775 lines
46 KiB
Plaintext
775 lines
46 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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"# 筛选芳香胺候选药物 - Sandmeyer反应起始物分析\n",
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"\n",
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"## 背景介绍\n",
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"\n",
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"### Sandmeyer反应回顾\n",
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"Sandmeyer反应是经典的芳香胺转化方法：\n",
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"**Ar-NH₂ → [Ar-N₂]⁺ → Ar-X**\n",
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"其中 X = Cl, Br, I, CN, OH, SCN 等\n",
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"\n",
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"### 筛选目标\n",
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"通过识别药物分子中含有芳香胺结构（Ar-NH₂）的化合物，\n",
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"找出可能作为Sandmeyer反应起始物的候选药物。\n",
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"这些分子可能原本通过Sandmeyer反应引入芳香卤素，\n",
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"现在可以用张夏恒反应进行更高效的转化。\n",
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"\n",
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"### SMARTS模式\n",
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"使用SMARTS模式 `[c,n][NH2]` 匹配：\n",
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"- `[c,n]`: 芳香碳或氮原子\n",
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"- `[NH2]`: 氨基（-NH₂）\n",
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"\n",
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"**重要提醒：**\n",
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"- 此筛选基于分子结构特征\n",
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"- 最终需要查阅文献确认合成路线\n",
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"- 并非所有含芳香胺的药物都使用Sandmeyer反应"
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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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"## 导入所需库"
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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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"execution": {
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||
"iopub.execute_input": "2025-11-11T13:21:31.660096Z",
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"iopub.status.busy": "2025-11-11T13:21:31.657369Z",
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"iopub.status.idle": "2025-11-11T13:21:32.943162Z",
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"shell.execute_reply": "2025-11-11T13:21:32.938881Z"
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}
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},
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"outputs": [],
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"source": [
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"import os\n",
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"from pathlib import Path\n",
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"from rdkit import Chem\n",
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"from rdkit.Chem import PandasTools, 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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"from rdkit.Chem import AllChem\n",
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"import pandas as pd\n",
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"import warnings\n",
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"warnings.filterwarnings('ignore')\n",
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"\n",
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"# 设置显示选项\n",
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"pd.set_option('display.max_columns', None)\n",
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"pd.set_option('display.max_colwidth', 100)"
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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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"## 定义筛选模式和可视化函数\n",
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"\n",
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"### SMARTS模式设置\n",
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"- **目标模式**: `[c,n][NH2]` - 芳香碳/氮原子连接的氨基\n",
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"- **匹配逻辑**: 寻找所有包含此子结构的分子"
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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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"execution": {
