3323 lines
744 KiB
Plaintext
3323 lines
744 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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"# 测试两个分子的对齐\n",
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"\n",
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"目标：对齐 ring16_mol_1109 和 ring16_mol_1110 的酯基原子\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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"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 rdMolAlign\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 numpy as np\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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"加载分子1: /home/zly/project/macro_split/output/ring16_fragments/ring16_mol_1109/ring16_mol_1109_2d.sdf\n",
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"加载分子2: /home/zly/project/macro_split/output/ring16_fragments/ring16_mol_1110/ring16_mol_1110_2d.sdf\n",
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"\n",
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"分子1: 44 个原子\n",
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"分子2: 41 个原子\n"
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]
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}
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],
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"source": [
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"# 加载两个分子的2D SDF文件\n",
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"sdf1_path = project_root / \"output\" / \"ring16_fragments\" / \"ring16_mol_1109\" / \"ring16_mol_1109_2d.sdf\"\n",
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"sdf2_path = project_root / \"output\" / \"ring16_fragments\" / \"ring16_mol_1110\" / \"ring16_mol_1110_2d.sdf\"\n",
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"\n",
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"print(f\"加载分子1: {sdf1_path}\")\n",
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"print(f\"加载分子2: {sdf2_path}\")\n",
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"\n",
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"supplier1 = Chem.SDMolSupplier(str(sdf1_path))\n",
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"mol1 = supplier1[0]\n",
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"\n",
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"supplier2 = Chem.SDMolSupplier(str(sdf2_path))\n",
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"mol2 = supplier2[0]\n",
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"\n",
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"print(f\"\\n分子1: {mol1.GetNumAtoms()} 个原子\")\n",
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"print(f\"分子2: {mol2.GetNumAtoms()} 个原子\")\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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"data": {
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"image/png": 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",
|
||
"text/plain": [
|
||
"<IPython.core.display.Image object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 显示两个分子\n",
|
||
"from rdkit.Chem import AllChem\n",
|
||
"\n",
|
||
"# 确保有2D坐标\n",
|
||
"AllChem.Compute2DCoords(mol1)\n",
|
||
"AllChem.Compute2DCoords(mol2)\n",
|
||
"\n",
|
||
"img = Draw.MolsToGridImage([mol1, mol2], molsPerRow=2, subImgSize=(600, 400), legends=[\"mol_1109\", \"mol_1110\"])\n",
|
||
"display(img)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"分子1的环信息:\n",
|
||
" 6元环: 1 个\n",
|
||
" 16元环: 1 个\n",
|
||
"\n",
|
||
"分子2的环信息:\n",
|
||
" 5元环: 1 个\n",
|
||
" 6元环: 1 个\n",
|
||
" 16元环: 1 个\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 步骤1: 检查环的大小\n",
|
||
"# 找到所有环\n",
|
||
"def get_ring_info(mol):\n",
|
||
" \"\"\"获取分子的环信息\"\"\"\n",
|
||
" ring_info = mol.GetRingInfo()\n",
|
||
" rings = ring_info.AtomRings()\n",
|
||
" \n",
|
||
" ring_sizes = {}\n",
|
||
" for ring in rings:\n",
|
||
" size = len(ring)\n",
|
||
" if size not in ring_sizes:\n",
|
||
" ring_sizes[size] = []\n",
|
||
" ring_sizes[size].append(ring)\n",
|
||
" \n",
|
||
" return ring_sizes\n",
|
||
"\n",
|
||
"ring_info1 = get_ring_info(mol1)\n",
|
||
"ring_info2 = get_ring_info(mol2)\n",
|
||
"\n",
|
||
"print(\"分子1的环信息:\")\n",
|
||
"for size, rings in sorted(ring_info1.items()):\n",
|
||
" print(f\" {size}元环: {len(rings)} 个\")\n",
|
||
"\n",
|
||
"print(\"\\n分子2的环信息:\")\n",
|
||
"for size, rings in sorted(ring_info2.items()):\n",
|
||
" print(f\" {size}元环: {len(rings)} 个\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"分子1的16元环原子: [5, 41, 40, 38, 37, 35, 33, 19, 16, 15, 13, 11, 10, 9, 7, 6]\n",
|
||
"分子2的16元环原子: [10, 11, 12, 13, 14, 15, 16, 26, 28, 30, 32, 35, 37, 38, 40, 9]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 步骤2: 找到16元环的原子\n",
|
||
"def get_ring16_atoms(mol):\n",
|
||
" \"\"\"找到16元环的原子索引\"\"\"\n",
|
||
" ring_info = mol.GetRingInfo()\n",
|
||
" rings = ring_info.AtomRings()\n",
|
||
" \n",
|
||
" ring16_atoms = None\n",
|
||
" for ring in rings:\n",
|
||
" if len(ring) == 16:\n",
|
||
" ring16_atoms = list(ring)\n",
|
||
" break\n",
|
||
" \n",
|
||
" return ring16_atoms\n",
|
||
"\n",
|
||
"ring16_atoms1 = get_ring16_atoms(mol1)\n",
|
||
"ring16_atoms2 = get_ring16_atoms(mol2)\n",
|
||
"\n",
|
||
"print(f\"分子1的16元环原子: {ring16_atoms1}\")\n",
|
||
"print(f\"分子2的16元环原子: {ring16_atoms2}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"测试分子1的酯基匹配:\n",
|
||
" [r16][C](=O)[O] : 找到匹配: (37, 38, 39, 40)\n",
|
||
" 原子索引: [37, 38, 39, 40]\n",
|
||
" [r16][C](=O)O : 找到匹配: (37, 38, 39, 40)\n",
|
||
" 原子索引: [37, 38, 39, 40]\n",
|
||
" [C](=O)[O] : 找到匹配: (38, 39, 40)\n",
|
||
" 原子索引: [38, 39, 40]\n",
|
||
" [C](=O)O : 找到匹配: (38, 39, 40)\n",
|
||
" 原子索引: [38, 39, 40]\n",
|
||
" C(=O)O : 找到匹配: (38, 39, 40)\n",
|
||
" 原子索引: [38, 39, 40]\n",
|
||
"\n",
|
||
"测试分子2的酯基匹配:\n",
|
||
" [r16][C](=O)[O] : 找到匹配: (37, 38, 39, 40)\n",
|
||
" 原子索引: [37, 38, 39, 40]\n",
|
||
" [r16][C](=O)O : 找到匹配: (37, 38, 39, 40)\n",
|
||
" 原子索引: [37, 38, 39, 40]\n",
|
||
" [C](=O)[O] : 找到匹配: (38, 39, 40)\n",
|
||
" 原子索引: [38, 39, 40]\n",
|
||
" [C](=O)O : 找到匹配: (38, 39, 40)\n",
|
||
" 原子索引: [38, 39, 40]\n",
|
||
" C(=O)O : 找到匹配: (38, 39, 40)\n",
|
||
" 原子索引: [38, 39, 40]\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 步骤3: 尝试不同的SMARTS模式来找到酯基\n",
|
||
"# 测试多个SMARTS模式\n",
|
||
"ester_patterns = [\n",
|
||
" \"[r16][C](=O)[O]\", # 原始模式\n",
|
||
" \"[r16][C](=O)O\", # 简化\n",
|
||
" \"[C](=O)[O]\", # 不指定环大小\n",
|
||
" \"[C](=O)O\", # 更简单\n",
|
||
" \"C(=O)O\", # 最简单\n",
|
||
"]\n",
|
||
"\n",
|
||
"def find_ester_atoms_with_pattern(mol, pattern_str, ring_atoms=None):\n",
|
||
" \"\"\"使用SMARTS模式找到酯基原子\"\"\"\n",
|
||
" pattern = Chem.MolFromSmarts(pattern_str)\n",
|
||
" if pattern is None:\n",
|
||
" return None, f\"无效的SMARTS模式: {pattern_str}\"\n",
|
||
" \n",
|
||
" matches = mol.GetSubstructMatches(pattern)\n",
|
||
" if len(matches) == 0:\n",
|
||
" return None, f\"未找到匹配\"\n",
|
||
" \n",
|
||
" # 如果指定了环原子，筛选出在环上的酯基\n",
|
||
" if ring_atoms is not None:\n",
|
||
" ring_atoms_set = set(ring_atoms)\n",
|
||
" for match in matches:\n",
|
||
" # 检查第一个原子是否在环上\n",
|
||
" if match[0] in ring_atoms_set:\n",
|
||
" return list(match), f\"找到匹配: {match}\"\n",
|
||
" return None, f\"找到{len(matches)}个匹配，但都不在16元环上\"\n",
|
||
" \n",
|
||
" return list(matches[0]), f\"找到匹配: {matches[0]}\"\n",
|
||
"\n",
|
||
"print(\"测试分子1的酯基匹配:\")\n",
|
||
"for pattern in ester_patterns:\n",
|
||
" result, msg = find_ester_atoms_with_pattern(mol1, pattern, ring16_atoms1)\n",
|
||
" print(f\" {pattern:20s}: {msg}\")\n",
|
||
" if result is not None:\n",
|
||
" print(f\" 原子索引: {result}\")\n",
|
||
"\n",
|
||
"print(\"\\n测试分子2的酯基匹配:\")\n",
|
||
"for pattern in ester_patterns:\n",
|
||
" result, msg = find_ester_atoms_with_pattern(mol2, pattern, ring16_atoms2)\n",
|
||
" print(f\" {pattern:20s}: {msg}\")\n",
|
||
" if result is not None:\n",
|
||
" print(f\" 原子索引: {result}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"分子1在16元环上的酯基候选: []\n",
|
||
"分子2在16元环上的酯基候选: []\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 步骤4: 手动检查酯基结构\n",
|
||
"# 在16元环上查找C=O和O的连接\n",
|
||
"def find_ester_in_ring(mol, ring_atoms):\n",
|
||
" \"\"\"在环上查找酯基\"\"\"\n",
|
||
" ring_atoms_set = set(ring_atoms)\n",
|
||
" \n",
|
||
" # 查找所有C=O键\n",
|
||
" ester_candidates = []\n",
|
||
" \n",
|
||
" for atom_idx in ring_atoms:\n",
|
||
" atom = mol.GetAtomWithIdx(atom_idx)\n",
|
||
" if atom.GetSymbol() == 'C':\n",
|
||
" # 检查这个C是否有双键到O\n",
|
||
" for neighbor in atom.GetNeighbors():\n",
|
||
" neighbor_idx = neighbor.GetIdx()\n",
|
||
" bond = mol.GetBondBetweenAtoms(atom_idx, neighbor_idx)\n",
|
||
" if bond.GetBondType() == Chem.BondType.DOUBLE and neighbor.GetSymbol() == 'O':\n",
|
||
" # 找到C=O，现在找连接的O\n",
|
||
" for neighbor2 in neighbor.GetNeighbors():\n",
|
||
" neighbor2_idx = neighbor2.GetIdx()\n",
|
||
" if neighbor2_idx != atom_idx:\n",
|
||
" # 找到酯基: [环上的C, =O的C, O]\n",
|
||
" ester_candidates.append((atom_idx, neighbor_idx, neighbor2_idx))\n",
|
||
" \n",
|
||
" return ester_candidates\n",
|
||
"\n",
|
||
"ester1 = find_ester_in_ring(mol1, ring16_atoms1)\n",
|
||
"ester2 = find_ester_in_ring(mol2, ring16_atoms2)\n",
|
||
"\n",
|
||
"print(f\"分子1在16元环上的酯基候选: {ester1}\")\n",
|
||
"print(f\"分子2在16元环上的酯基候选: {ester2}\")\n",
|
||
"\n",
|
||
"# 显示酯基原子的符号\n",
|
||
"if ester1:\n",
|
||
" print(\"\\n分子1的酯基原子:\")\n",
|
||
" for i, idx in enumerate(ester1[0]):\n",
|
||
" atom = mol1.GetAtomWithIdx(idx)\n",
|
||
" print(f\" 原子{i+1} (索引{idx}): {atom.GetSymbol()}\")\n",
|
||
"\n",
|
||
"if ester2:\n",
|
||
" print(\"\\n分子2的酯基原子:\")\n",
|
||
" for i, idx in enumerate(ester2[0]):\n",
|
||
" atom = mol2.GetAtomWithIdx(idx)\n",
|
||
" print(f\" 原子{i+1} (索引{idx}): {atom.GetSymbol()}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"分子1的酯基原子: [37, 38, 39, 40] (模式: [r16][C](=O)[O])\n",
|
||
"分子2的酯基原子: [37, 38, 39, 40] (模式: [r16][C](=O)[O])\n",
|
||
"\n",
|
||
"分子1酯基原子详情:\n",
|
||
" 索引37: C, 度=2, 在环上=True\n",
|
||
" 索引38: C, 度=3, 在环上=True\n",
|
||
" 索引39: O, 度=1, 在环上=False\n",
|
||
" 索引40: O, 度=2, 在环上=True\n",
|
||
"\n",
|
||
"分子2酯基原子详情:\n",
|
||
" 索引37: C, 度=2, 在环上=True\n",
|
||
" 索引38: C, 度=3, 在环上=True\n",
|
||
" 索引39: O, 度=1, 在环上=False\n",
|
||
" 索引40: O, 度=2, 在环上=True\n",
|
||
"\n",
|
||
"分子1羰基C原子索引: 38\n",
|
||
"分子1酯键O原子索引: 40\n",
|
||
"\n",
|
||
"分子2羰基C原子索引: 38\n",
|
||
"分子2酯键O原子索引: 40\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 步骤5: 使用正确的SMARTS模式\n",
|
||
"# 根据实际结构，酯基应该是: 环上的C(=O)O\n",
|
||
"# 让我们尝试更准确的SMARTS\n",
|
||
"\n",
|
||
"def find_ester_smarts(mol, ring_atoms):\n",
|
||
" \"\"\"使用SMARTS找到环上的酯基\"\"\"\n",
|
||
" ring_atoms_set = set(ring_atoms)\n",
|
||
" \n",
|
||
" # 尝试不同的SMARTS模式\n",
|
||
" patterns = [\n",
|
||
" \"[r16][C](=O)[O]\", # 原始\n",
|
||
" \"[r16]C(=O)O\", # 简化\n",
|
||
" \"C(=O)O\", # 不指定环\n",
|
||
" ]\n",
|
||
" \n",
|
||
" for pattern_str in patterns:\n",
|
||
" pattern = Chem.MolFromSmarts(pattern_str)\n",
|
||
" if pattern is None:\n",
|
||
" continue\n",
|
||
" \n",
|
||
" matches = mol.GetSubstructMatches(pattern)\n",
|
||
" for match in matches:\n",
|
||
" # 检查第一个原子是否在16元环上\n",
|
||
" if match[0] in ring_atoms_set:\n",
|
||
" return list(match), pattern_str\n",
|
||
" \n",
|
