0,207.2\" style=\"fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"atom-0\" d=\"M 29.3 204.0 L 33.5 199.8 L 28.0 198.7 L 28.8 195.8 L 33.9 198.4 L 33.2 192.8 L 36.2 192.8 L 35.3 198.4 L 40.5 196.0 L 41.4 198.7 L 35.8 199.7 L 39.8 204.0 L 37.3 205.8 L 34.6 200.5 L 31.8 205.8 L 29.3 204.0 \" fill=\"#191919\"/>\n",
|
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"</svg>"
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],
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"text/plain": [
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"<IPython.core.display.SVG object>"
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]
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},
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"metadata": {},
|
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"output_type": "display_data"
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},
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{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"... and 4 more fragments\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"if 'result' in locals() and len(result.fragments) > 0:\n",
|
||
" print(f\"显示碎片 (最多5个):\\n\")\n",
|
||
" \n",
|
||
" # 显示前5个碎片\n",
|
||
" fragmenter.visualize_all_fragments(result, max_display=5)\n",
|
||
" \n",
|
||
" # 可选：保存所有碎片图片到目录\n",
|
||
" # output_dir = project_root / \"output\" / \"test_fragmenter\" / \"fragments\"\n",
|
||
" # fragmenter.visualize_all_fragments(result, max_display=10, save_dir=output_dir, dpi=600)\n",
|
||
" # print(f\"\\n✓ 碎片图片已保存到: {output_dir}\")\n",
|
||
"else:\n",
|
||
" print(\"请先运行前面的cell来处理分子\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 15.4. 数据导出\n",
|
||
"\n",
|
||
"使用 `to_dataframe()` 和 `save_to_json()` 导出数据\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"碎片DataFrame:\n",
|
||
" 形状: (9, 7)\n",
|
||
" 列: ['fragment_id', 'parent_id', 'parent_smiles', 'cleavage_position', 'fragment_smiles', 'atom_count', 'molecular_weight']\n",
|
||
"\n",
|
||
"前5行:\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>fragment_id</th>\n",
|
||
" <th>parent_id</th>\n",
|
||
" <th>parent_smiles</th>\n",
|
||
" <th>cleavage_position</th>\n",
|
||
" <th>fragment_smiles</th>\n",
|
||
" <th>atom_count</th>\n",
|
||
" <th>molecular_weight</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>mol_00113_frag_0</td>\n",
|
||
" <td>mol_00113</td>\n",
|
||
" <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>*O</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>17.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>mol_00113_frag_1</td>\n",
|
||
" <td>mol_00113</td>\n",
|
||
" <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>*C</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>15.04</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>mol_00113_frag_2</td>\n",
|
||
" <td>mol_00113</td>\n",
|
||
" <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>*OC1OC(C)C(O)C(N(C)C)C1O</td>\n",
|
||
" <td>14</td>\n",
|
||
" <td>190.22</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>mol_00113_frag_3</td>\n",
|
||
" <td>mol_00113</td>\n",
|
||
" <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>*CCN1CC(C)CC(C)C1</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>140.25</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>mol_00113_frag_4</td>\n",
|
||
" <td>mol_00113</td>\n",
|
||
" <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
|
||
" <td>9</td>\n",
|
||
" <td>*C</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>15.04</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" fragment_id parent_id \\\n",
|
||
"0 mol_00113_frag_0 mol_00113 \n",
|
||
"1 mol_00113_frag_1 mol_00113 \n",
|
||
"2 mol_00113_frag_2 mol_00113 \n",
|