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"iopub.execute_input": "2025-11-11T13:21:32.959832Z",
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"iopub.status.busy": "2025-11-11T13:21:32.957734Z",
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"iopub.status.idle": "2025-11-11T13:21:32.987085Z",
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"shell.execute_reply": "2025-11-11T13:21:32.980584Z"
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}
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},
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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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"使用SMARTS模式: [c,n][NH2]\n",
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"模式验证: ✓\n",
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"\n",
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"创建目录：../data/drug_targetmol/aniline_candidates\n",
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"创建可视化目录：../data/drug_targetmol/aniline_candidates/visualizations\n"
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]
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}
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],
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"source": [
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"# 定义筛选模式\n",
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"TARGET_SMARTS = '[c,n][NH2]'\n",
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"pattern = Chem.MolFromSmarts(TARGET_SMARTS)\n",
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"\n",
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"if pattern is None:\n",
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" raise ValueError(f\"无效的SMARTS模式: {TARGET_SMARTS}\")\n",
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"\n",
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"print(f\"使用SMARTS模式: {TARGET_SMARTS}\")\n",
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"print(f\"模式验证: {'✓' if pattern else '✗'}\")\n",
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"\n",
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"# 创建输出目录\n",
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"output_base = Path(\"../data/drug_targetmol\")\n",
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"output_dir = output_base / \"aniline_candidates\"\n",
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"visualization_dir = output_dir / \"visualizations\"\n",
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"\n",
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"output_dir.mkdir(exist_ok=True)\n",
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"visualization_dir.mkdir(exist_ok=True)\n",
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"\n",
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"print(f\"\\n创建目录：{output_dir}\")\n",
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"print(f\"创建可视化目录：{visualization_dir}\")\n",
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"\n",
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"def generate_highlighted_svg(mol, highlight_atoms, filename, title=\"\"):\n",
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" \"\"\"生成高亮匹配结构的高清晰度SVG图片\"\"\"\n",
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" # 计算2D坐标\n",
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" AllChem.Compute2DCoords(mol)\n",
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" \n",
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" # 创建SVG绘制器\n",
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" drawer = rdMolDraw2D.MolDraw2DSVG(1200, 900) # 更大的尺寸以提高清晰度\n",
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" drawer.SetFontSize(12)\n",
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" \n",
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" # 绘制选项\n",
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" draw_options = drawer.drawOptions()\n",
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" draw_options.addAtomIndices = False # 不显示原子索引，保持简洁\n",
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" draw_options.addBondIndices = False\n",
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" draw_options.addStereoAnnotation = True\n",
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" draw_options.fixedFontSize = 12\n",
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" \n",
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" # 高亮匹配的原子（蓝色）\n",
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" atom_colors = {}\n",