||
" return None, None\n",
|
||
"\n",
|
||
"ester_atoms1, pattern1 = find_ester_smarts(mol1, ring16_atoms1)\n",
|
||
"ester_atoms2, pattern2 = find_ester_smarts(mol2, ring16_atoms2)\n",
|
||
"\n",
|
||
"print(f\"分子1的酯基原子: {ester_atoms1} (模式: {pattern1})\")\n",
|
||
"print(f\"分子2的酯基原子: {ester_atoms2} (模式: {pattern2})\")\n",
|
||
"\n",
|
||
"# 如果找到了，显示原子信息\n",
|
||
"if ester_atoms1:\n",
|
||
" print(\"\\n分子1酯基原子详情:\")\n",
|
||
" for idx in ester_atoms1:\n",
|
||
" atom = mol1.GetAtomWithIdx(idx)\n",
|
||
" print(f\" 索引{idx}: {atom.GetSymbol()}, 度={atom.GetDegree()}, 在环上={idx in ring16_atoms1}\")\n",
|
||
"\n",
|
||
"if ester_atoms2:\n",
|
||
" print(\"\\n分子2酯基原子详情:\")\n",
|
||
" for idx in ester_atoms2:\n",
|
||
" atom = mol2.GetAtomWithIdx(idx)\n",
|
||
" print(f\" 索引{idx}: {atom.GetSymbol()}, 度={atom.GetDegree()}, 在环上={idx in ring16_atoms2}\")\n",
|
||
"\n",
|
||
"# 找到C=O中的羰基C原子（在环上，有双键O，度=3）\n",
|
||
"def get_carbonyl_carbon_in_ring(mol, ester_atoms, ring_atoms):\n",
|
||
" \"\"\"找到C=O中的羰基C原子（在环上，有双键O）\"\"\"\n",
|
||
" ring_atoms_set = set(ring_atoms)\n",
|
||
" ester_atoms_set = set(ester_atoms)\n",
|
||
" \n",
|
||
" for idx in ester_atoms:\n",
|
||
" atom = mol.GetAtomWithIdx(idx)\n",
|
||
" # 找到在环上的C原子\n",
|
||
" if atom.GetSymbol() == 'C' and idx in ring_atoms_set:\n",
|
||
" # 检查这个C是否有双键到O（羰基特征）\n",
|
||
" for neighbor in atom.GetNeighbors():\n",
|
||
" neighbor_idx = neighbor.GetIdx()\n",
|
||
" bond = mol.GetBondBetweenAtoms(idx, neighbor_idx)\n",
|
||
" if bond.GetBondType() == Chem.BondType.DOUBLE and neighbor.GetSymbol() == 'O':\n",
|
||
" # 找到羰基C\n",
|
||
" return idx\n",
|
||
" return None\n",
|
||
"\n",
|
||
"# 找到酯键另一边的O原子（在环上）\n",
|
||
"def get_ester_oxygen_in_ring(mol, ester_atoms, ring_atoms):\n",
|
||
" \"\"\"找到酯键另一边的O原子（在环上）\"\"\"\n",
|
||
" ring_atoms_set = set(ring_atoms)\n",
|
||
" for idx in ester_atoms:\n",
|
||
" atom = mol.GetAtomWithIdx(idx)\n",
|
||
" if atom.GetSymbol() == 'O' and idx in ring_atoms_set:\n",
|
||
" return idx\n",
|
||
" return None\n",
|
||
"\n",
|
||
"carbonyl_carbon1 = get_carbonyl_carbon_in_ring(mol1, ester_atoms1, ring16_atoms1)\n",
|
||
"carbonyl_carbon2 = get_carbonyl_carbon_in_ring(mol2, ester_atoms2, ring16_atoms2)\n",
|
||
"\n",
|
||
"ester_oxygen1 = get_ester_oxygen_in_ring(mol1, ester_atoms1, ring16_atoms1)\n",
|
||
"ester_oxygen2 = get_ester_oxygen_in_ring(mol2, ester_atoms2, ring16_atoms2)\n",
|
||
"\n",
|
||
"print(f\"\\n分子1羰基C原子索引: {carbonyl_carbon1}\")\n",
|
||
"print(f\"分子1酯键O原子索引: {ester_oxygen1}\")\n",
|
||
"print(f\"\\n分子2羰基C原子索引: {carbonyl_carbon2}\")\n",
|
||
"print(f\"分子2酯键O原子索引: {ester_oxygen2}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"分子1的环上原子编号（从C=O羰基C原子开始）:\n",
|
||
" 位置1: 原子索引38 (C)\n",
|
||
" 位置2: 原子索引40 (O)\n",
|
||
" 位置3: 原子索引37 (C)\n",
|
||
" 位置4: 原子索引35 (C)\n",
|
||
" 位置5: 原子索引33 (C)\n",
|
||
" 位置6: 原子索引19 (C)\n",
|
||
" 位置7: 原子索引16 (C)\n",
|
||
" 位置8: 原子索引15 (C)\n",
|
||
" 位置9: 原子索引13 (C)\n",
|
||
" 位置10: 原子索引11 (C)\n",
|
||
" 位置11: 原子索引10 (C)\n",
|
||
" 位置12: 原子索引9 (C)\n",
|
||
" 位置13: 原子索引7 (C)\n",
|
||
" 位置14: 原子索引6 (C)\n",
|
||
" 位置15: 原子索引5 (C)\n",
|
||
" 位置16: 原子索引41 (C)\n",
|
||
"\n",
|
||
"分子2的环上原子编号（从C=O羰基C原子开始）:\n",
|
||
" 位置1: 原子索引38 (C)\n",
|
||
" 位置2: 原子索引40 (O)\n",
|
||
" 位置3: 原子索引37 (C)\n",
|
||
" 位置4: 原子索引35 (C)\n",
|
||
" 位置5: 原子索引32 (C)\n",
|
||
" 位置6: 原子索引30 (C)\n",
|
||
" 位置7: 原子索引28 (C)\n",
|
||
" 位置8: 原子索引26 (C)\n",
|
||
" 位置9: 原子索引16 (C)\n",
|
||
" 位置10: 原子索引15 (C)\n",
|
||
" 位置11: 原子索引14 (C)\n",
|
||
" 位置12: 原子索引13 (C)\n",
|
||
" 位置13: 原子索引12 (C)\n",
|
||
" 位置14: 原子索引11 (C)\n",
|
||
" 位置15: 原子索引10 (C)\n",
|
||
" 位置16: 原子索引9 (C)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 步骤5.5: 对环上原子进行编号（从C=O中的羰基C原子开始，按照从羰基C到酯键O的顺序）\n",
|
||
"def order_ring_atoms_from_start(mol, ring_atoms, start_atom_idx, target_atom_idx=None):\n",
|
||
" \"\"\"从指定原子开始，按照环的连接顺序排列原子，优先朝向目标原子方向\"\"\"\n",
|
||
" ring_atoms_set = set(ring_atoms)\n",
|
||
" \n",
|
||
" if start_atom_idx not in ring_atoms_set:\n",
|
||
" return ring_atoms\n",
|
||
" \n",
|
||
" ordered = [start_atom_idx]\n",
|
||
" remaining = ring_atoms_set - {start_atom_idx}\n",
|
||
" current = start_atom_idx\n",
|
||
" \n",
|
||
" # 如果指定了目标原子，优先选择朝向目标原子的路径\n",
|
||
" if target_atom_idx and target_atom_idx in ring_atoms_set:\n",
|
||
" # 使用BFS找到从start到target的最短路径\n",
|
||
" from collections import deque\n",
|
||
" queue = deque([(start_atom_idx, [start_atom_idx])])\n",
|
||
" visited = {start_atom_idx}\n",
|
||
" \n",
|
||
" while queue:\n",
|
||
" node, path = queue.popleft()\n",
|
||
" if node == target_atom_idx:\n",
|
||
" # 找到路径，先添加路径上的原子\n",
|
||
" for atom_idx in path[1:]: # 跳过第一个（已经在ordered中）\n",
|
||
" if atom_idx in remaining:\n",
|
||
" ordered.append(atom_idx)\n",
|
||
" remaining.remove(atom_idx)\n",
|
||
" break\n",
|
||
" \n",
|
||
" atom = mol.GetAtomWithIdx(node)\n",
|
||
" for neighbor in atom.GetNeighbors():\n",
|
||
" neighbor_idx = neighbor.GetIdx()\n",
|
||
" if neighbor_idx in ring_atoms_set and neighbor_idx not in visited:\n",
|
||
" visited.add(neighbor_idx)\n",
|
||
" queue.append((neighbor_idx, path + [neighbor_idx]))\n",
|
||
" \n",
|
||
" # 继续添加剩余的环原子\n",
|
||
" while remaining:\n",
|
||
" atom = mol.GetAtomWithIdx(current)\n",
|
||
" found_next = False\n",
|
||
" \n",
|
||
" # 找到当前原子的邻居中属于环的原子\n",
|
||
" for neighbor in atom.GetNeighbors():\n",
|
||
" neighbor_idx = neighbor.GetIdx()\n",
|
||
" if neighbor_idx in remaining:\n",
|
||
" ordered.append(neighbor_idx)\n",
|
||
" remaining.remove(neighbor_idx)\n",
|
||
" current = neighbor_idx\n",
|
||
" found_next = True\n",
|
||
" break\n",
|
||
" \n",
|
||
" if not found_next:\n",
|
||
" # 如果找不到连接的环原子，添加剩余的任意一个\n",
|
||
" if remaining:\n",
|
||
" next_atom = remaining.pop()\n",
|
||
" ordered.append(next_atom)\n",
|
||
" current = next_atom\n",
|
||
" \n",
|
||
" return ordered\n",
|
||
"\n",
|
||
"def create_ring_numbering(mol, ring_atoms, carbonyl_carbon_idx, ester_oxygen_idx):\n",
|
||
" \"\"\"创建环上原子的编号映射：从C=O中的羰基C原子开始编号为1，按照从羰基C到酯键O的顺序\"\"\"\n",
|
||
" ordered_atoms = order_ring_atoms_from_start(mol, ring_atoms, carbonyl_carbon_idx, ester_oxygen_idx)\n",
|
||
" numbering = {}\n",
|
||
" for i, atom_idx in enumerate(ordered_atoms, start=1):\n",
|
||
" numbering[atom_idx] = i\n",
|
||
" return numbering, ordered_atoms\n",
|
||
"\n",
|
||
"# 创建编号映射\n",
|
||
"ring_numbering1, ordered_ring1 = create_ring_numbering(mol1, ring16_atoms1, carbonyl_carbon1, ester_oxygen1)\n",
|
||
"ring_numbering2, ordered_ring2 = create_ring_numbering(mol2, ring16_atoms2, carbonyl_carbon2, ester_oxygen2)\n",
|
||
"\n",
|
||
"print(\"分子1的环上原子编号（从C=O羰基C原子开始）:\")\n",
|
||
"for atom_idx in ordered_ring1:\n",
|
||
" atom = mol1.GetAtomWithIdx(atom_idx)\n",
|
||
" print(f\" 位置{ring_numbering1[atom_idx]}: 原子索引{atom_idx} ({atom.GetSymbol()})\")\n",
|
||
"\n",
|
||
"print(\"\\n分子2的环上原子编号（从C=O羰基C原子开始）:\")\n",
|
||
"for atom_idx in ordered_ring2:\n",
|
||
" atom = mol2.GetAtomWithIdx(atom_idx)\n",
|
||
" print(f\" 位置{ring_numbering2[atom_idx]}: 原子索引{atom_idx} ({atom.GetSymbol()})\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"显示分子1（只显示环上原子编号）:\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[15:18:57] The new font size 0.8 is below the current minimum (6).\n"
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]
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},
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"\n",
|
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"显示分子2（只显示环上原子编号）:\n"
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]
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},
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"<path class=\"note\" d=\"M 540.8 402.4 Q 542.1 402.4, 542.7 403.3 Q 543.4 404.1, 543.4 405.9 Q 543.4 407.8, 542.6 408.8 Q 541.8 409.8, 540.2 409.8 Q 539.8 409.8, 539.4 409.7 Q 539.0 409.6, 538.6 409.4 L 539.0 408.7 Q 539.6 409.0, 540.2 409.0 Q 541.2 409.0, 541.8 408.4 Q 542.3 407.7, 542.4 406.3 Q 542.0 406.7, 541.5 406.8 Q 541.0 407.0, 540.5 407.0 Q 539.9 407.0, 539.4 406.7 Q 538.9 406.5, 538.6 406.0 Q 538.3 405.5, 538.3 404.8 Q 538.3 404.1, 538.6 403.6 Q 539.0 403.0, 539.5 402.7 Q 540.1 402.4, 540.8 402.4 M 539.4 404.8 Q 539.4 405.5, 539.7 405.8 Q 540.1 406.2, 540.7 406.2 Q 541.2 406.2, 541.6 406.0 Q 542.0 405.8, 542.4 405.5 Q 542.3 404.3, 541.9 403.8 Q 541.6 403.2, 540.8 403.2 Q 540.4 403.2, 540.1 403.4 Q 539.7 403.6, 539.6 404.0 Q 539.4 404.4, 539.4 404.8 \" fill=\"#000000\"/>\n",
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"<path class=\"note\" d=\"M 537.0 352.4 Q 537.7 352.7, 538.0 353.2 Q 538.4 353.6, 538.4 354.3 Q 538.4 354.9, 538.1 355.4 Q 537.8 355.9, 537.2 356.1 Q 536.6 356.4, 535.9 356.4 Q 534.7 356.4, 534.0 355.8 Q 533.3 355.3, 533.3 354.3 Q 533.3 353.7, 533.6 353.3 Q 533.9 352.8, 534.5 352.5 Q 534.1 352.2, 533.8 351.8 Q 533.5 351.4, 533.5 350.8 Q 533.5 349.9, 534.2 349.4 Q 534.8 348.9, 535.8 348.9 Q 536.9 348.9, 537.5 349.4 Q 538.2 349.9, 538.2 350.8 Q 538.2 351.3, 537.9 351.7 Q 537.6 352.1, 537.0 352.4 M 535.8 349.7 Q 535.2 349.7, 534.9 350.0 Q 534.6 350.3, 534.6 350.8 Q 534.6 351.2, 534.8 351.4 Q 535.0 351.7, 535.3 351.8 Q 535.7 352.0, 536.3 352.2 Q 536.7 351.9, 536.9 351.5 Q 537.1 351.2, 537.1 350.8 Q 537.1 350.3, 536.8 350.0 Q 536.5 349.7, 535.8 349.7 M 535.9 355.6 Q 536.6 355.6, 537.0 355.3 Q 537.4 354.9, 537.4 354.3 Q 537.4 353.9, 537.2 353.6 Q 537.0 353.4, 536.6 353.3 Q 536.3 353.1, 535.7 352.9 L 535.3 352.8 Q 534.8 353.1, 534.5 353.4 Q 534.3 353.8, 534.3 354.3 Q 534.3 354.9, 534.7 355.3 Q 535.2 355.6, 535.9 355.6 \" fill=\"#000000\"/>\n",
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"<path class=\"note\" d=\"M 414.7 267.1 Q 415.3 267.1, 415.9 267.4 Q 416.4 267.7, 416.7 268.2 Q 417.0 268.7, 417.0 269.4 Q 417.0 270.1, 416.6 270.7 Q 416.2 271.2, 415.6 271.5 Q 415.0 271.8, 414.4 271.8 Q 413.7 271.8, 413.1 271.5 Q 412.5 271.3, 412.0 270.8 L 412.7 270.1 Q 413.0 270.5, 413.5 270.7 Q 413.9 270.9, 414.4 270.9 Q 415.0 270.9, 415.5 270.5 Q 415.9 270.1, 415.9 269.4 Q 415.9 268.6, 415.5 268.2 Q 415.0 267.9, 414.3 267.9 Q 413.7 267.9, 413.0 268.2 L 412.4 267.9 L 412.8 264.4 L 416.5 264.4 L 416.4 265.2 L 413.6 265.2 L 413.4 267.4 Q 414.0 267.1, 414.7 267.1 \" fill=\"#000000\"/>\n",
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"<path class=\"note\" d=\"M 291.3 340.1 L 292.9 340.1 L 292.9 334.7 L 291.2 335.2 L 290.9 334.6 L 293.2 333.6 L 293.9 333.7 L 293.9 340.1 L 295.3 340.1 L 295.3 340.9 L 291.3 340.9 L 291.3 340.1 \" fill=\"#000000\"/>\n",
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"<path class=\"note\" d=\"M 252.5 381.6 Q 252.8 380.9, 253.4 380.6 Q 254.1 380.2, 255.0 380.2 Q 256.1 380.2, 256.7 380.8 Q 257.3 381.4, 257.3 382.4 Q 257.3 383.5, 256.5 384.5 Q 255.7 385.5, 254.0 386.7 L 257.4 386.7 L 257.4 387.5 L 252.5 387.5 L 252.5 386.8 Q 253.9 385.9, 254.7 385.2 Q 255.5 384.4, 255.9 383.8 Q 256.3 383.1, 256.3 382.5 Q 256.3 381.8, 255.9 381.4 Q 255.6 381.0, 255.0 381.0 Q 254.4 381.0, 254.0 381.2 Q 253.6 381.5, 253.3 382.0 L 252.5 381.6 \" fill=\"#000000\"/>\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>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 步骤5.6: 使用SVG格式显示分子，只显示环上原子的编号\n",
|
||
"def draw_mol_with_ring_numbering(mol, ring_atoms, ring_numbering, size=(800, 800), title=\"\"):\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",