||
"3 mol_00113_frag_3 mol_00113 \n",
|
||
"4 mol_00113_frag_4 mol_00113 \n",
|
||
"\n",
|
||
" parent_smiles cleavage_position \\\n",
|
||
"0 CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O... 4 \n",
|
||
"1 CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O... 5 \n",
|
||
"2 CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O... 6 \n",
|
||
"3 CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O... 7 \n",
|
||
"4 CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O... 9 \n",
|
||
"\n",
|
||
" fragment_smiles atom_count molecular_weight \n",
|
||
"0 *O 2 17.01 \n",
|
||
"1 *C 2 15.04 \n",
|
||
"2 *OC1OC(C)C(O)C(N(C)C)C1O 14 190.22 \n",
|
||
"3 *CCN1CC(C)CC(C)C1 11 140.25 \n",
|
||
"4 *C 2 15.04 "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"✓ JSON已保存到: /home/zly/project/macro_split/output/test_fragmenter/mol_00113_fragments.json\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"if 'result' in locals():\n",
|
||
" # 转换为DataFrame\n",
|
||
" df_fragments = fragmenter.to_dataframe(result)\n",
|
||
" \n",
|
||
" print(f\"碎片DataFrame:\")\n",
|
||
" print(f\" 形状: {df_fragments.shape}\")\n",
|
||
" print(f\" 列: {df_fragments.columns.tolist()}\")\n",
|
||
" print(f\"\\n前5行:\")\n",
|
||
" display(df_fragments.head())\n",
|
||
" \n",
|
||
" # 保存为JSON\n",
|
||
" json_path = project_root / \"output\" / \"test_fragmenter\" / f\"{result.parent_id}_fragments.json\"\n",
|
||
" fragmenter.save_to_json(result, json_path)\n",
|
||
" print(f\"\\n✓ JSON已保存到: {json_path}\")\n",
|
||
"else:\n",
|
||
" print(\"请先运行前面的cell来处理分子\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 15.5. 批量处理CSV\n",
|
||
"\n",
|
||
"使用 `process_csv()` 方法批量处理 `ring16_match_smarts.csv` 中的分子\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 34,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"批量处理CSV: /home/zly/project/macro_split/output/ring16_match_smarts.csv\n",
|
||
"处理前10个分子（测试）...\n",
|
||
"\n",
|
||
"\n",
|
||
"Processed 10 molecules successfully\n",
|
||
"\n",
|
||
"批处理统计:\n",
|
||
" 成功处理: 10 个分子\n",
|
||
" 总碎片数: 88\n",
|
||
"\n",
|
||
"示例结果:\n",
|
||
" 1. mol_00113: 9 个碎片\n",
|
||
" 2. mol_00288: 9 个碎片\n",
|
||
" 3. mol_00313: 9 个碎片\n",
|
||
"\n",
|
||
"合并后的DataFrame:\n",
|
||
" 形状: (88, 7)\n",
|
||
" 前5行:\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"text/html": [
|
||
"<div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>fragment_id</th>\n",
|
||
" <th>parent_id</th>\n",
|
||
" <th>parent_smiles</th>\n",
|
||
" <th>cleavage_position</th>\n",
|
||
" <th>fragment_smiles</th>\n",
|
||
" <th>atom_count</th>\n",
|
||
" <th>molecular_weight</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>mol_00113_frag_0</td>\n",
|
||
" <td>mol_00113</td>\n",
|
||
" <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
|
||
" <td>4</td>\n",
|
||
" <td>*O</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>17.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>mol_00113_frag_1</td>\n",
|
||
" <td>mol_00113</td>\n",
|
||
" <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
|
||
" <td>5</td>\n",
|
||
" <td>*C</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>15.04</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>mol_00113_frag_2</td>\n",
|
||
" <td>mol_00113</td>\n",
|
||
" <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
|
||
" <td>6</td>\n",
|
||
" <td>*OC1OC(C)C(O)C(N(C)C)C1O</td>\n",
|
||
" <td>14</td>\n",
|
||
" <td>190.22</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>mol_00113_frag_3</td>\n",
|
||
" <td>mol_00113</td>\n",
|
||
" <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>*CCN1CC(C)CC(C)C1</td>\n",
|
||
" <td>11</td>\n",
|
||
" <td>140.25</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>mol_00113_frag_4</td>\n",