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" for atom_idx in highlight_atoms:\n",
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" atom_colors[atom_idx] = (0.3, 0.3, 1.0) # 蓝色高亮\n",
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" \n",
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" # 绘制分子\n",
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" drawer.DrawMolecule(mol, \n",
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" highlightAtoms=highlight_atoms,\n",
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" highlightAtomColors=atom_colors)\n",
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" \n",
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" drawer.FinishDrawing()\n",
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" svg_content = drawer.GetDrawingText()\n",
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" \n",
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" # 添加标题\n",
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" if title:\n",
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" # 在SVG中添加标题\n",
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" svg_lines = svg_content.split(\"\\\\n\")\n",
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" # 在<g>标签前插入标题\n",
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" for i, line in enumerate(svg_lines):\n",
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" if \"<g \" in line and \"transform\" in line:\n",
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" svg_lines.insert(i, f\"<text x=\\\"50%\\\" y=\\\"30\\\" text-anchor=\\\"middle\\\" font-size=\\\"16\\\" font-weight=\\\"bold\\\">{title}</text>\")\n",
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" break\n",
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" svg_with_title = \"\\\\n\".join(svg_lines)\n",
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" else:\n",
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" svg_with_title = svg_content\n",
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" \n",
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" # 保存文件\n",
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" with open(filename, \"w\") as f:\n",
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" f.write(svg_with_title)\n",
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" \n",
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" return svg_content"
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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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"## 数据加载和分子筛选\n",
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"\n",
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"### 数据源\n",
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"- 文件位置：`data/drug_targetmol/0c04ffc9fe8c2ec916412fbdc2a49bf4.sdf`\n",
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"- 包含药物分子结构和丰富属性信息\n",
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"\n",
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"### 筛选逻辑\n",
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"1. 读取SDF文件\n",
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"2. 对每个分子进行SMARTS匹配\n",
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"3. 记录匹配的原子和匹配数量\n",
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"4. 保存匹配结果到CSV\n",
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"5. 生成高亮可视化图片"
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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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"execution": {
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||
"iopub.execute_input": "2025-11-11T13:21:33.114695Z",
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"iopub.status.busy": "2025-11-11T13:21:33.113063Z",
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"iopub.status.idle": "2025-11-11T13:21:35.754026Z",
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"shell.execute_reply": "2025-11-11T13:21:35.745369Z"
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}
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},
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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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"正在读取SDF文件...\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[21:21:34] Both bonds on one end of an atropisomer are on the same side - atoms is : 3\n",
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"[21:21:34] Explicit valence for atom # 2 N greater than permitted\n",