|
||
" 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",
|
||
"# 显示两个分子，只显示环上原子的编号\n",
|
||
"print(\"显示分子1（只显示环上原子编号）:\")\n",
|
||
"svg1 = draw_mol_with_ring_numbering(mol1, ring16_atoms1, ring_numbering1, size=(800, 800), title=\"mol_1109\")\n",
|
||
"display(SVG(svg1))\n",
|
||
"\n",
|
||
"print(\"\\n显示分子2（只显示环上原子编号）:\")\n",
|
||
"svg2 = draw_mol_with_ring_numbering(mol2, ring16_atoms2, ring_numbering2, size=(800, 800), title=\"mol_1110\")\n",
|
||
"display(SVG(svg2))\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"=== 测试封装函数 ===\n",
|
||
"\n",
|
||
"分子1:\n",
|
||
" 羰基C索引: 38, 位置: 1\n",
|
||
" 酯键O索引: 40, 位置: 2\n",
|
||
" 编号范围: 1-16\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[15:18:58] 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",
|
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"<path class=\"bond-5 atom-5 atom-6\" d=\"M 244.9,411.2 L 256.7,412.1 L 271.9,457.8 L 262.2,463.4 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",
|
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"<path class=\"bond-43 atom-5 atom-41\" d=\"M 271.9,457.8 L 304.2,495.2 L 297.3,504.0 L 262.2,463.4 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",
|
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"<path class=\"bond-6 atom-6 atom-7\" d=\"M 268.5,364.1 L 279.3,367.0 L 256.7,412.1 L 244.9,411.2 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",
|
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"<path class=\"bond-8 atom-7 atom-9\" d=\"M 272.4,310.1 L 282.9,318.2 L 279.3,367.0 L 268.5,364.1 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 323.8,293.0 L 329.4,302.7 L 282.9,318.2 L 272.4,310.1 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=\"bond-10 atom-10 atom-11\" d=\"M 363.7,258.5 L 367.5,269.8 L 329.4,302.7 L 323.8,293.0 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",
|
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"<path class=\"bond-12 atom-11 atom-13\" d=\"M 363.7,258.5 L 418.5,262.4 L 415.5,273.3 L 367.5,269.8 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=\"bond-14 atom-13 atom-15\" d=\"M 418.5,262.4 L 466.6,286.5 L 459.7,295.4 L 415.5,273.3 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",
|
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"<path class=\"bond-15 atom-15 atom-16\" d=\"M 466.6,286.5 L 501.7,327.2 L 492.0,332.8 L 459.7,295.4 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",
|
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"<path class=\"bond-18 atom-16 atom-19\" d=\"M 501.7,327.2 L 518.7,378.2 L 507.5,379.6 L 492.0,332.8 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=\"bond-32 atom-19 atom-33\" d=\"M 518.7,378.2 L 514.8,431.9 L 504.0,428.9 L 507.5,379.6 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=\"bond-34 atom-33 atom-35\" d=\"M 504.0,428.9 L 514.8,431.9 L 490.7,479.9 L 481.8,473.0 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",
|
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"<path class=\"bond-36 atom-35 atom-37\" d=\"M 481.8,473.0 L 490.7,479.9 L 450.0,515.1 L 444.5,505.3 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",
|
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"<path class=\"bond-37 atom-37 atom-38\" d=\"M 444.5,505.3 L 450.0,515.1 L 399.0,532.0 L 397.6,520.9 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",
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"<path class=\"bond-39 atom-38 atom-40\" d=\"M 348.4,517.3 L 397.6,520.9 L 399.0,532.0 L 345.4,528.1 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=\"bond-40 atom-40 atom-41\" d=\"M 304.2,495.2 L 348.4,517.3 L 345.4,528.1 L 297.3,504.0 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",
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"<ellipse cx=\"267.0\" cy=\"460.6\" rx=\"10.3\" ry=\"10.3\" class=\"atom-5\" 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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"<ellipse cx=\"250.8\" cy=\"411.7\" rx=\"10.3\" ry=\"10.3\" class=\"atom-6\" 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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"<ellipse cx=\"273.9\" cy=\"365.6\" rx=\"10.3\" ry=\"10.3\" class=\"atom-7\" 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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"<ellipse cx=\"277.6\" cy=\"314.1\" rx=\"10.3\" ry=\"10.3\" class=\"atom-9\" 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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"<ellipse cx=\"326.6\" cy=\"297.9\" rx=\"10.3\" ry=\"10.3\" class=\"atom-10\" 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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"<ellipse cx=\"365.6\" cy=\"264.1\" rx=\"10.3\" ry=\"10.3\" 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",
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"<ellipse cx=\"417.0\" cy=\"267.9\" rx=\"10.3\" ry=\"10.3\" class=\"atom-13\" 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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"<ellipse cx=\"463.1\" cy=\"291.0\" rx=\"10.3\" ry=\"10.3\" class=\"atom-15\" 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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"<ellipse cx=\"496.9\" cy=\"330.0\" rx=\"10.3\" ry=\"10.3\" class=\"atom-16\" 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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"<ellipse cx=\"513.1\" cy=\"378.9\" rx=\"10.3\" ry=\"10.3\" class=\"atom-19\" 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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"<ellipse cx=\"509.4\" cy=\"430.4\" rx=\"10.3\" ry=\"10.3\" class=\"atom-33\" 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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"<ellipse cx=\"486.3\" cy=\"476.5\" rx=\"10.3\" ry=\"10.3\" class=\"atom-35\" 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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"name": "stdout",
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"text": [
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"\n",
|
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"分子2:\n",
|
||
" 羰基C索引: 38, 位置: 1\n",
|
||
" 酯键O索引: 40, 位置: 2\n",
|
||
" 编号范围: 1-16\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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"[15:18:58] The new font size 0.8 is below the current minimum (6).\n"
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]
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},
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{
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"<!-- END OF HEADER -->\n",
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"<path class=\"note\" d=\"M 252.5 381.6 Q 252.8 380.9, 253.4 380.6 Q 254.1 380.2, 255.0 380.2 Q 256.1 380.2, 256.7 380.8 Q 257.3 381.4, 257.3 382.4 Q 257.3 383.5, 256.5 384.5 Q 255.7 385.5, 254.0 386.7 L 257.4 386.7 L 257.4 387.5 L 252.5 387.5 L 252.5 386.8 Q 253.9 385.9, 254.7 385.2 Q 255.5 384.4, 255.9 383.8 Q 256.3 383.1, 256.3 382.5 Q 256.3 381.8, 255.9 381.4 Q 255.6 381.0, 255.0 381.0 Q 254.4 381.0, 254.0 381.2 Q 253.6 381.5, 253.3 382.0 L 252.5 381.6 \" fill=\"#000000\"/>\n",
|
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"</svg>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.SVG object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 封装函数：完整的环编号和显示流程\n",
|
||
"def get_ring_numbering_for_molecule(mol):\n",
|
||
" \"\"\"\n",
|
||
" 完整的环编号流程：\n",
|
||
" 1. 找到16元环\n",
|
||
" 2. 找到酯基\n",
|
||
" 3. 找到羰基C和酯键O\n",
|
||
" 4. 创建编号映射\n",
|
||
" 5. 返回编号结果\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" tuple: (ring_atoms, ring_numbering, ordered_atoms, carbonyl_carbon, ester_oxygen)\n",
|
||
" \"\"\"\n",
|
||
" # 1. 找到16元环\n",
|
||
" ring16_atoms = get_ring16_atoms(mol)\n",
|
||
" if ring16_atoms is None:\n",
|
||
" return None, None, None, None, None\n",
|
||
" \n",
|
||
" # 2. 找到酯基\n",
|
||
" ester_atoms, pattern = find_ester_smarts(mol, ring16_atoms)\n",
|
||
" if ester_atoms is None:\n",
|
||
" return None, None, None, None, None\n",
|
||
" \n",
|
||
" # 3. 找到羰基C和酯键O\n",
|
||
" carbonyl_carbon = get_carbonyl_carbon_in_ring(mol, ester_atoms, ring16_atoms)\n",
|
||
" ester_oxygen = get_ester_oxygen_in_ring(mol, ester_atoms, ring16_atoms)\n",
|
||
" \n",
|
||
" if carbonyl_carbon is None or ester_oxygen is None:\n",
|
||
" return None, None, None, None, None\n",
|
||
" \n",
|
||
" # 4. 创建编号映射\n",
|
||
" ring_numbering, ordered_atoms = create_ring_numbering(\n",
|
||
" mol, ring16_atoms, carbonyl_carbon, ester_oxygen\n",
|
||
" )\n",
|
||
" \n",
|
||
" return ring16_atoms, ring_numbering, ordered_atoms, carbonyl_carbon, ester_oxygen\n",
|
||
"\n",
|
||
"# 测试封装函数\n",
|
||
"print(\"=== 测试封装函数 ===\")\n",
|
||
"print(\"\\n分子1:\")\n",
|
||
"ring_atoms1, numbering1, ordered1, carbonyl1, oxygen1 = get_ring_numbering_for_molecule(mol1)\n",
|
||
"if numbering1:\n",
|
||
" print(f\" 羰基C索引: {carbonyl1}, 位置: {numbering1[carbonyl1]}\")\n",
|
||
" print(f\" 酯键O索引: {oxygen1}, 位置: {numbering1[oxygen1]}\")\n",
|
||
" print(f\" 编号范围: 1-{len(numbering1)}\")\n",
|
||
" svg1 = draw_mol_with_ring_numbering(mol1, ring_atoms1, numbering1, size=(800, 800))\n",
|
||
" display(SVG(svg1))\n",
|
||
"else:\n",
|
||
" print(\" 编号失败\")\n",
|
||
"\n",
|
||
"print(\"\\n分子2:\")\n",
|
||
"ring_atoms2, numbering2, ordered2, carbonyl2, oxygen2 = get_ring_numbering_for_molecule(mol2)\n",
|
||
"if numbering2:\n",
|
||
" print(f\" 羰基C索引: {carbonyl2}, 位置: {numbering2[carbonyl2]}\")\n",
|
||
" print(f\" 酯键O索引: {oxygen2}, 位置: {numbering2[oxygen2]}\")\n",
|
||
" print(f\" 编号范围: 1-{len(numbering2)}\")\n",
|
||
" svg2 = draw_mol_with_ring_numbering(mol2, ring_atoms2, numbering2, size=(800, 800))\n",
|
||
" display(SVG(svg2))\n",
|
||
"else:\n",
|
||
" print(\" 编号失败\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 12,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"=== 测试断裂侧链功能 ===\n",
|
||
"\n",
|
||
"分子1的所有侧链位置:\n",
|
||
" 位置 15 (环原子5 C) -> 侧链起始原子4 (C)\n",
|
||
" 位置 16 (环原子41 C) -> 侧链起始原子42 (C)\n",
|
||
" 位置 1 (环原子38 C) -> 侧链起始原子39 (O)\n",
|
||
" 位置 4 (环原子35 C) -> 侧链起始原子36 (O)\n",
|
||
" 位置 5 (环原子33 C) -> 侧链起始原子34 (C)\n",
|
||
" 位置 6 (环原子19 C) -> 侧链起始原子20 (O)\n",
|
||
" 位置 7 (环原子16 C) -> 侧链起始原子17 (C)\n",
|
||
" 位置 9 (环原子13 C) -> 侧链起始原子14 (C)\n",
|
||
" 位置 10 (环原子11 C) -> 侧链起始原子12 (O)\n",
|
||
" 位置 13 (环原子7 C) -> 侧链起始原子8 (C)\n",
|
||
"\n",
|
||
"分子2的所有侧链位置:\n",
|
||
" 位置 9 (环原子16 C) -> 侧链起始原子17 (C)\n",
|
||
" 位置 8 (环原子26 C) -> 侧链起始原子27 (O)\n",
|
||
" 位置 7 (环原子28 C) -> 侧链起始原子29 (C)\n",
|
||
" 位置 6 (环原子30 C) -> 侧链起始原子31 (O)\n",
|
||
" 位置 5 (环原子32 C) -> 侧链起始原子33 (C)\n",
|
||
" 位置 5 (环原子32 C) -> 侧链起始原子34 (C)\n",
|
||
" 位置 4 (环原子35 C) -> 侧链起始原子36 (O)\n",
|
||
" 位置 1 (环原子38 C) -> 侧链起始原子39 (O)\n",
|
||
" 位置 16 (环原子9 C) -> 侧链起始原子1 (C)\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 断裂指定编号位置的侧链碎片\n",
|
||
"def identify_side_chains(mol, ring_atoms):\n",
|
||
" \"\"\"\n",
|
||
" 识别连接到环上的侧链\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" mol: RDKit分子对象\n",
|
||
" ring_atoms: 环上原子的索引列表\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" List of tuples: (ring_atom_idx, side_chain_first_atom_idx)\n",