|
||
" <td>mol_00113</td>\n",
|
||
" <td>CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O...</td>\n",
|
||
" <td>9</td>\n",
|
||
" <td>*C</td>\n",
|
||
" <td>2</td>\n",
|
||
" <td>15.04</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"</div>"
|
||
],
|
||
"text/plain": [
|
||
" fragment_id parent_id \\\n",
|
||
"0 mol_00113_frag_0 mol_00113 \n",
|
||
"1 mol_00113_frag_1 mol_00113 \n",
|
||
"2 mol_00113_frag_2 mol_00113 \n",
|
||
"3 mol_00113_frag_3 mol_00113 \n",
|
||
"4 mol_00113_frag_4 mol_00113 \n",
|
||
"\n",
|
||
" parent_smiles cleavage_position \\\n",
|
||
"0 CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O... 4 \n",
|
||
"1 CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O... 5 \n",
|
||
"2 CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O... 6 \n",
|
||
"3 CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O... 7 \n",
|
||
"4 CCC1OC(=O)C[C@H](O)C(C)[C@@H](O[C@@H]2OC(C)C(O... 9 \n",
|
||
"\n",
|
||
" fragment_smiles atom_count molecular_weight \n",
|
||
"0 *O 2 17.01 \n",
|
||
"1 *C 2 15.04 \n",
|
||
"2 *OC1OC(C)C(O)C(N(C)C)C1O 14 190.22 \n",
|
||
"3 *CCN1CC(C)CC(C)C1 11 140.25 \n",
|
||
"4 *C 2 15.04 "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"✓ 合并结果已保存到: /home/zly/project/macro_split/output/test_fragmenter/batch_fragments.csv\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 批量处理前10个分子（测试）\n",
|
||
"csv_path = project_root / \"output\" / \"ring16_match_smarts.csv\"\n",
|
||
"\n",
|
||
"if csv_path.exists():\n",
|
||
" print(f\"批量处理CSV: {csv_path}\")\n",
|
||
" print(f\"处理前10个分子（测试）...\\n\")\n",
|
||
" \n",
|
||
" # 批量处理\n",
|
||
" batch_results = fragmenter.process_csv(\n",
|
||
" csv_path,\n",
|
||
" smiles_column=\"smiles\",\n",
|
||
" id_column=\"unique_id\",\n",
|
||
" max_rows=10 # 只处理前10个\n",
|
||
" )\n",
|
||
" \n",
|
||
" print(f\"\\n批处理统计:\")\n",
|
||
" print(f\" 成功处理: {len(batch_results)} 个分子\")\n",
|
||
" \n",
|
||
" total_fragments = sum(len(r.fragments) for r in batch_results)\n",
|
||
" print(f\" 总碎片数: {total_fragments}\")\n",
|
||
" \n",
|
||
" if len(batch_results) > 0:\n",
|
||
" print(f\"\\n示例结果:\")\n",
|
||
" for i, mol_frag in enumerate(batch_results[:3], 1):\n",
|
||
" print(f\" {i}. {mol_frag.parent_id}: {len(mol_frag.fragments)} 个碎片\")\n",
|
||
" \n",
|
||
" # 转换为DataFrame\n",
|
||
" df_all = fragmenter.batch_to_dataframe(batch_results)\n",
|
||
" print(f\"\\n合并后的DataFrame:\")\n",
|
||
" print(f\" 形状: {df_all.shape}\")\n",
|
||
" print(f\" 前5行:\")\n",
|
||
" display(df_all.head())\n",
|
||
" \n",
|
||
" # 保存合并结果\n",
|
||
" output_csv = project_root / \"output\" / \"test_fragmenter\" / \"batch_fragments.csv\"\n",
|
||
" output_csv.parent.mkdir(parents=True, exist_ok=True)\n",
|
||
" df_all.to_csv(output_csv, index=False)\n",
|
||
" print(f\"\\n✓ 合并结果已保存到: {output_csv}\")\n",
|
||
"else:\n",
|
||
" print(f\"CSV文件不存在: {csv_path}\")\n",
|
||
" print(\"请先运行前面的cell生成此文件\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"### 15.6. MacrolactoneFragmenter 使用总结\n",
|
||
"\n",
|
||
"**核心功能：**\n",
|
||
"1. ✅ **自动编号和断裂**：`process_molecule()` 一站式处理\n",
|
||
"2. ✅ **单位置断裂**：`cleave_at_position()` 精确控制\n",
|
||
"3. ✅ **可视化**：`visualize_molecule()` 和 `visualize_fragment()`\n",
|
||
"4. ✅ **批量处理**：`process_csv()` 处理整个CSV\n",
|
||
"5. ✅ **数据导出**：`to_dataframe()` 和 `save_to_json()`\n",
|
||
"\n",
|
||
"**特性：**\n",
|
||
"- 🔹 支持 12-20 元环大环内酯（目前默认实现16环）\n",
|
||
"- 🔹 可自定义 SMARTS 模式\n",
|
||
"- 🔹 输出格式：JSON、DataFrame、PNG图片\n",
|
||
"- 🔹 预留 SQLModel 数据库接口\n",
|
||
"\n",
|
||
"**下一步：**\n",
|
||
"- 补充其他环大小的 SMARTS 模式（12, 14, 15, 17-20）\n",
|
||
"- 实现 SQLModel 数据库集成\n",
|
||
"- 添加更多统计分析功能\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.14.0"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 2
|
||
}
|