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"[21:21:34] ERROR: Could not sanitize molecule ending on line 217340\n",
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"[21:21:34] ERROR: Explicit valence for atom # 2 N greater than permitted\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"[21:21:35] Explicit valence for atom # 4 N greater than permitted\n",
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"[21:21:35] ERROR: Could not sanitize molecule ending on line 317283\n",
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"[21:21:35] ERROR: Explicit valence for atom # 4 N greater than permitted\n",
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"[21:21:35] Explicit valence for atom # 4 N greater than permitted\n",
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"[21:21:35] ERROR: Could not sanitize molecule ending on line 324666\n",
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"[21:21:35] ERROR: Explicit valence for atom # 4 N greater than permitted\n",
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"[21:21:35] Explicit valence for atom # 5 N greater than permitted\n",
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"[21:21:35] ERROR: Could not sanitize molecule ending on line 365883\n",
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"[21:21:35] ERROR: Explicit valence for atom # 5 N greater than permitted\n"
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]
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},
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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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"成功加载 3276 个分子\n",
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"\n",
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"数据概览：\n",
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" Index Plate Row Col ID Name \\\n",
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"0 1 L1010-1 a 2 Dexamethasone \n",
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"1 2 L1010-1 a 3 Danicopan \n",
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"2 3 L1010-1 a 4 Cyclosporin A \n",
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"3 4 L1010-1 a 5 L-Carnitine \n",
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"4 5 L1010-1 a 6 Trimetazidine dihydrochloride \n",
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"\n",
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" Synonyms CAS \\\n",
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"0 MK 125;Prednisolone F;NSC 34521;Hexadecadrol 50-02-2 \n",
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"1 ACH-4471 1903768-17-1 \n",
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"2 Cyclosporine A;Ciclosporin;Cyclosporine 59865-13-3 \n",
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"3 L(-)-Carnitine;Levocarnitine 541-15-1 \n",
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"4 Yoshimilon;Kyurinett;Vastarel F 13171-25-0 \n",
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"\n",
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" SMILES \\\n",
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"0 C[C@@H]1C[C@H]2[C@@H]3CCC4=CC(=O)C=C[C@]4(C)[C@@]3(F)[C@@H](O)C[C@]2(C)[C@@]1(O)C(=O)CO \n",
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"1 CC(=O)c1nn(CC(=O)N2C[C@H](F)C[C@H]2C(=O)Nc2cccc(Br)n2)c2ccc(cc12)-c1cnc(C)nc1 \n",
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"2 [C@H]([C@@H](C/C=C/C)C)(O)[C@@]1(N(C)C(=O)[C@H]([C@@H](C)C)N(C)C(=O)[C@H](CC(C)C)N(C)C(=O)[C@H](... \n",
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"3 C[N+](C)(C)C[C@@H](O)CC([O-])=O \n",
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"4 Cl.Cl.COC1=C(OC)C(OC)=C(CN2CCNCC2)C=C1 \n",
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"\n",
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" Formula MolWt Approved status \\\n",
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"0 C22H29FO5 392.46 NMPA;EMA;FDA \n",
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"1 C26H23BrFN7O3 580.41 FDA \n",
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"2 C62H111N11O12 1202.61 FDA \n",
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"3 C7H15NO3 161.2 FDA \n",
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"4 C14H24Cl2N2O3 339.258 NMPA;EMA \n",
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"\n",
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" Pharmacopoeia \\\n",
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"0 USP39-NF34;BP2015;JP16;IP2010 \n",