|
||
" \"\"\"\n",
|
||
" side_chains = []\n",
|
||
" ring_atom_set = set(ring_atoms)\n",
|
||
" \n",
|
||
" for ring_atom_idx in ring_atoms:\n",
|
||
" atom = mol.GetAtomWithIdx(ring_atom_idx)\n",
|
||
" for neighbor in atom.GetNeighbors():\n",
|
||
" neighbor_idx = neighbor.GetIdx()\n",
|
||
" # 如果邻居不在环上，就是侧链\n",
|
||
" if neighbor_idx not in ring_atom_set:\n",
|
||
" side_chains.append((ring_atom_idx, neighbor_idx))\n",
|
||
" \n",
|
||
" return side_chains\n",
|
||
"\n",
|
||
"def extract_side_chain_fragment(mol, ring_atom_idx, side_chain_start_idx, ring_atoms):\n",
|
||
" \"\"\"\n",
|
||
" 提取侧链碎片为SMILES字符串，在断裂位置添加dummy原子（*）以便后续使用molzip拼接\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" mol: RDKit分子对象\n",
|
||
" ring_atom_idx: 侧链连接的环原子索引\n",
|
||
" side_chain_start_idx: 侧链第一个原子索引\n",
|
||
" ring_atoms: 环上所有原子索引列表\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" SMILES字符串（包含dummy原子*），如果提取失败返回None\n",
|
||
" \"\"\"\n",
|
||
" ring_atom_set = set(ring_atoms)\n",
|
||
" visited = set()\n",
|
||
" queue = [side_chain_start_idx]\n",
|
||
" side_chain_atoms = []\n",
|
||
" \n",
|
||
" # 使用BFS收集侧链所有原子\n",
|
||
" while queue:\n",
|
||
" current_idx = queue.pop(0)\n",
|
||
" if current_idx in visited:\n",
|
||
" continue\n",
|
||
" \n",
|
||
" visited.add(current_idx)\n",
|
||
" side_chain_atoms.append(current_idx)\n",
|
||
" \n",
|
||
" atom = mol.GetAtomWithIdx(current_idx)\n",
|
||
" for neighbor in atom.GetNeighbors():\n",
|
||
" neighbor_idx = neighbor.GetIdx()\n",
|
||
" # 只继续到非环原子\n",
|
||
" if neighbor_idx not in ring_atom_set and neighbor_idx not in visited:\n",
|
||
" queue.append(neighbor_idx)\n",
|
||
" \n",
|
||
" if not side_chain_atoms:\n",
|
||
" return None\n",
|
||
" \n",
|
||
" # 创建只包含侧链原子的新分子\n",
|
||
" fragment_mol = Chem.RWMol()\n",
|
||
" old_to_new = {}\n",
|
||
" \n",
|
||
" # 添加原子\n",
|
||
" for old_idx in side_chain_atoms:\n",
|
||
" atom = mol.GetAtomWithIdx(old_idx)\n",
|
||
" new_atom = Chem.Atom(atom.GetAtomicNum())\n",
|
||
" new_atom.SetFormalCharge(atom.GetFormalCharge())\n",
|
||
" new_atom.SetIsAromatic(atom.GetIsAromatic())\n",
|
||
" new_idx = fragment_mol.AddAtom(new_atom)\n",
|
||
" old_to_new[old_idx] = new_idx\n",
|
||
" \n",
|
||
" # 获取原来连接到环原子的键类型\n",
|
||
" bond_to_ring = mol.GetBondBetweenAtoms(ring_atom_idx, side_chain_start_idx)\n",
|
||
" if bond_to_ring is None:\n",
|
||
" return None\n",
|
||
" bond_type = bond_to_ring.GetBondType()\n",
|
||
" \n",
|
||
" # 添加键（侧链内部的键）\n",
|
||
" for old_idx in side_chain_atoms:\n",
|
||
" atom = mol.GetAtomWithIdx(old_idx)\n",
|
||
" for neighbor in atom.GetNeighbors():\n",
|
||
" neighbor_idx = neighbor.GetIdx()\n",
|
||
" if neighbor_idx in old_to_new and old_idx < neighbor_idx:\n",
|
||
" bond = mol.GetBondBetweenAtoms(old_idx, neighbor_idx)\n",
|
||
" fragment_mol.AddBond(\n",
|
||
" old_to_new[old_idx],\n",
|
||
" old_to_new[neighbor_idx],\n",
|
||
" bond.GetBondType()\n",
|
||
" )\n",
|
||
" \n",
|
||
" # 在侧链连接点添加dummy原子（原子序数0，在SMILES中显示为*）\n",
|
||
" # dummy原子连接到原来连接到环原子的那个原子\n",
|
||
" attachment_point = old_to_new[side_chain_start_idx] # 侧链中原来连接到环的原子\n",
|
||
" dummy_atom_idx = fragment_mol.AddAtom(Chem.Atom(0)) # 添加dummy原子（原子序数0）\n",
|
||
" \n",
|
||
" # 添加dummy原子和连接点之间的键，保持原来的键类型\n",
|
||
" fragment_mol.AddBond(attachment_point, dummy_atom_idx, bond_type)\n",
|
||
" \n",
|
||
" # 转换为分子并获取SMILES\n",
|
||
" try:\n",
|
||
" fragment_mol = fragment_mol.GetMol()\n",
|
||
" Chem.SanitizeMol(fragment_mol)\n",
|
||
" fragment_smiles = Chem.MolToSmiles(fragment_mol)\n",
|
||
" return fragment_smiles\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"提取侧链失败: {e}\")\n",
|
||
" return None\n",
|
||
"\n",
|
||
"def cleave_side_chain_at_position(mol, ring_atoms, ring_numbering, position):\n",
|
||
" \"\"\"\n",
|
||
" 在指定编号位置断裂侧链\n",
|
||
" \n",
|
||
" Args:\n",
|
||
" mol: RDKit分子对象\n",
|
||
" ring_atoms: 环上原子索引列表\n",
|
||
" ring_numbering: 环原子编号映射 {atom_idx: position}\n",
|
||
" position: 要断裂的位置编号（1-16）\n",
|
||
" \n",
|
||
" Returns:\n",
|
||
" List of SMILES字符串，每个侧链一个（包含dummy原子*，可用于molzip拼接）\n",
|
||
" \"\"\"\n",
|
||
" # 找到该位置对应的环原子索引\n",
|
||
" ring_atom_idx = None\n",
|
||
" for atom_idx, pos in ring_numbering.items():\n",
|
||
" if pos == position:\n",
|
||
" ring_atom_idx = atom_idx\n",
|
||
" break\n",
|
||
" \n",
|
||
" if ring_atom_idx is None:\n",
|
||
" print(f\"错误: 位置 {position} 不存在\")\n",
|
||
" return []\n",
|
||
" \n",
|
||
" # 找到连接到该环原子的所有侧链\n",
|
||
" side_chains = identify_side_chains(mol, ring_atoms)\n",
|
||
" fragments = []\n",
|
||
" \n",
|
||
" for ring_atom, side_start in side_chains:\n",
|
||
" if ring_atom == ring_atom_idx:\n",
|
||
" fragment_smiles = extract_side_chain_fragment(mol, ring_atom, side_start, ring_atoms)\n",
|
||
" if fragment_smiles:\n",
|
||
" fragments.append(fragment_smiles)\n",
|
||
" \n",
|
||
" return fragments\n",
|
||
"\n",
|
||
"# 测试：断裂指定位置的侧链\n",
|
||
"print(\"=== 测试断裂侧链功能 ===\\n\")\n",
|
||
"\n",
|
||
"# 显示所有侧链位置\n",
|
||
"print(\"分子1的所有侧链位置:\")\n",
|
||
"side_chains1 = identify_side_chains(mol1, ring_atoms1)\n",
|
||
"for ring_atom_idx, side_start_idx in side_chains1:\n",
|
||
" position = numbering1.get(ring_atom_idx, \"?\")\n",
|
||
" atom = mol1.GetAtomWithIdx(ring_atom_idx)\n",
|
||
" side_atom = mol1.GetAtomWithIdx(side_start_idx)\n",
|
||
" print(f\" 位置 {position} (环原子{ring_atom_idx} {atom.GetSymbol()}) -> 侧链起始原子{side_start_idx} ({side_atom.GetSymbol()})\")\n",
|
||
"\n",
|
||
"print(\"\\n分子2的所有侧链位置:\")\n",
|
||
"side_chains2 = identify_side_chains(mol2, ring_atoms2)\n",
|
||
"for ring_atom_idx, side_start_idx in side_chains2:\n",
|
||
" position = numbering2.get(ring_atom_idx, \"?\")\n",
|
||
" atom = mol2.GetAtomWithIdx(ring_atom_idx)\n",
|
||
" side_atom = mol2.GetAtomWithIdx(side_start_idx)\n",
|
||
" print(f\" 位置 {position} (环原子{ring_atom_idx} {atom.GetSymbol()}) -> 侧链起始原子{side_start_idx} ({side_atom.GetSymbol()})\")\n",
|
||
"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 13,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"=== 测试断裂分子1的位置 [15]（带dummy原子*）===\n",
|
||
"\n",
|
||
"位置 15 的侧链碎片 (1 个):\n",
|
||
" 碎片1: *COCC#C\n",
|
||
" ✓ 包含dummy原子，可用于molzip拼接\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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|
||
"image/png": 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"text/plain": [
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"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
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]
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||
},
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"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 测试断裂指定位置（带dummy原子）\n",
|
||
"test_positions = [15] # 测试位置6\n",
|
||
"print(f\"\\n=== 测试断裂分子1的位置 {test_positions}（带dummy原子*）===\")\n",
|
||
"for pos in test_positions:\n",
|
||
" fragments = cleave_side_chain_at_position(mol1, ring_atoms1, numbering1, pos)\n",
|
||
" if fragments:\n",
|
||
" print(f\"\\n位置 {pos} 的侧链碎片 ({len(fragments)} 个):\")\n",
|
||
" for i, frag_smiles in enumerate(fragments, 1):\n",
|
||
" print(f\" 碎片{i}: {frag_smiles}\")\n",
|
||
" # 检查是否包含dummy原子（*）\n",
|
||
" if '*' in frag_smiles:\n",
|
||
" print(f\" ✓ 包含dummy原子，可用于molzip拼接\")\n",
|
||
" frag_mol = Chem.MolFromSmiles(frag_smiles)\n",
|
||
" if frag_mol:\n",
|
||
" display(Draw.MolToImage(frag_mol, size=(300, 300)))\n",
|
||
" else:\n",
|
||
" print(f\"位置 {pos}: 无侧链\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"警告: RDKit版本可能不支持molzip功能\n",
|
||
"需要RDKit 2022.09或更高版本\n",
|
||
"可以使用 pip install rdkit --upgrade 升级\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 测试使用molzip拼接带dummy原子的碎片\n",
|
||
"# 注意：需要RDKit 2022.09或更高版本才支持molzip\n",
|
||
"try:\n",
|
||
" from rdkit.Chem import rdMolZip\n",
|
||
" \n",
|
||
" print(\"=== 测试molzip拼接功能 ===\\n\")\n",
|
||
" \n",
|
||
" # 获取位置6的侧链碎片\n",
|
||
" fragments = cleave_side_chain_at_position(mol1, ring_atoms1, numbering1, 6)\n",
|
||
" \n",
|
||
" if fragments:\n",
|
||
" frag_smiles = fragments[0]\n",
|
||
" print(f\"侧链碎片SMILES: {frag_smiles}\")\n",
|
||
" \n",
|
||
" # 创建一个简单的环骨架（带dummy原子）用于测试拼接\n",
|
||
" # 这里只是演示，实际使用时需要创建完整的环骨架\n",
|
||
" ring_skeleton_smiles = \"C1CCCCCCCCCCCCCC1\" # 示例：16元环骨架\n",
|
||
" \n",
|
||
" print(f\"\\n环骨架SMILES（示例）: {ring_skeleton_smiles}\")\n",
|
||
" print(\"\\n注意：要使用molzip拼接，需要：\")\n",
|
||
" print(\"1. 环骨架在指定位置也有dummy原子（*）\")\n",
|
||
" print(\"2. 使用 rdMolZip.ZipMols() 函数进行拼接\")\n",
|
||
" print(\"3. 例如: combined = rdMolZip.ZipMols(ring_mol, fragment_mol)\")\n",
|
||
" \n",
|
||
" # 示例：如果有环骨架分子，可以这样拼接\n",
|
||
" # ring_mol = Chem.MolFromSmiles(ring_skeleton_smiles)\n",
|
||
" # frag_mol = Chem.MolFromSmiles(frag_smiles)\n",
|
||
" # combined = rdMolZip.ZipMols(ring_mol, frag_mol)\n",
|
||
" \n",
|
||
" else:\n",
|
||
" print(\"未找到侧链碎片\")\n",
|
||
" \n",
|
||
"except ImportError:\n",
|
||
" print(\"警告: RDKit版本可能不支持molzip功能\")\n",
|
||
" print(\"需要RDKit 2022.09或更高版本\")\n",
|
||
" print(\"可以使用 pip install rdkit --upgrade 升级\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 15,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"=== 测试封装的可视化函数 ===\n",
|
||
"\n",
|
||
"测试1: 使用SMILES字符串\n",
|
||
"Ring size: 16\n",
|
||
"Carbonyl C position: 1\n",
|
||
"Ester O position: 2\n",
|
||
"Numbering range: 1-16\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
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"<path class=\"note\" d=\"M 452.6 517.5 Q 453.3 517.7, 453.6 518.2 Q 454.0 518.7, 454.0 519.4 Q 454.0 520.0, 453.7 520.5 Q 453.3 521.0, 452.8 521.2 Q 452.2 521.5, 451.5 521.5 Q 450.7 521.5, 450.1 521.2 Q 449.5 520.9, 449.1 520.4 L 449.6 519.8 Q 450.1 520.3, 450.5 520.5 Q 450.9 520.7, 451.5 520.7 Q 452.1 520.7, 452.5 520.3 Q 452.9 519.9, 452.9 519.4 Q 452.9 518.6, 452.5 518.3 Q 452.1 517.9, 451.2 517.9 L 450.7 517.9 L 450.7 517.2 L 451.1 517.2 Q 451.9 517.2, 452.4 516.9 Q 452.8 516.5, 452.8 515.9 Q 452.8 515.4, 452.4 515.1 Q 452.1 514.8, 451.5 514.8 Q 450.9 514.8, 450.5 515.0 Q 450.1 515.2, 449.8 515.8 L 449.1 515.4 Q 449.4 514.8, 450.0 514.4 Q 450.6 514.0, 451.5 514.0 Q 452.6 514.0, 453.2 514.5 Q 453.8 515.0, 453.8 515.9 Q 453.8 516.4, 453.5 516.9 Q 453.2 517.3, 452.6 517.5 \" fill=\"#000000\"/>\n",
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||
"<path class=\"note\" d=\"M 392.5 521.7 L 394.1 521.7 L 394.1 516.2 L 392.4 516.8 L 392.1 516.2 L 394.4 515.2 L 395.1 515.3 L 395.1 521.7 L 396.5 521.7 L 396.5 522.5 L 392.5 522.5 L 392.5 521.7 \" fill=\"#000000\"/>\n",
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||
"<path class=\"note\" d=\"M 339.9 537.1 Q 340.1 536.4, 340.8 536.0 Q 341.4 535.6, 342.3 535.6 Q 343.4 535.6, 344.0 536.2 Q 344.7 536.8, 344.7 537.9 Q 344.7 539.0, 343.8 540.0 Q 343.1 541.0, 341.4 542.2 L 344.8 542.2 L 344.8 543.0 L 339.9 543.0 L 339.9 542.3 Q 341.2 541.3, 342.0 540.6 Q 342.8 539.9, 343.2 539.2 Q 343.6 538.6, 343.6 537.9 Q 343.6 537.2, 343.3 536.8 Q 342.9 536.4, 342.3 536.4 Q 341.7 536.4, 341.3 536.7 Q 340.9 536.9, 340.7 537.4 L 339.9 537.1 \" fill=\"#000000\"/>\n",
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||
"<path class=\"note\" d=\"M 299.8 519.2 L 301.4 519.2 L 301.4 513.8 L 299.6 514.3 L 299.4 513.7 L 301.6 512.7 L 302.4 512.9 L 302.4 519.2 L 303.8 519.2 L 303.8 520.1 L 299.8 520.1 L 299.8 519.2 \" fill=\"#000000\"/>\n",
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||
"<path class=\"note\" d=\"M 307.5 515.5 Q 308.2 515.5, 308.7 515.8 Q 309.2 516.1, 309.5 516.6 Q 309.8 517.1, 309.8 517.7 Q 309.8 518.4, 309.4 519.0 Q 309.1 519.5, 308.6 519.8 Q 308.0 520.2, 307.3 520.2 Q 306.0 520.2, 305.3 519.3 Q 304.7 518.4, 304.7 516.6 Q 304.7 514.7, 305.5 513.7 Q 306.3 512.7, 307.9 512.7 Q 308.3 512.7, 308.7 512.8 Q 309.1 512.9, 309.5 513.1 L 309.1 513.8 Q 308.5 513.5, 307.9 513.5 Q 306.8 513.5, 306.3 514.2 Q 305.8 514.8, 305.7 516.2 Q 306.1 515.9, 306.6 515.7 Q 307.0 515.5, 307.5 515.5 M 307.3 519.3 Q 307.7 519.3, 308.0 519.1 Q 308.4 518.9, 308.5 518.5 Q 308.7 518.2, 308.7 517.7 Q 308.7 517.1, 308.4 516.7 Q 308.0 516.3, 307.4 516.3 Q 306.9 516.3, 306.5 516.5 Q 306.0 516.7, 305.7 517.0 Q 305.8 518.2, 306.2 518.8 Q 306.5 519.3, 307.3 519.3 \" fill=\"#000000\"/>\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": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"测试2: 使用RDKit对象\n",
|
||
"Ring size: 16\n",
|
||
"Carbonyl C position: 1\n",
|
||
"Ester O position: 2\n",
|
||
"Numbering range: 1-16\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"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=\"800px\" height=\"800px\" viewBox=\"0 0 800 800\">\n",
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||
"<!-- END OF HEADER -->\n",
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||
"<rect style=\"opacity:1.0;fill:#FFFFFF;stroke:none\" width=\"800.0\" height=\"800.0\" x=\"0.0\" y=\"0.0\"> </rect>\n",
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||
"<path class=\"bond-9 atom-9 atom-10\" d=\"M 284.7,433.9 L 309.8,476.4 L 301.5,483.9 L 274.1,437.6 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=\"bond-41 atom-9 atom-40\" d=\"M 266.5,384.5 L 277.7,385.1 L 284.7,433.9 L 274.1,437.6 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-10 atom-10 atom-11\" d=\"M 309.8,476.4 L 349.3,506.0 L 344.4,516.1 L 301.5,483.9 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",
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"<path class=\"bond-11 atom-11 atom-12\" d=\"M 349.3,506.0 L 397.1,518.2 L 396.5,529.4 L 344.4,516.1 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=\"bond-15 atom-15 atom-16\" d=\"M 530.2,398.8 L 541.4,399.5 L 528.1,451.5 L 518.0,446.6 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=\"bond-25 atom-16 atom-26\" d=\"M 533.8,346.3 L 541.4,399.5 L 530.2,398.8 L 523.2,350.0 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",
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"<path class=\"bond-27 atom-26 atom-28\" d=\"M 506.4,300.1 L 533.8,346.3 L 523.2,350.0 L 498.1,307.6 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=\"bond-29 atom-28 atom-30\" d=\"M 463.5,267.9 L 506.4,300.1 L 498.1,307.6 L 458.6,278.0 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",
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"<path class=\"bond-31 atom-30 atom-32\" d=\"M 411.4,254.5 L 463.5,267.9 L 458.6,278.0 L 410.8,265.7 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",
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"<path class=\"bond-34 atom-32 atom-35\" d=\"M 411.4,254.5 L 410.8,265.7 L 362.0,272.7 L 358.3,262.1 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=\"bond-36 atom-35 atom-37\" d=\"M 358.3,262.1 L 362.0,272.7 L 319.5,297.8 L 312.1,289.5 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",
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"<path class=\"bond-37 atom-37 atom-38\" d=\"M 312.1,289.5 L 319.5,297.8 L 289.9,337.3 L 279.8,332.5 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",
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"<path class=\"bond-39 atom-38 atom-40\" d=\"M 279.8,332.5 L 289.9,337.3 L 277.7,385.1 L 266.5,384.5 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",
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"<ellipse cx=\"279.4\" cy=\"435.8\" rx=\"10.3\" ry=\"10.3\" class=\"atom-9\" 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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"<ellipse cx=\"360.1\" cy=\"267.4\" rx=\"10.3\" ry=\"10.3\" class=\"atom-35\" 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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"<ellipse cx=\"315.8\" cy=\"293.7\" rx=\"10.3\" ry=\"10.3\" 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",
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"<ellipse cx=\"284.9\" cy=\"334.9\" rx=\"10.3\" ry=\"10.3\" class=\"atom-38\" 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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"<ellipse cx=\"272.1\" cy=\"384.9\" rx=\"10.3\" ry=\"10.6\" class=\"atom-40\" 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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||
"<path class=\"bond-0 atom-0 atom-1\" d=\"M 191.7,419.3 L 230.8,452.9\" 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-1 atom-1 atom-2\" d=\"M 230.8,452.9 L 221.3,503.5\" 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-1 atom-1 atom-2\" d=\"M 222.4,455.8 L 214.5,497.7\" 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-2 atom-2 atom-3\" d=\"M 221.3,503.5 L 172.7,520.6\" 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-3 atom-3 atom-4\" d=\"M 172.7,520.6 L 158.0,570.0\" 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-3 atom-3 atom-4\" d=\"M 163.7,523.8 L 152.2,562.4\" 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-4 atom-4 atom-5\" d=\"M 158.0,570.0 L 135.4,570.5\" 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-4 atom-4 atom-5\" d=\"M 135.4,570.5 L 112.8,571.1\" style=\"fill:none;fill-rule:evenodd;stroke:#CCCC00;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-5 atom-5 atom-6\" d=\"M 103.3,562.2 L 96.3,542.4\" style=\"fill:none;fill-rule:evenodd;stroke:#CCCC00;stroke-width:2.0px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1\"/>\n",
|
||
"<path class=\"bond-5 atom-5 atom-6\" d=\"M 96.3,542.4 L 89.4,522.6\" 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-6 atom-6 atom-7\" d=\"M 89.4,522.6 L 40.0,507.9\" 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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||
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|
||
"<path class=\"bond-7 atom-6 atom-8\" d=\"M 98.5,525.4 L 113.1,514.2\" 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-8 atom-9 atom-1\" d=\"M 279.4,435.8 L 229.9,457.5 L 230.8,452.9 Z\" style=\"fill:#000000;fill-rule:evenodd;fill-opacity:1;stroke:#000000;stroke-width:0.5px;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:10;stroke-opacity:1;\"/>\n",
|
||
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|
||
"<path class=\"bond-9 atom-9 atom-10\" d=\"M 279.4,435.8 L 305.6,480.1\" 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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|
||
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|
||
"<path class=\"bond-11 atom-11 atom-12\" d=\"M 350.3,503.9 L 398.7,516.3\" 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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|
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|
||
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|
||
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|
||
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|
||
"<path class=\"bond-17 atom-17 atom-18\" d=\"M 585.3,398.1 L 596.6,420.4\" 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=\"note\" d=\"M 252.5 381.6 Q 252.8 380.9, 253.4 380.6 Q 254.1 380.2, 255.0 380.2 Q 256.1 380.2, 256.7 380.8 Q 257.3 381.4, 257.3 382.4 Q 257.3 383.5, 256.5 384.5 Q 255.7 385.5, 254.0 386.7 L 257.4 386.7 L 257.4 387.5 L 252.5 387.5 L 252.5 386.8 Q 253.9 385.9, 254.7 385.2 Q 255.5 384.4, 255.9 383.8 Q 256.3 383.1, 256.3 382.5 Q 256.3 381.8, 255.9 381.4 Q 255.6 381.0, 255.0 381.0 Q 254.4 381.0, 254.0 381.2 Q 253.6 381.5, 253.3 382.0 L 252.5 381.6 \" fill=\"#000000\"/>\n",
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"</svg>"
|
||
],
|
||
"text/plain": [
|
||
"<IPython.core.display.SVG object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 导入封装的可视化模块\n",
|
||
"from src.ring_visualization import (\n",
|
||
" get_macrolactone_numbering,\n",
|
||
" draw_mol_with_ring_numbering,\n",
|
||
" visualize_molecule_with_numbering\n",
|
||
")\n",
|
||
"\n",
|
||
"# 测试封装函数\n",
|
||
"print(\"=== 测试封装的可视化函数 ===\\n\")\n",
|
||
"\n",
|
||
"# 测试1: 使用SMILES字符串\n",
|
||
"test_smiles = Chem.MolToSmiles(mol1)\n",
|
||
"print(\"测试1: 使用SMILES字符串\")\n",
|
||
"numbering, ring_atoms = visualize_molecule_with_numbering(test_smiles, ring_size=16)\n",
|
||
"\n",
|
||
"# 测试2: 使用RDKit对象\n",
|
||
"print(\"\\n测试2: 使用RDKit对象\")\n",
|
||
"numbering2, ring_atoms2 = visualize_molecule_with_numbering(mol2, ring_size=16)\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 16,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"找到 795 个SDF文件\n",
|
||
"已处理 50 个文件...\n",
|
||
"已处理 100 个文件...\n",
|
||
"已处理 150 个文件...\n",
|
||
"已处理 200 个文件...\n",
|
||
"已处理 250 个文件...\n",
|
||
"已处理 300 个文件...\n",
|
||
"已处理 350 个文件...\n",
|
||
"已处理 400 个文件...\n",
|
||
"已处理 450 个文件...\n",
|
||
"已处理 500 个文件...\n",
|
||
"已处理 550 个文件...\n",
|
||
"已处理 600 个文件...\n",
|
||
"已处理 650 个文件...\n",
|
||
"已处理 700 个文件...\n",
|
||
"已处理 750 个文件...\n",
|
||
"\n",
|
||
"处理完成!\n",
|
||
"成功: 792 个\n",
|
||
"失败: 3 个\n",
|
||
"输出目录: /home/zly/project/macro_split/output/numbered_molecules\n",
|
||
"\n",
|
||
"失败的文件 (前10个):\n",
|
||
" ring16_mol_94_2d.sdf: 编号失败\n",
|
||
" ring16_mol_370_2d.sdf: 编号失败\n",
|
||
" ring16_mol_400_2d.sdf: 编号失败\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 批量处理：对所有*_2d.sdf文件进行编号并生成图片\n",
|
||
"from pathlib import Path\n",
|
||
"\n",
|
||
"# 确保导入必要的函数\n",
|
||
"from src.ring_visualization import get_macrolactone_numbering\n",
|
||
"\n",
|
||
"# 设置路径\n",
|
||
"fragments_dir = project_root / \"output\" / \"ring16_fragments\"\n",
|
||
"output_images_dir = project_root / \"output\" / \"numbered_molecules\"\n",
|
||
"output_images_dir.mkdir(parents=True, exist_ok=True)\n",
|
||
"\n",
|
||
"# 查找所有*_2d.sdf文件\n",
|
||
"sdf_files = list(fragments_dir.glob(\"**/*_2d.sdf\"))\n",
|
||
"print(f\"找到 {len(sdf_files)} 个SDF文件\")\n",
|
||
"\n",
|
||
"# 处理每个文件\n",
|
||
"success_count = 0\n",
|
||
"failed_count = 0\n",
|
||
"failed_files = []\n",
|
||
"\n",
|
||
"for sdf_file in sdf_files:\n",
|
||
" try:\n",
|
||
" # 加载分子\n",
|
||
" supplier = Chem.SDMolSupplier(str(sdf_file))\n",
|
||
" mol = supplier[0]\n",
|
||
" \n",
|
||
" if mol is None:\n",
|
||
" failed_files.append((sdf_file.name, \"无法加载分子\"))\n",
|
||
" failed_count += 1\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 获取编号\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 None:\n",
|
||
" failed_files.append((sdf_file.name, \"编号失败\"))\n",
|
||
" failed_count += 1\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 使用Cairo backend生成PNG（dpi=600）\n",
|
||
" from rdkit.Chem.Draw import rdMolDraw2D\n",
|
||
" \n",
|
||
" drawer = rdMolDraw2D.MolDraw2DCairo(1200, 1200)\n",
|
||
" drawer.SetFontSize(6)\n",
|
||
" drawer.drawOptions().addAtomIndices = False\n",