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"1 NaN \n",
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"2 Martindale the Extra Pharmacopoei, EP10.2, USP43-NF38, Ph.Int_6th, JP17 \n",
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"3 NaN \n",
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"4 BP2019;KP Ⅹ;EP9.2;IP2010;JP17;Martindale:The Extra Pharmacopoeia \n",
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"\n",
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" Disease \\\n",
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"0 Metabolism \n",
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"1 Others \n",
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"2 Immune system \n",
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"3 Cardiovascular system \n",
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"4 Cardiovascular system \n",
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"\n",
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" Pathways \\\n",
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"0 Antibody-drug Conjugate/ADC Related;Autophagy;Endocrinology/Hormones;Immunology/Inflammation;Mic... \n",
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"1 Immunology/Inflammation \n",
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"2 Immunology/Inflammation;Metabolism;Microbiology/Virology \n",
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"3 Metabolism \n",
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"4 Autophagy;Metabolism \n",
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"\n",
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" Target \\\n",
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"0 Antibacterial;Antibiotic;Autophagy;Complement System;Glucocorticoid Receptor;IL Receptor;Mitopha... \n",
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"1 Complement System \n",
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"2 Phosphatase;Antibiotic;Complement System \n",
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"3 Endogenous Metabolite;Fatty Acid Synthase \n",
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"4 Autophagy;Fatty Acid Synthase \n",
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"\n",
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" Receptor \\\n",
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"0 Antibiotic; Autophagy; Bacterial; Complement System; Glucocorticoid Receptor; IL receptor; Mitop... \n",
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"1 Complement System; factor D \n",
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"2 Antibiotic; calcineurin phosphatase; Complement System; Phosphatase \n",
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"3 Endogenous Metabolite; FAS \n",
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"4 Autophagy; mitochondrial long-chain 3-ketoacyl thiolase \n",
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"\n",
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" Bioactivity \\\n",
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"0 Dexamethasone is a glucocorticoid receptor agonist and IL receptor modulator with anti-inflammat... \n",
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"1 Danicopan (ACH-4471) (ACH-4471) is a selective, orally active small molecule factor D inhibitor ... \n",
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"2 Cyclosporin A is a natural product and an active fungal metabolite, classified as a cyclic polyp... \n",
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"3 L-Carnitine (L(-)-Carnitine) is an amino acid derivative. L-Carnitine facilitates long-chain fat... \n",
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"4 Trimetazidine dihydrochloride (Vastarel F) can improve myocardial glucose utilization by inhibit... \n",
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"\n",
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" Reference \\\n",
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"0 Li M, Yu H. Identification of WP1066, an inhibitor of JAK2 and STAT3, as a Kv1. 3 potassium chan... \n",
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"1 Yuan X, et al. Small-molecule factor D inhibitors selectively block the alternative pathway of c... \n",
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"2 D'Angelo G, et al. Cyclosporin A prevents the hypoxic adaptation by activating hypoxia-inducible... \n",
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"3 Jogl G, Tong L. Cell. 2003 Jan 10; 112(1):113-22. \n",
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"4 Yang Q, et al. Int J Clin Exp Pathol. 2015, 8(4):3735-3741.;Liu Z, et al. Metabolism. 2016, 65(3... \n",
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"\n",
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" ROMol \n",
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"0 <rdkit.Chem.rdchem.Mol object at 0x774684c557e0> \n",
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"1 <rdkit.Chem.rdchem.Mol object at 0x7746818ffdf0> \n",
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"2 <rdkit.Chem.rdchem.Mol object at 0x7746818ffd80> \n",
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"3 <rdkit.Chem.rdchem.Mol object at 0x7746816e0040> \n",
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"4 <rdkit.Chem.rdchem.Mol object at 0x7746816e00b0> \n",
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"\n",
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"列名：['Index', 'Plate', 'Row', 'Col', 'ID', 'Name', 'Synonyms', 'CAS', 'SMILES', 'Formula', 'MolWt', 'Approved status', 'Pharmacopoeia', 'Disease', 'Pathways', 'Target', 'Receptor', 'Bioactivity', 'Reference', 'ROMol']\n"
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]
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}
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],
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"source": [
|
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"# 读取SDF文件\n",
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"sdf_path = '../data/drug_targetmol/0c04ffc9fe8c2ec916412fbdc2a49bf4.sdf'\n",
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"\n",
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"print(\"正在读取SDF文件...\")\n",
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"df = PandasTools.LoadSDF(sdf_path)\n",
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"print(f\"成功加载 {len(df)} 个分子\")\n",
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"\n",
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"# 显示数据基本信息\n",
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"print(\"\\n数据概览：\")\n",
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"print(df.head())\n",
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"print(f\"\\n列名：{list(df.columns)}\")"
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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": 4,
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-11-11T13:21:35.770585Z",
|
||
"iopub.status.busy": "2025-11-11T13:21:35.768752Z",
|
||
"iopub.status.idle": "2025-11-11T13:21:36.114723Z",
|
||
"shell.execute_reply": "2025-11-11T13:21:36.111467Z"
|
||
}
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||
},
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"outputs": [
|
||
{
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||
"name": "stdout",
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"output_type": "stream",
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"text": [
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"开始筛选芳香胺结构...\n",
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"SMARTS模式: [c,n][N&H2]\n"
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]
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},
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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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"找到 78 个匹配分子（处理了 1000 个分子）\n",
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"\n",
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"筛选结果摘要：\n",
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" Name CAS Formula total_matches\n",
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"17 Guanosine 118-00-3 C10H13N5O5 1\n",
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"20 Ganciclovir 82410-32-0 C9H13N5O4 1\n",
|
||
"22 Imiquimod maleate 896106-16-4 C18H20N4O4 1\n",
|
||
"27 Brincidofovir 444805-28-1 C27H52N3O7P 1\n",
|
||
"28 Imiquimod 99011-02-6 C14H16N4 1\n",
|
||
"32 Ganciclovir sodium 107910-75-8 C9H13N5NaO4 1\n",
|
||
"33 Cytarabine 147-94-4 C9H13N3O5 1\n",
|
||
"35 Vidarabine 5536-17-4 C10H13N5O4 1\n",
|
||
"38 Penciclovir 39809-25-1 C10H15N5O3 1\n",
|
||
"41 Famciclovir 104227-87-4 C14H19N5O4 1\n",
|
||
"... 还有 68 个分子\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"def screen_molecules_for_aniline(df, smarts_pattern, max_molecules=100):\n",
|
||
" \"\"\"\n",
|
||
" 筛选包含芳香胺结构的分子\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" df: 包含分子的DataFrame\n",
|
||
" smarts_pattern: RDKit SMARTS模式对象\n",
|
||