|
||
" \n",
|
||
" # 高亮环原子\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)\n",
|
||
" \n",
|
||
" # 设置原子注释（编号）\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\n",
|
||
" drawer.DrawMolecule(mol_copy, highlightAtoms=highlight_atoms, highlightAtomColors=atom_colors)\n",
|
||
" drawer.FinishDrawing()\n",
|
||
" \n",
|
||
" # 保存PNG（dpi=600）\n",
|
||
" png_data = drawer.GetDrawingText()\n",
|
||
" output_file = output_images_dir / f\"{sdf_file.stem}_numbered.png\"\n",
|
||
" with open(output_file, 'wb') as f:\n",
|
||
" f.write(png_data)\n",
|
||
" \n",
|
||
" success_count += 1\n",
|
||
" \n",
|
||
" if success_count % 50 == 0:\n",
|
||
" print(f\"已处理 {success_count} 个文件...\")\n",
|
||
" \n",
|
||
" except Exception as e:\n",
|
||
" failed_files.append((sdf_file.name, str(e)))\n",
|
||
" failed_count += 1\n",
|
||
"\n",
|
||
"print(f\"\\n处理完成!\")\n",
|
||
"print(f\"成功: {success_count} 个\")\n",
|
||
"print(f\"失败: {failed_count} 个\")\n",
|
||
"print(f\"输出目录: {output_images_dir}\")\n",
|
||
"\n",
|
||
"if failed_files:\n",
|
||
" print(f\"\\n失败的文件 (前10个):\")\n",
|
||
" for name, reason in failed_files[:10]:\n",
|
||
" print(f\" {name}: {reason}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 17,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"=== 分析编号失败的SDF文件 ===\n",
|
||
"\n",
|
||
"\n",
|
||
"============================================================\n",
|
||
"分析文件: ring16_mol_94_2d.sdf\n",
|
||
"============================================================\n",
|
||
"\n",
|
||
"1. 分子基本信息:\n",
|
||
" 原子数: 57\n",
|
||
" 键数: 59\n",
|
||
"\n",
|
||
"2. 分子结构可视化:\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
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",
|
||
"text/plain": [
|
||
"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x600>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"3. 环信息:\n",
|
||
" 6元环: 2 个\n",
|
||
" 16元环: 1 个\n",
|
||
" 是否有16元环: True\n",
|
||
"\n",
|
||
"4. 尝试编号:\n",
|
||
" 编号失败!\n",
|
||
"\n",
|
||
"5. 失败原因分析:\n",
|
||
" - 找到16元环\n",
|
||
" - 找到酯基: [5, 3, 4, 2]\n",
|
||
" - 原因: 未找到羰基C（C=O中的C）\n",
|
||
"\n",
|
||
"============================================================\n",
|
||
"分析文件: ring16_mol_370_2d.sdf\n",
|
||
"============================================================\n",
|
||
"\n",
|
||
"1. 分子基本信息:\n",
|
||
" 原子数: 50\n",
|
||
" 键数: 51\n",
|
||
"\n",
|
||
"2. 分子结构可视化:\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/jpeg": 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",
|
||
"text/plain": [
|
||
"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x600>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"3. 环信息:\n",
|
||
" 6元环: 1 个\n",
|
||
" 16元环: 1 个\n",
|
||
" 是否有16元环: True\n",
|
||
"\n",
|
||
"4. 尝试编号:\n",
|
||
" 编号失败!\n",
|
||
"\n",
|
||
"5. 失败原因分析:\n",
|
||
" - 找到16元环\n",
|
||
" - 找到酯基: [4, 2, 3, 1]\n",
|
||
" - 原因: 未找到羰基C（C=O中的C）\n",
|
||
"\n",
|
||
"============================================================\n",
|
||
"分析文件: ring16_mol_400_2d.sdf\n",
|
||
"============================================================\n",
|
||
"\n",
|
||
"1. 分子基本信息:\n",
|
||
" 原子数: 48\n",
|
||
" 键数: 49\n",
|
||
"\n",
|
||
"2. 分子结构可视化:\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/jpeg": 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",
|
||
"text/plain": [
|
||
"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x600>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"3. 环信息:\n",
|
||
" 6元环: 1 个\n",
|
||
" 16元环: 1 个\n",
|
||
" 是否有16元环: True\n",
|
||
"\n",
|
||
"4. 尝试编号:\n",
|
||
" 编号失败!\n",
|
||
"\n",
|
||
"5. 失败原因分析:\n",
|
||
" - 找到16元环\n",
|
||
" - 找到酯基: [4, 2, 3, 1]\n",
|
||
" - 原因: 未找到羰基C（C=O中的C）\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 分析编号失败的三个SDF文件\n",
|
||
"failed_sdf_files = [\n",
|
||
" \"ring16_mol_94_2d.sdf\",\n",
|
||
" \"ring16_mol_370_2d.sdf\",\n",
|
||
" \"ring16_mol_400_2d.sdf\"\n",
|
||
"]\n",
|
||
"\n",
|
||
"print(\"=== 分析编号失败的SDF文件 ===\\n\")\n",
|
||
"\n",
|
||
"for sdf_name in failed_sdf_files:\n",
|
||
" print(f\"\\n{'='*60}\")\n",
|
||
" print(f\"分析文件: {sdf_name}\")\n",
|
||
" print('='*60)\n",
|
||
" \n",
|
||
" # 找到文件路径\n",
|
||
" sdf_file = None\n",
|
||
" for f in sdf_files:\n",
|
||
" if f.name == sdf_name:\n",
|
||
" sdf_file = f\n",
|
||
" break\n",
|
||
" \n",
|
||
" if sdf_file is None:\n",
|
||
" print(f\" 错误: 找不到文件 {sdf_name}\")\n",
|
||
" continue\n",
|
||
" \n",
|
||
" try:\n",
|
||
" # 加载分子\n",
|
||
" supplier = Chem.SDMolSupplier(str(sdf_file))\n",
|
||
" mol = supplier[0]\n",
|
||
" \n",
|
||
" if mol is None:\n",
|
||
" print(\" 错误: 无法加载分子\")\n",
|
||
" continue\n",
|
||
" \n",
|
||
" print(f\"\\n1. 分子基本信息:\")\n",
|
||
" print(f\" 原子数: {mol.GetNumAtoms()}\")\n",
|
||
" print(f\" 键数: {mol.GetNumBonds()}\")\n",
|
||
" \n",
|
||
" # 可视化分子\n",
|
||
" print(f\"\\n2. 分子结构可视化:\")\n",
|
||
" from rdkit.Chem import AllChem\n",
|
||
" AllChem.Compute2DCoords(mol)\n",
|
||
" img = Draw.MolToImage(mol, size=(600, 600))\n",
|
||
" display(img)\n",
|
||
" \n",
|
||
" # 检查环信息\n",
|
||
" print(f\"\\n3. 环信息:\")\n",
|
||
" ring_info = mol.GetRingInfo()\n",
|
||
" rings = ring_info.AtomRings()\n",
|
||
" ring_sizes = {}\n",
|
||
" for ring in rings:\n",
|
||
" size = len(ring)\n",
|
||
" ring_sizes[size] = ring_sizes.get(size, 0) + 1\n",
|
||
" \n",
|
||
" for size, count in sorted(ring_sizes.items()):\n",
|
||
" print(f\" {size}元环: {count} 个\")\n",
|
||
" \n",
|
||
" # 检查是否有16元环\n",
|
||
" has_16_ring = any(len(ring) == 16 for ring in rings)\n",
|
||
" print(f\" 是否有16元环: {has_16_ring}\")\n",
|
||
" \n",
|
||
" # 尝试编号\n",
|
||
" print(f\"\\n4. 尝试编号:\")\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 None:\n",
|
||
" print(\" 编号失败!\")\n",
|
||
" \n",
|
||
" # 详细分析失败原因\n",
|
||
" print(f\"\\n5. 失败原因分析:\")\n",
|
||
" \n",
|
||
" # 检查是否有16元环\n",
|
||
" ring_atoms_16 = None\n",
|
||
" for ring in rings:\n",
|
||
" if len(ring) == 16:\n",
|
||
" ring_atoms_16 = list(ring)\n",
|
||
" break\n",
|
||
" \n",
|
||
" if ring_atoms_16 is None:\n",
|
||
" print(\" - 原因: 未找到16元环\")\n",
|
||
" print(f\" - 实际环大小: {sorted(ring_sizes.keys())}\")\n",
|
||
" else:\n",
|
||
" print(\" - 找到16元环\")\n",
|
||
" \n",
|
||
" # 检查是否有酯基\n",
|
||
" from src.ring_visualization import find_ester_smarts\n",
|
||
" ester_atoms, pattern = find_ester_smarts(mol, ring_atoms_16)\n",
|
||
" \n",
|
||
" if ester_atoms is None:\n",
|
||
" print(\" - 原因: 未找到酯基\")\n",
|
||
" print(\" - 可能原因: 分子结构不符合大环内酯的特征\")\n",
|
||
" else:\n",
|
||
" print(f\" - 找到酯基: {ester_atoms}\")\n",
|
||
" \n",
|
||
" # 检查羰基C和酯键O\n",
|
||
" from src.ring_visualization import (\n",
|
||
" get_carbonyl_carbon_in_ring,\n",
|
||
" get_ester_oxygen_in_ring\n",
|
||
" )\n",
|
||
" carbonyl_carbon = get_carbonyl_carbon_in_ring(mol, ester_atoms, ring_atoms_16)\n",
|
||
" ester_oxygen = get_ester_oxygen_in_ring(mol, ester_atoms, ring_atoms_16)\n",
|
||
" \n",
|
||
" if carbonyl_carbon is None:\n",
|
||
" print(\" - 原因: 未找到羰基C（C=O中的C）\")\n",
|
||
" elif ester_oxygen is None:\n",
|
||
" print(\" - 原因: 未找到酯键O（在环上的O）\")\n",
|
||
" else:\n",
|
||
" print(f\" - 找到羰基C: {carbonyl_carbon}\")\n",
|
||
" print(f\" - 找到酯键O: {ester_oxygen}\")\n",
|
||
" print(\" - 原因: 编号逻辑失败（可能是环的连接顺序问题）\")\n",
|
||
" else:\n",
|
||
" print(f\" 编号成功!\")\n",
|
||
" print(f\" 编号范围: 1-{len(ring_numbering)}\")\n",
|
||
" print(f\" 羰基C位置: {ring_numbering.get(carbonyl_carbon, 'N/A')}\")\n",
|
||
" print(f\" 酯键O位置: {ring_numbering.get(ester_oxygen, 'N/A')}\")\n",
|
||
" \n",
|
||
" # 可视化带编号的分子\n",
|
||
" print(f\"\\n5. 带编号的分子结构:\")\n",
|
||
" from src.ring_visualization import draw_mol_with_ring_numbering\n",
|
||
" draw_mol_with_ring_numbering(mol, ring_numbering, ring_atoms, size=(800, 800))\n",
|
||
" \n",
|
||
" except Exception as e:\n",
|
||
" print(f\" 处理出错: {e}\")\n",
|
||
" import traceback\n",
|
||
" traceback.print_exc()\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 关于 \"Can't kekulize mol\" 警告的说明\n",
|
||
"\n",
|
||
"**什么是Kekulization？**\n",
|
||
"- Kekulization是将芳香环的键类型从\"芳香键\"转换为明确的单键/双键的过程\n",
|
||
"- RDKit在处理某些分子时需要这个过程来正确表示结构\n",
|
||
"\n",
|
||
"**为什么会出现这个警告？**\n",
|
||
"- 某些侧链碎片包含芳香环，但RDKit无法自动确定正确的键类型\n",
|
||
"- 这通常发生在：\n",
|
||
" 1. 复杂的芳香系统\n",
|
||
" 2. 非标准芳香环\n",
|
||
" 3. 包含dummy原子的碎片\n",
|
||
"\n",
|
||
"**对分析的影响：**\n",
|
||
"- ✅ **不影响功能**：碎片仍然可以正确提取和保存\n",
|
||
"- ✅ **不影响SMILES**：SMILES字符串仍然有效\n",
|
||
"- ✅ **不影响统计**：原子数、分子量等属性计算正常\n",
|
||
"- ⚠️ **仅影响显示**：某些可视化可能不够美观，但不影响数据\n",
|
||
"\n",
|
||
"**是否需要处理？**\n",
|
||
"- 对于统计分析：**不需要处理**，可以忽略这些警告\n",
|
||
"- 对于可视化：如果需要，可以使用 `RDLogger.DisableLog('rdApp.*')` 来抑制警告（已在代码中添加）\n",
|
||
"\n",
|
||
"**结论：**\n",
|
||
"这些警告是**无害的**，不会影响侧链断裂分析的准确性。可以安全地忽略它们。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"开始批量断裂侧链...\n",
|
||
"已处理 50 个分子...\n",
|
||
"已处理 100 个分子...\n",
|
||
"已处理 150 个分子...\n",
|
||
"已处理 200 个分子...\n",
|
||
"已处理 250 个分子...\n",
|
||
"已处理 300 个分子...\n",
|
||
"已处理 350 个分子...\n",
|
||
"已处理 400 个分子...\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stderr",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[15:19:50] Can't kekulize mol. Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
|
||
"[15:19:50] Can't kekulize mol. Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
|
||
"[15:19:50] Can't kekulize mol. Unkekulized atoms: 1 2 3 4 5 6 7 8 9\n",
|
||
"[15:19:51] Can't kekulize mol. Unkekulized atoms: 1 2 3 4 5\n",
|
||
"[15:19:51] Can't kekulize mol. Unkekulized atoms: 12 15 16 18 19\n",
|
||
"[15:19:51] Can't kekulize mol. Unkekulized atoms: 1 2 3 4 5\n",
|
||
"[15:19:51] Can't kekulize mol. Unkekulized atoms: 12 15 16 18 19\n",
|
||
"[15:19:51] Can't kekulize mol. Unkekulized atoms: 24 28 29 34 35 36 40 41 44\n",
|
||
"[15:19:51] Can't kekulize mol. Unkekulized atoms: 11 14 15 17 18\n",
|
||
"[15:19:51] Can't kekulize mol. Unkekulized atoms: 11 14 15 17 18\n",
|
||
"[15:19:51] Can't kekulize mol. Unkekulized atoms: 11 14 15 17 18\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"已处理 450 个分子...\n",
|
||
"已处理 500 个分子...\n",
|
||
"已处理 550 个分子...\n",
|
||
"已处理 600 个分子...\n",
|
||
"已处理 650 个分子...\n",
|
||
"已处理 700 个分子...\n",
|
||
"已处理 750 个分子...\n",
|
||
"\n",
|
||
"处理完成! 共处理 792 个分子\n",
|
||
"\n",
|
||
"=== 统计1: 每个位置的碎片数量 ===\n",
|
||
"位置 | 碎片数量\n",
|
||
"--------------------\n",
|
||
" 3 | 177\n",
|
||
" 4 | 473\n",
|
||
" 5 | 735\n",
|
||
" 6 | 587\n",
|
||
" 7 | 615\n",
|
||
" 8 | 227\n",
|
||
" 9 | 678\n",
|
||
" 10 | 473\n",
|
||
" 11 | 170\n",
|
||
" 12 | 72\n",
|
||
" 13 | 412\n",
|
||
" 14 | 73\n",
|
||
" 15 | 504\n",
|
||
" 16 | 726\n",
|
||
"\n",
|
||
"统计结果已保存到: /home/zly/project/macro_split/output/position_fragment_count.csv\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 批量断裂侧链并统计信息\n",
|
||
"from rdkit.Chem import Descriptors\n",
|
||
"from collections import defaultdict\n",
|
||
"import pandas as pd\n",
|
||
"\n",
|
||
"# 重新导入侧链断裂函数（从之前的单元格）\n",
|
||
"# 这里假设之前的单元格已经定义了这些函数\n",
|
||
"\n",
|
||
"# 统计每个位置的碎片数量（除了位置1和2，因为它们是酯基）\n",
|
||
"position_fragment_count = defaultdict(int)\n",
|
||
"position_fragments = defaultdict(list) # 存储每个位置的所有碎片信息\n",
|
||
"\n",
|
||
"print(\"开始批量断裂侧链...\")\n",
|
||
"processed_molecules = 0\n",
|
||
"\n",
|
||
"for sdf_file in sdf_files:\n",
|
||
" try:\n",
|
||
" # 加载分子\n",
|
||
" supplier = Chem.SDMolSupplier(str(sdf_file))\n",