" max_molecules: 最大处理分子数量\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" 筛选结果DataFrame\n",
|
||
" \"\"\"\n",
|
||
" print(f\"开始筛选芳香胺结构...\")\n",
|
||
" print(f\"SMARTS模式: {Chem.MolToSmarts(smarts_pattern)}\")\n",
|
||
" \n",
|
||
" matched_molecules = []\n",
|
||
" processed_count = 0\n",
|
||
" \n",
|
||
" for idx, row in df.iterrows():\n",
|
||
" if processed_count >= max_molecules:\n",
|
||
" break\n",
|
||
" \n",
|
||
" mol = row['ROMol']\n",
|
||
" if mol is None:\n",
|
||
" continue\n",
|
||
" \n",
|
||
" processed_count += 1\n",
|
||
" \n",
|
||
" # 检查是否匹配SMARTS模式\n",
|
||
" if mol.HasSubstructMatch(smarts_pattern):\n",
|
||
" matches = mol.GetSubstructMatches(smarts_pattern)\n",
|
||
" \n",
|
||
" # 收集所有匹配的原子\n",
|
||
" matched_atoms = set()\n",
|
||
" for match in matches:\n",
|
||
" matched_atoms.update(match)\n",
|
||
" \n",
|
||
" # 创建匹配记录\n",
|
||
" match_record = row.copy()\n",
|
||
" match_record['matched_atoms'] = list(matched_atoms)\n",
|
||
" match_record['total_matches'] = len(matches)\n",
|
||
" match_record['smarts_pattern'] = Chem.MolToSmarts(smarts_pattern)\n",
|
||
" matched_molecules.append(match_record)\n",
|
||
" \n",
|
||
" result_df = pd.DataFrame(matched_molecules)\n",
|
||
" print(f\"找到 {len(result_df)} 个匹配分子（处理了 {processed_count} 个分子）\")\n",
|
||
" \n",
|
||
" return result_df\n",
|
||
"\n",
|
||
"# 执行筛选\n",
|
||
"matched_df = screen_molecules_for_aniline(df, pattern, max_molecules=1000)\n",
|
||
"\n",
|
||
"# 显示结果摘要\n",
|
||
"if len(matched_df) > 0:\n",
|
||
" print(\"\\n筛选结果摘要：\")\n",
|
||
" summary_cols = ['Name', 'CAS', 'Formula', 'total_matches']\n",
|
||
" if len(matched_df) <= 10:\n",
|
||
" print(matched_df[summary_cols])\n",
|
||
" else:\n",
|
||
" print(matched_df[summary_cols].head(10))\n",
|
||
" print(f\"... 还有 {len(matched_df) - 10} 个分子\")\n",
|
||
"else:\n",
|
||
" print(\"\\n未找到匹配分子\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 保存筛选结果\n",
|
||
"\n",
|
||
"### 输出文件\n",
|
||
"1. **CSV文件**：包含所有匹配分子的属性信息和匹配详情\n",
|
||
"2. **SVG图片**：每个匹配分子的结构可视化，高亮芳香胺结构"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-11-11T13:21:36.120981Z",
|
||
"iopub.status.busy": "2025-11-11T13:21:36.120553Z",
|
||
"iopub.status.idle": "2025-11-11T13:21:36.279125Z",
|
||
"shell.execute_reply": "2025-11-11T13:21:36.277892Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"CSV结果已保存到：../data/drug_targetmol/aniline_candidates/aniline_candidates.csv\n",
|
||
"包含 78 个分子，23 个属性列\n",
|
||
"\n",
|
||
"开始生成可视化图片（最多50个）...\n",
|
||
"已生成 10 个分子图片\n",
|
||
"已生成 20 个分子图片\n",
|
||
"已生成 30 个分子图片\n",
|
||
"已生成 40 个分子图片\n",
|
||
"已生成 50 个分子图片\n",
|
||
"已达到最大可视化数量限制 (50)，停止生成\n",
|
||
"完成！共生成 50 个可视化图片\n",
|
||
"\n",
|
||
"示例图片: 118-00-3_Guanosine.svg\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/svg+xml": [
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|
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"<path class=\"atom-19\" d=\"M 1015.3 406.3 L 1018.1 410.8 Q 1018.4 411.2, 1018.8 412.0 Q 1019.3 412.8, 1019.3 412.9 L 1019.3 406.3 L 1020.4 406.3 L 1020.4 414.8 L 1019.3 414.8 L 1016.3 409.8 Q 1015.9 409.3, 1015.6 408.6 Q 1015.2 407.9, 1015.1 407.7 L 1015.1 414.8 L 1014.0 414.8 L 1014.0 406.3 L 1015.3 406.3 \" fill=\"#0000FF\"/>\n",
|
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"<path class=\"atom-19\" d=\"M 1022.1 406.3 L 1023.2 406.3 L 1023.2 409.9 L 1027.6 409.9 L 1027.6 406.3 L 1028.7 406.3 L 1028.7 414.8 L 1027.6 414.8 L 1027.6 410.8 L 1023.2 410.8 L 1023.2 414.8 L 1022.1 414.8 L 1022.1 406.3 \" fill=\"#0000FF\"/>\n",
|
||
"</svg>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.SVG object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"def save_aniline_screening_results(df, output_dir, visualization_dir, max_visualizations=50):\n",
|
||
" \"\"\"保存芳香胺筛选结果\"\"\"\n",
|
||
" \n",
|
||
" # 保存CSV文件\n",
|
||
" csv_path = output_dir / \"aniline_candidates.csv\"\n",
|
||
" \n",
|
||
" # 转换ROMol列为SMILES（因为ROMol对象无法保存到CSV）\n",
|
||
" df_export = df.copy()\n",
|
||
" if 'ROMol' in df_export.columns:\n",
|
||
" df_export['SMILES_from_mol'] = df_export['ROMol'].apply(lambda x: Chem.MolToSmiles(x) if x else '')\n",
|
||
" df_export = df_export.drop('ROMol', axis=1)\n",
|
||
" \n",
|
||
" df_export.to_csv(csv_path, index=False, encoding='utf-8')\n",
|
||
" print(f\"CSV结果已保存到：{csv_path}\")\n",
|
||
" print(f\"包含 {len(df_export)} 个分子，{len(df_export.columns)} 个属性列\")\n",
|
||
" \n",
|
||
" # 生成可视化图片\n",
|
||
" print(f\"\\n开始生成可视化图片（最多{max_visualizations}个）...\")\n",
|
||
" generated_count = 0\n",
|
||
" \n",
|
||
" for idx, row in df.iterrows():\n",
|
||
" if generated_count >= max_visualizations:\n",
|
||
" print(f\"已达到最大可视化数量限制 ({max_visualizations})，停止生成\")\n",
|
||
" break\n",
|
||
" \n",
|
||
" cas = str(row.get('CAS', 'unknown')).strip()\n",
|
||
" name = str(row.get('Name', 'unknown')).strip()\n",
|
||
" \n",
|
||
" # 清理文件名（去除特殊字符）\n",
|
||
" safe_name = \"\".join(c for c in name if c.isalnum() or c in (' ', '-', '_')).rstrip()\n",
|
||
" safe_cas = \"\".join(c for c in cas if c.isalnum() or c in ('-',)).rstrip()\n",
|
||
" \n",
|
||
" # 跳过无效的标识符\n",
|
||
" if not safe_cas or safe_cas == 'nan' or safe_cas == 'unknown':\n",
|
||
" continue\n",
|
||
" \n",
|
||
" mol = row.get('ROMol')\n",
|
||
" if mol is None:\n",
|
||
" continue\n",
|
||
" \n",
|
||
" matched_atoms = row.get('matched_atoms', [])\n",
|
||
" if not matched_atoms:\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 生成文件名和标题\n",
|
||
" filename = visualization_dir / f\"{safe_cas}_{safe_name.replace(' ', '_')}.svg\"\n",
|
||
" title = f\"{name} ({cas}) - 芳香胺结构\"\n",
|
||
" \n",
|
||
" try:\n",
|
||
" # 生成SVG\n",
|
||
" svg_content = generate_highlighted_svg(mol, matched_atoms, filename, title)\n",
|
||
" generated_count += 1\n",
|
||
" \n",
|
||
" # 每10个显示一次进度\n",
|
||
" if generated_count % 10 == 0:\n",
|
||
" print(f\"已生成 {generated_count} 个分子图片\")\n",
|
||
" \n",
|
||
" except Exception as e:\n",
|
||
" print(f\"生成 {safe_cas} 失败: {e}\")\n",
|
||
" continue\n",
|
||
" \n",
|
||
" print(f\"完成！共生成 {generated_count} 个可视化图片\")\n",
|
||
" return csv_path, generated_count\n",
|
||
"\n",
|
||
"# 保存结果\n",
|
||
"if len(matched_df) > 0:\n",
|
||
" csv_path, viz_count = save_aniline_screening_results(\n",
|
||
" matched_df, output_dir, visualization_dir, max_visualizations=50\n",
|
||
" )\n",
|
||
" \n",
|
||
" # 显示第一个生成的图片作为示例\n",
|
||
" if viz_count > 0:\n",
|
||
" example_files = list(visualization_dir.glob(\"*.svg\"))\n",
|
||
" if example_files:\n",
|
||
" example_file = example_files[0]\n",
|
||
" print(f\"\\n示例图片: {example_file.name}\")\n",
|
||
" with open(example_file, \"r\") as f:\n",
|
||
" svg_content = f.read()\n",
|
||
" display(SVG(svg_content))\n",
|
||
"else:\n",
|
||
" print(\"没有匹配结果，无需保存\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 结果统计和分析\n",
|
||
"\n",
|
||
"### 筛选统计\n",
|
||
"- 总分子数\n",
|
||
"- 匹配分子数\n",
|
||
"- 可视化文件数量\n",
|
||
"- 输出文件位置"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2025-11-11T13:21:36.282118Z",
|
||
"iopub.status.busy": "2025-11-11T13:21:36.281886Z",
|
||
"iopub.status.idle": "2025-11-11T13:21:36.317857Z",
|
||
"shell.execute_reply": "2025-11-11T13:21:36.316621Z"
|
||
}
|
||
},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"=== 芳香胺筛选结果统计 ===\n",
|
||
"总分子数：3276\n",
|
||
"匹配分子数：78\n",
|
||
"匹配率：2.38%\n",
|
||
"\n",
|
||
"输出目录：../data/drug_targetmol/aniline_candidates\n",
|
||
"CSV文件：../data/drug_targetmol/aniline_candidates/aniline_candidates.csv\n",
|
||
"可视化目录：../data/drug_targetmol/aniline_candidates/visualizations\n",
|
||
"SVG文件数量：50\n",
|
||
"\n",
|
||
"匹配数量最多的分子：\n",
|
||
" Name CAS total_matches\n",
|
||
"432 Proflavine Hemisulfate 1811-28-5 4\n",
|
||
"335 Pemetrexed disodium hemipenta hydrate 357166-30-4 2\n",
|
||
"463 Lamotrigine 84057-84-1 2\n",
|
||
"779 Pyrimethamine 58-14-0 2\n",
|
||
"784 Dapsone 80-08-0 2\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 结果统计\n",
|
||
"print(\"=== 芳香胺筛选结果统计 ===\")\n",
|
||
"print(f\"总分子数：{len(df)}\")\n",
|
||
"print(f\"匹配分子数：{len(matched_df)}\")\n",
|
||
"print(f\"匹配率：{len(matched_df)/len(df)*100:.2f}%\")\n",
|
||
"print(f\"\\n输出目录：{output_dir}\")\n",
|
||
"print(f\"CSV文件：{output_dir}/aniline_candidates.csv\")\n",
|
||
"print(f\"可视化目录：{visualization_dir}\")\n",
|
||
"print(f\"SVG文件数量：{len(list(visualization_dir.glob('*.svg')))}\")\n",
|
||
"\n",
|
||
"# 显示匹配最多的前几个分子\n",
|
||
"if len(matched_df) > 0:\n",
|
||
" print(\"\\n匹配数量最多的分子：\")\n",
|
||
" top_matches = matched_df.nlargest(5, 'total_matches')[['Name', 'CAS', 'total_matches']]\n",
|
||
" print(top_matches)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 使用建议\n",
|
||
"\n",
|
||
"### 筛选结果解读\n",
|
||
"- **匹配分子**：包含芳香胺结构（Ar-NH₂）的药物\n",
|
||
"- **蓝色高亮**：匹配的SMARTS结构（芳香碳/氮 + 氨基）\n",
|
||
"- **多重匹配**：分子中可能存在多个芳香胺基团\n",
|
||
"\n",
|
||
"### 后续分析建议\n",
|
||
"1. **合成路线验证**：查阅匹配分子的合成文献\n",
|
||
"2. **Sandmeyer反应确认**：确认是否使用Sandmeyer反应引入卤素\n",
|
||
"3. **张夏恒反应评估**：评估替代Sandmeyer反应的可行性\n",
|
||
"4. **工艺优化潜力**：分析替换为张夏恒反应的经济效益\n",
|
||
"\n",
|
||
"### 文件说明\n",
|
||
"- **CSV文件**：完整的分子属性和匹配信息\n",
|
||
"- **SVG文件**：结构可视化，蓝色高亮芳香胺结构\n",
|
||
"- **命名规则**：{CAS}_{Name}.svg（特殊字符已清理）"
|
||
]
|
||
}
|
||
],
|
||
"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": 4
|
||
}
|