|
||
" mol = supplier[0]\n",
|
||
" \n",
|
||
" if mol is None:\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 获取编号\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 None:\n",
|
||
" continue\n",
|
||
" \n",
|
||
" # 获取分子ID\n",
|
||
" mol_id = sdf_file.parent.name\n",
|
||
" \n",
|
||
" # 断裂所有侧链（除了位置1和2）\n",
|
||
" for position in range(3, 17): # 位置3-16\n",
|
||
" fragments = cleave_side_chain_at_position(mol, ring_atoms, ring_numbering, position)\n",
|
||
" \n",
|
||
" for frag_smiles in fragments:\n",
|
||
" if frag_smiles:\n",
|
||
" position_fragment_count[position] += 1\n",
|
||
" \n",
|
||
" # 计算碎片属性\n",
|
||
" frag_mol = Chem.MolFromSmiles(frag_smiles)\n",
|
||
" if frag_mol:\n",
|
||
" frag_info = {\n",
|
||
" 'position': position,\n",
|
||
" 'smiles': frag_smiles,\n",
|
||
" 'atom_count': frag_mol.GetNumAtoms(),\n",
|
||
" 'molecular_weight': Descriptors.MolWt(frag_mol),\n",
|
||
" 'parent_id': mol_id\n",
|
||
" }\n",
|
||
" position_fragments[position].append(frag_info)\n",
|
||
" \n",
|
||
" processed_molecules += 1\n",
|
||
" \n",
|
||
" if processed_molecules % 50 == 0:\n",
|
||
" print(f\"已处理 {processed_molecules} 个分子...\")\n",
|
||
" \n",
|
||
" except Exception as e:\n",
|
||
" continue\n",
|
||
"\n",
|
||
"print(f\"\\n处理完成! 共处理 {processed_molecules} 个分子\")\n",
|
||
"\n",
|
||
"# 统计1: 每个位置的碎片数量\n",
|
||
"print(\"\\n=== 统计1: 每个位置的碎片数量 ===\")\n",
|
||
"print(\"位置 | 碎片数量\")\n",
|
||
"print(\"-\" * 20)\n",
|
||
"for position in sorted(position_fragment_count.keys()):\n",
|
||
" count = position_fragment_count[position]\n",
|
||
" print(f\"{position:4d} | {count:6d}\")\n",
|
||
"\n",
|
||
"# 保存统计结果\n",
|
||
"stats_data = {\n",
|
||
" 'position': list(position_fragment_count.keys()),\n",
|
||
" 'fragment_count': [position_fragment_count[p] for p in sorted(position_fragment_count.keys())]\n",
|
||
"}\n",
|
||
"stats_df = pd.DataFrame(stats_data)\n",
|
||
"stats_df.to_csv(output_images_dir.parent / 'position_fragment_count.csv', index=False)\n",
|
||
"print(f\"\\n统计结果已保存到: {output_images_dir.parent / 'position_fragment_count.csv'}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"metadata": {},
|
||
"source": [
|
||
"## 11. 扩展到其他环大小（可选）\n",
|
||
"\n",
|
||
"如果需要扩展到其他环大小，可以使用以下代码检查14, 15, 17, 18元环的分子数量。\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 19,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"已保存: /home/zly/project/macro_split/output/atom_count_distribution_ring16.png\n",
|
||
"已保存: /home/zly/project/macro_split/output/molecular_weight_distribution_ring16.png\n",
|
||
"\n",
|
||
"所有分布图已生成完成!\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 统计2: 每个位置的碎片分布情况（原子数、分子量等）\n",
|
||
"# 需要安装seaborn: pixi add seaborn matplotlib\n",
|
||
"\n",
|
||
"try:\n",
|
||
" import seaborn as sns\n",
|
||
" import matplotlib.pyplot as plt\n",
|
||
" import numpy as np\n",
|
||
"except ImportError:\n",
|
||
" print(\"需要安装seaborn和matplotlib:\")\n",
|
||
" print(\"pixi add seaborn matplotlib\")\n",
|
||
" raise\n",
|
||
"\n",
|
||
"# 准备数据\n",
|
||
"properties_to_plot = [\n",
|
||
" ('atom_count', 'Atom Count', 'Number of Atoms'),\n",
|
||
" ('molecular_weight', 'Molecular Weight', 'Molecular Weight (Da)')\n",
|
||
"]\n",
|
||
"\n",
|
||
"ring_size = 16\n",
|
||
"positions_to_plot = list(range(3, ring_size + 1)) # 位置3-16\n",
|
||
"\n",
|
||
"# 为每个属性创建图表\n",
|
||
"for prop_key, prop_title, prop_label in properties_to_plot:\n",
|
||
" fig, axes = plt.subplots(4, 4, figsize=(20, 16))\n",
|
||
" fig.suptitle(f'{prop_title} Distribution for {ring_size}-Membered Macrolactones', \n",
|
||
" fontsize=16, fontweight='bold')\n",
|
||
" \n",
|
||
" axes = axes.flatten()\n",
|
||
" \n",
|
||
" for idx, position in enumerate(positions_to_plot):\n",
|
||
" ax = axes[idx]\n",
|
||
" \n",
|
||
" if position in position_fragments and len(position_fragments[position]) > 0:\n",
|
||
" # 提取该位置的所有属性值\n",
|
||
" values = [frag[prop_key] for frag in position_fragments[position]]\n",
|
||
" \n",
|
||
" # 绘制分布图（使用KDE平滑）\n",
|
||
" sns.histplot(values, kde=True, ax=ax, bins=30, color='steelblue', alpha=0.7)\n",
|
||
" ax.set_title(f'Position {position}\\n(n={len(values)})', fontsize=10)\n",
|
||
" ax.set_xlabel(prop_label, fontsize=9)\n",
|
||
" ax.set_ylabel('Frequency', fontsize=9)\n",
|
||
" ax.grid(True, alpha=0.3)\n",
|
||
" else:\n",
|
||
" ax.text(0.5, 0.5, f'Position {position}\\nNo fragments', \n",
|
||
" ha='center', va='center', transform=ax.transAxes, fontsize=10)\n",
|
||
" ax.set_title(f'Position {position}', fontsize=10)\n",
|
||
" \n",
|
||
" # 隐藏多余的子图\n",
|
||
" for idx in range(len(positions_to_plot), len(axes)):\n",
|
||
" axes[idx].axis('off')\n",
|
||
" \n",
|
||
" plt.tight_layout()\n",
|
||
" \n",
|
||
" # 保存图片\n",
|
||
" output_file = output_images_dir.parent / f'{prop_key}_distribution_ring{ring_size}.png'\n",
|
||
" plt.savefig(output_file, dpi=600, bbox_inches='tight')\n",
|
||
" print(f\"已保存: {output_file}\")\n",
|
||
" plt.close()\n",
|
||
"\n",
|
||
"print(\"\\n所有分布图已生成完成!\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 20,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"碎片数据已保存到: /home/zly/project/macro_split/output/all_fragments_data.csv\n",
|
||
"共 5922 个碎片\n",
|
||
"\n",
|
||
"数据预览:\n",
|
||
" position smiles atom_count molecular_weight parent_id\n",
|
||
"30 3 *C 2 15.035 ring16_mol_265\n",
|
||
"31 3 *C 2 15.035 ring16_mol_266\n",
|
||
"43 3 *C 2 15.035 ring16_mol_324\n",
|
||
"51 3 *C 2 15.035 ring16_mol_377\n",
|
||
"54 3 *C 2 15.035 ring16_mol_388\n",
|
||
"57 3 *C 2 15.035 ring16_mol_391\n",
|
||
"108 3 *C 2 15.035 ring16_mol_833\n",
|
||
"110 3 *C 2 15.035 ring16_mol_889\n",
|
||
"122 3 *C 2 15.035 ring16_mol_1008\n",
|
||
"131 3 *C 2 15.035 ring16_mol_1030\n",
|
||
"\n",
|
||
"=== 碎片统计摘要 ===\n",
|
||
"总碎片数: 5922\n",
|
||
"平均原子数: 5.29\n",
|
||
"平均分子量: 63.11\n",
|
||
"\n",
|
||
"每个位置的碎片数:\n",
|
||
"position\n",
|
||
"3 177\n",
|
||
"4 473\n",
|
||
"5 735\n",
|
||
"6 587\n",
|
||
"7 615\n",
|
||
"8 227\n",
|
||
"9 678\n",
|
||
"10 473\n",
|
||
"11 170\n",
|
||
"12 72\n",
|
||
"13 412\n",
|
||
"14 73\n",
|
||
"15 504\n",
|
||
"16 726\n",
|
||
"dtype: int64\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 统计3: 保存所有碎片数据到CSV文件，方便后续分析\n",
|
||
"all_fragments_data = []\n",
|
||
"\n",
|
||
"for position in sorted(position_fragments.keys()):\n",
|
||
" for frag_info in position_fragments[position]:\n",
|
||
" all_fragments_data.append(frag_info)\n",
|
||
"\n",
|
||
"if all_fragments_data:\n",
|
||
" fragments_df = pd.DataFrame(all_fragments_data)\n",
|
||
" fragments_df = fragments_df.sort_values(['position', 'molecular_weight'])\n",
|
||
" \n",
|
||
" output_csv = output_images_dir.parent / 'all_fragments_data.csv'\n",
|
||
" fragments_df.to_csv(output_csv, index=False)\n",
|
||
" \n",
|
||
" print(f\"碎片数据已保存到: {output_csv}\")\n",
|
||
" print(f\"共 {len(fragments_df)} 个碎片\")\n",
|
||
" print(f\"\\n数据预览:\")\n",
|
||
" print(fragments_df.head(10))\n",
|
||
" \n",
|
||
" # 统计摘要\n",
|
||
" print(f\"\\n=== 碎片统计摘要 ===\")\n",
|
||
" print(f\"总碎片数: {len(fragments_df)}\")\n",
|
||
" print(f\"平均原子数: {fragments_df['atom_count'].mean():.2f}\")\n",
|
||
" print(f\"平均分子量: {fragments_df['molecular_weight'].mean():.2f}\")\n",
|
||
" print(f\"\\n每个位置的碎片数:\")\n",
|
||
" print(fragments_df.groupby('position').size())\n",
|
||
"else:\n",
|
||
" print(\"没有找到碎片数据\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 21,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"=== 测试断裂分子1的位置 [6] ===\n",
|
||
"\n",
|
||
"位置 6 的侧链碎片 (1 个):\n",
|
||
" 碎片1: *OC1OC(C)C(O)C(N(C)C)C1O\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
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|
||
"image/png": "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",
|
||
"text/plain": [
|
||
"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 测试断裂指定位置\n",
|
||
"test_positions = [6] # 测试前3个位置\n",
|
||
"print(f\"\\n=== 测试断裂分子1的位置 {test_positions} ===\")\n",
|
||
"for pos in test_positions:\n",
|
||
" fragments = cleave_side_chain_at_position(mol1, ring_atoms1, numbering1, pos)\n",
|
||
" if fragments:\n",
|
||
" print(f\"\\n位置 {pos} 的侧链碎片 ({len(fragments)} 个):\")\n",
|
||
" for i, frag_smiles in enumerate(fragments, 1):\n",
|
||
" print(f\" 碎片{i}: {frag_smiles}\")\n",
|
||
" frag_mol = Chem.MolFromSmiles(frag_smiles)\n",
|
||
" if frag_mol:\n",
|
||
" display(Draw.MolToImage(frag_mol, size=(300, 300)))\n",
|
||
" else:\n",
|
||
" print(f\"位置 {pos}: 无侧链\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"准备对齐:\n",
|
||
" 参考分子(1109)酯基原子: [37, 38, 39, 40]\n",
|
||
" 目标分子(1110)酯基原子: [37, 38, 39, 40]\n",
|
||
"\n",
|
||
"原子映射: [(37, 37), (38, 38), (39, 39), (40, 40)]\n",
|
||
"\n",
|
||
"对齐成功! RMSD: 0.0000\n"
|
||
]
|
||
},
|
||
{
|
||
"data": {
|
||
"image/png": 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DQ8Nly5YBmDNnTsFZu6/S6NJBAP7+uH0bn3yC4cPzWxQKLF8OAD/8wE3mqJRhQUhUmFmfPnfLlYuLi/vpp58KGletWmVoaPjLL79cu3ZNwmwTJ058/vz5119/LWEGIiIq9YYMGQJg165dCoVCMz326tWre/fuycnJs9+YkKnRpYMi8XTiadNQMBS5axdCQ1GzJvr310QAIg1iQUhUhNWrV+vo6KxatSosLExsqVmz5rhx45RK5ffffy9tNn19/XLlykmbgYiISremTZvWqlUrJiYmMDBQY52uXr3a0NBwy5Ytr05V1fTSQeDS6dORAKytMXp0QYj8GaQ//ABdXQ1kINIkFoRERWjcuPEXX3yRm5s7c+bMgsa5c+dWqFDh9OnTBw8elCTVvXv3evbsef36dUl6JyIirTJo0CAAv/32m8Z6tLe3nzx5slKpHD9+vFKpFBs1unQQADBzwYLq589vmDIFJib5TQcO4M4dVK2KL77QQAAiDePB9ERFe/r0aa1atTIyMs6cOdOmTRuxce3atRMnTqxZs+bdu3cNDQ01HCktLe3IkSPNmjWrUaOGhrsmIiJtExYW5uDgYG5uHhsba2xsrJlOMzMz69SpExUVtXXr1pEjR166dKlNmzZyuXz//v2amSx66dKlFi1alC1bNiIiomA+jrJ1a53gYPj4YOJEDWQg0jCOEBIVrUqVKuLsUPHTSrHxm2++qV+/fnh4+Lp16zQfyczMbMCAAawGiYhIA+zt7Zs2bZqamnrkyBGNdWpiYrJ48WIAM2bMiIiI0PTSQWDBggUAJk6cWFANHj9+vMGDB1d79MCoUZrJQKRhLAiJ3mr69OnVqlW7cePG9u3bxRY9Pb2lS5cCWLBgQUJCgobznD17NjMzU8OdEhGR1ho8eDCA33//XcOdtmvXLj4+3sXFRZNLBwHcunXr8OHDJiYmHh4eBY2LFi26l5Bw0tkZmhomJdIwFoREb2VsbLxo0SIAM2bMSEtLExu7devWtWvXFy9ezJs3T8N51qxZU3ASBhERkboNHjxYV1c3ICAgJSVF3X0lJCRcvHhxx44dc+fONTY2lslk4eHhBgYG/fv3f/r0qbp7F/3000+CIIwdO7ZixYpiy8WLF8+ePVu2bNlvvvlGMxmINI9rCIneRRAEZ2fn4ODgOXPmzJ8/X2y8f/9+o0aNlErlzZs369evL21CIiIi9enYseOpU6d+/fXXL7/8UlX3TE5OfvS6O3fuxMbGFrqscuXKz549E7+3sbFp3bq1i4tLly5dbG1tVZXkVWFhYbVr19bT0wsPD69SpYrY2L179yNHjsydO/fHH39UR6dExQELQqJ/cfHixVatWhkZGd2/f7/gl9D48ePXr1/fsWPHEydOSBuPiIhIfbZs2TJ69OjOnTsfO3bsA56ekJAQFhYWGhoaGhoaFhYmfv/ixYs3ryxXrpy9vb2Dg0PB/+rp6V26dOn06dNnzpxJTEwsuPLTTz/t0KFD+/bt27VrV6lSpQ//2V43cuTIbdu2jR07duPGjWLLzZs3mzRpYmJiEhERUaFCBVV1RFTcsCAk+neDBw/etWvXkCFDduzYMXz48FOnTuXl5b148UIulw8bNqxly5aWlpbly5cvX768+E2ZMmVUnmHp0qVTp07V5fFHRESkQS9evLC2ts7Ly3vy5Im1tfU7rnzPcT8A5cqVq1mzpt0b3nHzR48enfhHcnJyQbudnZ2Li4uLi0vHjh0tLS0/+MeMjo62t7dXKBT37993cHAQG/v37793796pU6cuX778g+9MVPyxICT6d0+ePKlVq1ZWVpatrW1ERMT7PMXIyKhy5co2NjYW71S5cuX3zJCenq6OOpOIiOjd+vTpc+DAgdWrVxdstaLW2u/dFArFzZs3g4KCgoODjx07VrC0XkdHp3bt2uK00k6dOhXsEfqeJkyYsG7dui+++GLHjh1iS0hISL169fT19R89evT+v6yJSiIWhETvZfr06cuWLQMgk8kGDhxob29fvXr11NTUxMTEpKSkpKSkhISEpKQk8Y/p6enveVtjY2NxUNHS0rJgmPHVP5YrV06hUNjb27MaJCIiSezZs2fgwIFVqlRp06bNv875FCd8Fsz8VOtMy7y8vFu3bonDhkFBQdnZ2WK7rq7uZ5995uLi4uTk1K5dOzMzs3ffJy4urkaNGtnZ2bdv3y7YGmDEiBHbt28fN27c+vXr1fcjfLDw8HAbGxsTExOpg1BpwIKQ6N9FRER069YtJCTExsZm9+7dzs7O//qUrKysmJiYZ8+eJb9dTEzMq/Ne3q18+fJz5859dSNsIiIidXv27Nn333+/e/duIyOjjIwMsbHIcb8aNWrIZDKpcmZlZV27di04OPjEiRNnz57Nzc0V2/X09Bo1aiROK3V2djY0NHzzueJnvn379t27d6/YEhUVZW9vLwjCw4cPi+fxv999911oaOiBAwf09PSkzkIlHgtCon9x5coVNze3uLi4+vXrBwQEqHZzs8zMTHGAURxaTExMLPhGbH/69GlERIQgCOL/Vdu1azdv3ry2bduqMAMREdGbcnNzV65cuXDhwoyMDFNT0xEjRrRo0UID434fLyMj48KFC+Kw4eXLl+VyudhuYmLSqlUrJyen1q1bt2nTxsDAAEBSUlL16tXT0tIuX77crFkz8cpx48Zt3Lhx+PDhvr6+kv0Y76RQKPr371+mTJnt27dLWIdT6cCCkOhdDhw4MHTo0MzMTBcXlz/++KNs2bKSxIiJifnyyy8vXbokngTl5OT0448/duzYUZIwRERU6p08edLDw+PevXsAXF1d16xZU716dalDfYj09PSLFy+K00qvX79e8L7X1NS0ZcuWLi4ujx492rx5c5cuXY4ePSo+FBsba2dnl5OTc/v27Xr16kmXvQgJCQnTp09ft26dsbFxVlaWi4tL+/btFy5cKHUuKtlYEBK91erVq6dMmaJUKkeOHLlp0yZ9fX1p86Snp69bt27JkiXiRFMnJ6f58+d36NBB2lRERFSaREdHz5o1S9xbpVatWqtXr+7SpYvUoVQjPj4+MDDw1KlTp0+ffvjw4asPWVlZtWnTZsWKFVWrVp02bdqKFSv69+/v5+cnVdS3EQThq6++SkxM3Ldvn56eXkJCgpOT05QpU8aOHSt1NCrBWBASFUGhUHz33Xdr1qyRyWRz586dN2+e1IleYllIRETqkJWVtWTJkiVLlmRnZ5uamk6bNu2HH34Q51WWPrGxsefOnTtx4sRff/31+PFjsVFXV3f69Olr165NS0u7evWqo6OjtCFflZKSIpPJzM3N5XK5m5ubra3tpk2bADx+/DglJeWzzz6TOiCVYCwIiQrLyMgYMmTIoUOHDA0Nf/nll6FDh0qdqAhpaWnr169nWUhERCrh7+/v4eEREREhk8m++OKLpUuXvvvUwdLk+vXrPj4+/v7+SUlJYkvXrl2PHDkibapCli5deuzYsSNHjhgYGGRmZnbs2LFHjx6zZ8+WOheVBiwIiV4TExPTs2fPq1evWlpa7t+/v02bNlInepc3y8IFCxa0b99e6lxERFRiPHz4cPLkyWL907hx4zVr1jg5OUkdSgIKhWLo0KF79+7Ny8urW7fun3/+WayWTSqVykGDBuno6Pz22286OjpxcXH79u0bN26c1LmoNGBBSPTS3bt3e/ToERkZaWdnd/jw4Vq1akmd6L2wLCQiog+QkZGxbNmyxYsX5+TkWFhYeHl5TZgwQVdXV+pcUrp8+fLAgQMjIiIqVKjg6+vbvXt3afPk5OTMmjVr3rx5ZcqUyc3NdXV1dXBwWLdunbSpqJTRkToAUXFx8uRJJyenyMjIFi1aXLhwoaRUgwDMzMw8PT0jIyMXL15crly54ODgDh06tG7dOjAwUOpoRERUHAmC4OfnV6dOnR9//FEulw8bNuzBgweTJk3S8moQQPPmza9cudKtW7eEhARXV9dJkyYVnFohCUNDw9zc3F69euXm5hoYGPj5+eXk5GRlZUkYiUofjhASAcC2bdvGjBkjl8v79eu3Y8cOY2NjqRN9IHG0cPHixS9evADg5OS0cOHCdu3aSZ2LiIiKi5s3b06cODEoKAhAs2bN1q5d27x5c6lDFS+CIPj4+Hz//fdyubx58+a7d+/W/PRRhUKRkZFhbm6uUCgGDBhgZGS0c+dOHjlI6sARQtJ2giDMmzdv5MiRcrncw8Njz549JbcaxD+jhVFRUQWjhe3bt2/duvWZM2ekjkZERBJLTk6eNGlS06ZNg4KCbGxsNm3adPHiRVaDb5LJZJMmTQoKCqpevbp4YP3hw4c1nGHXrl1du3bNzMzU1dXduXNnUlJSaGiohjOQluAIIWm1nJycUaNG7dy5U1dXd82aNaVscXZqauqGDRsKRgtdXFwWLlz4+eefS52LiIg0TalU7ty5c9q0ac+fP9fX1x83btyCBQvMzc2lzlXcJSQkDB8+/MiRIzKZbOLEicuXL9fYocSCIHz99dfx8fEHDhzQ09PTTKeknThCSNorKSmpS5cuO3fuLFOmzMGDB0tZNQjA3Nzc09MzPDzcy8urXLlyJ06caNGiRadOnS5duiR1NCIi0pyrV686OTmNGDHi+fPn7du3v379+urVq1kNvo8KFSr8+eef3t7eenp6Pj4+rVu3joiIUHenK1eufPHihUwm27Rpk1Kp9PPzU3ePpOU4Qkha6tGjRz169AgJCalcuXJAQEDjxo2lTqReSUlJPj4+3t7eKSkpAFxcXBYtWsRpQkREpVtiYuL8+fPXrl2rVCqrVKny008/DR8+XOpQJZImdx+dNm3alStXjh07ZmRkJO4lo76+iMCCkLTTxYsXe/XqFR8f37Bhw4CAgKpVq0qdSEMSExPXrFnDspCIqNTLy8tbt26dl5dXSkqKgYHBN998s2jRojJlykidqwTTwPTR7OxsIyMjQRC++OILa2vrFStWqPb+REViQUhaZ+/evcOGDcvKyurcubOfn58WzplhWUhEVLoFBgZ6eHj8/fffAFxdXVevXm1nZyd1qNJArbuPBgUFeXh4BAYGmpub5+bmpqenW1paqurmRO/AgpC0y+rVq6dMmaJUKkeNGrVhwwZtXqVdqCxs3779yJEjhw0bJnUuIiL6cE+fPp05c+bOnTsFQbC3t/f29u7Ro4fUoUqbc+fODR48+OnTpyqfPjphwoTw8PAjR46o6oZE74MFIWkLhULh4eGxfv16mUw2d+7cefPmSZ2oWEhISFi2bNn69evT09MBfPrpp0ePHq1Ro4bUuYiI6L+Ry+Xr16+fM2dOWlqaiYnJ999/P3PmTENDQ6lzlU6qnT66f//+1q1bV6xYUalU3rhxw9HRUYVRif4VC0LSCunp6YMGDfrzzz+NjIx+/fXXQYMGSZ2oeElISBg0aNCpU6cEQShXrlxUVJSZmZnUoYiI6H0tXLhw+/bt4jl1rq6ua9eutbW1lTpUKafC6aPz5s07fPhwYGCgiYmJSjMSvRcWhFT6PXv2zM3N7fr16+XLlz9w4EDr1q2lTlRMBQYGuri4KBSKPn367N27VyaTSZ2IiIj+xYsXL1q2bBkSEgKgQYMGa9asadu2rdShtMhHTh9VKpU6OjoAfvvtt4EDB+rq6qonJtG78BxCKuX+/vvvFi1aXL9+3d7e/sKFC6wG36Fdu3YHDx40Nzffv3//jz/+KHUcIiL6d05OTiEhITo6Om5ublevXmU1qGHOzs43b97s1q1bQkKCq6vrpEmT5HL5ez730aNHzZo1S0xMBDBkyBBWgyQVFoRUmi1fvtzJySk6OrpVq1YXLlxwcHCQOlFx16NHj127dunp6c2fP9/X11fqOERE9C67du26d+8egO3btx86dIgH1knigw+vt7Oz++KLL8Q1/EQS4pRRKp3u3bs3YcKE06dPAxg6dOjWrVv5a/L9bdmyZfTo0fr6+ocPH3ZxcZE6DhERFSEsLMzR0TE1NXXu3Lmc1lEcvP/00WvXrllbW1epUkWT8cwNlMwAACAASURBVIjehiOEVKokJiZu3769e/fu9erVE6vB5s2bb9++ndXgfzJq1Khp06bJ5fJ+/fqJx1gREVGxkpOTM3DgwNTUVHd3d1aDxcT7Tx8NDg6+cuWKhuMRvQ1HCKk0iIuL27dvn5+f39mzZxUKBQAdHZ2KFSvOmjVr4sSJUqcrkQRBGDp06O+//169evWLFy9aWVlJnYiIiF4aN27cxo0b7e3tr169WrZsWanj0EtqPbyeSB1YEFIJlpiY+Oeff/r5+R09ejQvLw+AoaFhp06d3Nzc+vTpU7FiRakDlmxZWVkuLi7nz593dHQ8c+aMqamp1ImIiAgA9uzZM3DgQENDw/Pnzzdp0kTqOFSEV6ePbtu2rUePHgBSUlI2btzo6ekpdTqi17AgpJInISHh8OHDb9aB7u7uvXr14gelKpSQkNCqVavQ0NAePXocPHiQG6AREUmuYOng+vXrx40bJ3UcequCw+sBNGrU6Pz58yYmJoIg8FQnKm5YEFKJwTpQEuHh4S1atEhISPjuu+9WrlwpdZzXLF269MmTJ7Nnz65UqZLUWYiINCEnJ6dVq1bXr193d3ffs2eP1HHoXyiVygkTJmzYsAHA559/fvHiRakTERWBBSEVd2/WgUZGRi4uLu7u7r179zY3Ny/yWdnZ2X/99Zefn5+hoeHPP/+s2cilzblz5zp16pSTk+Pj41NM1mTeuHGjU6dOiYmJ4mLRpUuXDhs2jJ+5EpHKpKbCwABGRlLnKIxLB0uiVatWTZ06VRCEDRs2fPPNN1LHISqMBSEVU0+ePNm7d6+fn9+FCxeUSiXerw7Myso6fPjwH3/8ERAQIB7sU6ZMmYSEBENDQ42mL3V27949ePBgmUy2b9++Xr16SZgkJiZm9uzZ27ZtUyqVYjUYFxcHoG3btj4+Pg0bNpQwGxGVeLGxmD0b+/YhORkAatXCt99iwgToFItd2bl0sORq3759YGBg/fr1b968yfUXVOwIRMVJdHS0t7e3k5OTzj+/fY2MjFxdXX19fVNSUt72rKysrEOHDg0bNszMzKzg33bdunW9vLwePnyoyfylmLituYmJycWLFyUJkJOT4+3tLX4WoK+vP378+MjISKVS6evrK26CqqOjM2zYsPj4eEniEVGJ9/y5UKOG8MknwubNws2bQlCQMGWKoKMjjB4tdTJBEITQ0FDxBXD9+vVSZ6H/LDMzs0aNGgA2bNggdRaiwlgQUrEQFRUl1oEFs/4K6sDU1NS3PSszM/NtdWBoaKgm82uJsWPHArCxsYmIiNBw14cOHRJ/lQJwcXG5e/fuq48mJyd7enqKp01aWFh4e3vn5eVpOCERlXhjxwplygjh4a81LlsmAMKZMxJlypednS0OCbq7u0ubhD7Y3r17AVhaWj5//lzqLESvYUFIUmIdWLLk5uZ26tRJ/K+dnJysmU6vXLni7Oxc8Ld85MiRt1354MGDbt26iVd+9tlnZ6R+A0dEJUlenmBuLowYUbg9K0soU0YYM0aCSK8QF57Z29u/ePFC2iT0McRfUt98843UQYhew4KQJPBmHWhsbPz+dWCZMmUK1YFhYWGazK/NUlJSGjRoAKBdu3Y5OTlq7evJkydjxowRJw9XqFDhPcf9Xh1LdHV11fxgJhGVSOHhAiAsXlzEQ40aCS1aaDzQS7t37wZgaGh47do1CWPQx3v48KGhoaGOjs7ly5elzkL0EgtC0pyoqKiePXuam5sX1IGmpqYDBgzw8/PLyMh427NYBxY3jx8/tra2BvDVV1+pqYuMjIzFixeLI8AGBgYeHh5vW0G6cePGs2fPFmoUVxuK/2BMTEy8vLyysrLUFJWISolbtwRA2Lq1iIc6dxbq1s3/fvlywclJOHFCY7m4dLCU+f777wG0bNlSqVRKnYUoHwtC0pw2bdqI5ZyBgYE4HpiWlva2i9+sA3V0dBwdHb28vMILLfAgjbt69aqpqSmARYsWqfbOSqVyz549tra2BUN87/jrDg8PNzIyettIYGRkpLu7u3ifWg4O2X/+qdqoRFSqPHokAML//V8RDxWMECqVwqefCoAACG3aCMeOqTsUlw6WPqmpqZUrVwbg6+srdRaifCwISXMsLCwAiMfHve2ajIwMsQ4U6w2xDnRycvL29n7y5Ikm09K7BQQE6OrqymSyHTt2qOqely5datWqlfj37ujo+K+LAN9nJDAwMLBhw4a+zs4CIHToINy5o6q0RFSqKBRC2bL/voYwJUVYvFiwtMwvCxs1Enx9BbXtYsWlg6XSjh07AFhZWfGvlYoJFoSkOVWqVAHg7+//5kOsA0uiVatWieO9p06d+shbRUdHF5wsX7ly5U2bNikUivd87pMnTwqeW7NmzT179hS6QC6XZ61fL1hYCICgry9MnSq8/QgTItJeo0cLZmZCZORrjT4+AiAEBr7WmJYmeHsLlSvnl4X16gm+voJcrto4XDpYWimVyrZt2wKYMmWK1FmIBIEFIWlS1apVAURFRRW0vKMOfPr0qYRR6T15eHiIm2iHhIR82B3S09O9vLyMjY3FvYU8PT3fsbHQO4gjgeK/oo4dO955cyQwMVHw8BB0dQVAKF9e8PYW3rvmJCKtEBsrVKki2NkJe/YI0dHC3bvCvHmCnp4wcmTR12dnC76+gr19fllYvbrg7S1kZqokC5cOlm5///23np6enp7e7du3pc5CxIKQNMjExARAenp6QYv48SfrwJJLoVD07t0bgJ2dXVxc3H99rq+vr42NDQCZTObu7v748eOPDOPr61uhQgUA+vr6Hh4eRczGuX5daN06/92bo6Nw/vzH9EhEpU1kpDBggGBklP8qYWMj/N//CXl5gkIhjB5d9GmEubmCr69Qu3b+U6ysBC+vj5yGwKWD2mD8+PEAOnToIHUQIhaEpCnZ2dni1JdXG9PT07t06bJ+/frY2FipgtFHyszM/PzzzwE0a9bsHbvFFnL69OnGjRuLHwc0a9YsKChIVXmeP39ecFiFtbX1i99+Ewrt5KZUCnv2CNWqCYAgkwnDhgn850dEr8rOFiIihGfPXr56/PFHfr3n5CQcOlT4VUUQBIVC2LVLaNQo/7KKFYWFCxUfel4rlw5qg6SkpIoVKwLw8/OTOgtpOxaEpCFPnz4Vl4dJHYRULyYmRtwX1N3d/V/X/kVGRg4bNkwsBT/55BNfX191bL19/fp1Z2fnpS1avHUkMCND8PISDA0FQChTRvDyEtR8rCIRlWDiXjLly+fXew0bvnUvmb/+Elq2FADB0LC1nZ2Hh8ezZ8/+U1dcOqg9Nm3aBKBq1aqvTp4i0jwWhKQht2/fBlC/fn2pg5Ba3L17t1y5cgA8PT3fdk1aWpqXl5d4UIS4KWimihbbFEmpVGbu2pW/64OOjvD118Kbk1pDQwV39/x3eJ9+Khw+rL48RFTipacL3t5ClSr5Lxo1awqbNgm5uUVcefLkzdGjxU++TExMJk2aFB0d/T49cOmgVlEoFM2bNwcwe/ZsqbMIgiAIz54JFy8KN24Iheb7pKYKkZGFB8bT04XISPVtsUuaxIKQNCQwMBBA27ZtpQ5C6nL69GkDAwMA69atK/SQuLrPysqqYLlgZKF9/NRHHAkUVwS9bSQwIEBwcMh/h7dsmYaCEVEJlZMj+Pq+fNGwtX3bXjLXr193d3cX90A2MDAYNmzYu/ff4tJBLXTlyhUdHR0DA4MHDx5ImePmTcHJKf+fNCAYGQnffPOyLFyxQgAKV4m+vgJQeFdeKplYEJKG7N27F0CfPn2kDkJqtHXrVgC6urqHDh0qaDx58mTB/p+ff/75eUn2cfnXkcDcXMHbW7CyEl7d2CYvr+jP/omIFAphzx6hTp38F5ZKlQQvL6GoJX9///33sGHD9PT0xB3UXF1d3zYXlEsHtdPIkSMBuLm5SZYgNFQoW1Zo2lQIChJyc4XkZGHzZsHUVOjcOX87bhaEpR0LQtKQn3/+GcCoUaOkDkLqNWvWLABmZmY3b9588OCBu7u7WApWrVpVTcsF/4MTJ4S6dfPfvbm6CuHhhS8Qz7XPyRF++unlx/81aghz5qhqK3kiKlUUCuHQIaFZs/yXi/LlBS8vITHxzQsfPXrk4eEhzpmXyWSurq4XLlx49QIuHdRacXFx4pqLgIAAaRL07y9UqiQkJb3W+L//CYCwf78gsCAs/VgQkoYsXrwYwPTp06UOQuqlVCqHDBkCwNzcXF9fX/xmyZIl2dnZUkcTBOGfkUBzcwEQDAwEDw+h0LGHCoXQo4dgZCTMmSOcOycEBws//iiYmgrOzhwtJKKiKZXCn38WzLiLbtfO09OzyN2zIyMjPTw8xEOYADg5OYnzKbh0UMutWrUKQM2aNbPEzyU1KSdHMDISxo0r3J6XJ1hYCF98IQgsCEs/HRBpRFJSEgBLS0upg5B6yWSyX3/91dnZ2cLCIi8vb9iwYQ8fPpw+fbqhoaHU0QAA+vqYNAkhIRgzBnl58PFBnTrYvh2CkH/Bjh3480/873+YPx+tW6NVK8ydi337EBSEdeskjU5ExZVMhu7dERSEwEB07jwtJmbJkiU1atTw8PCIiop69cJq1aqtXr360aNHnp6eZmZmwcHBPXv2bNWqVbdu3VJTU93d3ceNGyfVD0ESmjBhQsOGDcPDw1euXKnpviMjkZ2NmjULt+vqomZNhIS8bFmxAosXv/z6809NxiS1YkFIGpKYmAigfPnyUgchtTMwMFizZk1kZKSZmdmWLVvEvWSKFxsbbNqEoCA4OuLpU4wYAU/P/Id27kTt2ujb97XrO3dG8+bYsUPzSYmoJGnbFseOzdi1a9iwYbm5uWvWrLG3tx8+fHjIq++qASsrq8WLFz99+tTb29va2vrChQthYWH29vbi2grSQnp6euvWrZPJZIsWLYqMjNRo39nZAGBhUcRDFhbIynr5x8OH4e//8uvGDQ0lJPVjQUgawhFCrdKoUaN69eqlpqaePn1a6ixv17IlrlyBry8qV8Y/RyPi1i3UrVvExQ0a4PZtKJWaDEhEJdFnn322ffv2W7duDRs2TBCEHTt21KtXz83N7erVq69eZmZmNmnSpH79+gEwMDDYvXt32bJlJYpM0mvduvWAAQMyMzM9Cz6gVLcbNxAdnV8KPn1axAVPn+LVz/FPnkRw8Muv2bM1lJPUjwUhaQhHCLWNuJ2Mn5+f1EHeSSbD8OF4/BgNGuS3pKXB2rqIK62tkZeHzExNpiOikqtevXrbt28PDQ318PAwMDAICAho1qxZp06dLly4UHBNSEjIhg0bAAwePFg8cIK02fLly8uUKbN79271fpYaFYXVq9GkCZo0wYYN+OQTWFnh9u3Cl6WkIDwcTZuqMQkVGywISUM4QqhtBg4cCODAgQNyuVzqLP/GwODl92XKID6+iGvi46Gnh3+2giAieh/Vq1dfvXp1RESEp6eniYnJiRMnWrVq1bp1a39/fwBLly4FYG1tvW3bNomDUjHwySefzJw5E8CECRNU/6szNharV6NFC9jaYvJk3LiBihXzf6l99RUOHsS1a69dv2gR8vIwcqSKY1CxxIKQNIQjhNqmdu3adevWTUxMLNazRt9Uvz5eX+2T7/591KsHHb5mEtF/Ji4ajIyM9PLysrCwEPeSadKkyc2bN5VK5cKFC6UOSMXFtGnTatWqde/evXWq2sYsJQXbt8PNDdWqYfJkXLoEY2O4u+PQITx9mj/tc9YsNGyIjh0xfz6OHcP+/Rg0CMuWYf581K+vmhhUvPHNDWkIRwi1UMmYNVrIwIG4cweFitirVxEcjMGDJcpERKVBhQoV5s2b9+jRo4ULF1aoUOHGjRs3btwA0LVrV6mjUXFhYGDg4+MDwMvLKyYm5sNvlJMDf38MH44qVTBiBAICoKMDV1f4+iI+Hnv2wM0N+vr5F5uaIjAQkyfj99/Rpw+++grx8ThwAD/8kH9BxYqoX7/wR6IWFqhf/7UpNlRiyYSCzdaJ1CYjI6NMmTKmpqbp6elSZyHNuXv3bv369cuXLx8bG6unpyd1nPcjl6NNGzx4gKVL0a0bdHRw4gSmTYO1NS5ehLGx1PmIqDTIyMj44YcffHx8ateuff/+fanjUPHSs2dPf3//kSNHbt269T89UaFQIDBQd+dO7N+PlBQA0NFB27YYMgT9+hW9lSgRRwhJM8T5ohwe1Db16tUrebNG9fVx7BgGDMDkyfjkE1SujLFj0b07Tp9mNUhEqmJqaioeydOhQweps1Cx4+3tbWRktG3btosXL77nU+7evTtjxoyqVas+nTsX27YhJQV168LLC2FhOHUKo0axGqR3YEFImiDOF+UCQi3Uv39/AH/88YfUQf4Lc3Ns3IikJDx4gJAQJCXh11/BjzOISKXET8rat28vdRAqduzs7KZNmyYIwvjx4xUKxTuuDAkJ8fLycnBwqF+//pIlS2JiYvaWLw8vL4SE4O5dzJuHGjU0FptKLk4ZJU04efKki4tLhw4dTp48KXUW0qg7d+40aNCgQoUKMTExJWbWKBGRmuXm5lpYWGRlZcXFxVWsWFHqOFTsZGVl1a1bNyIiYtOmTWPGjCn06NOnT//44w8/P7/g4GCxpXLlyv3793d3d3dycpLJZBrPSyUb35+RJnCEUGvVr1+/Tp069+/fP3PmTMeOHaWOQ0RULFy4cCEzM7Nhw4asBqlIxsbGy5Ytc3d3nzlzZr9+/cR3UC9evDh06JCfn9/Ro0fz8vIAlCtXzs3Nzd3dvVu3bvzUlT4Yp4ySJnANoTYTZ42WsL1GiYjUSZwvygWE9A79+/fv0qVLUlLS7Nmz/f39BwwYYGVlNWLEiICAAD09PVdXV19f36dPn27fvt3NzY3VIH0MFoSkCRwh1Gbi4RN79+4VP84kIqJTp06BCwjp36xcuVJfX3/Lli09e/b08/NTKBRdunTx9fWNj4/39/cfPny4iXiyPNHHYUFImsBDCLVZgwYN6tSpk5CQcPbsWamzEBFJLzMz8/Lly7q6um3atJE6CxVrdevWHTx4sKGhoZWV1eLFi588eXL06NHhw4ebmZlJHY1KFRaEpAmcMqrl+vXrB84aJSICAAQHB+fk5DRp0qRcuXJSZ6HirkmTJhkZGf369fP09LS2tpY6DpVOLAhJEzhlVMuJywg5a5SICDxwgv4LBwcHAGFhYVIHodKMBSFpAkcItVyjRo1q1679/Pnzc+fOSZ2ltDt/Hp6eGDIEX3+Ndevw4sXLh44fh6dn4euDgzFtGrKzNZmRSMtxASG9P7EgDA0NlToIlWYsCEkTOEJIffv2BWeNqpVSiTFj4OSEM2dgZoacHMydCwcHXLiQf0FwMFauLPys69exYgVycjQclkhrpaWlXbt2TU9Pz8nJSeosVALUqFFDX18/KioqNzdX6ixUarEgJE3gCCGJe43u27dPoVBInaWUWrsWP/+MrVtx8SI2bcLOnQgNRfXq6NsXKSlShyOifLrBwbdattw8aBD3BaH3oaenV61aNYVC8fjxY6mzUKnFgpDUThCE5ORkABYWFlJnIcl89tlntWrViouL46xRtRAELF+O7t0xcuTLRktLbNmC2Fhs3y5dMiJ6jclff9U9d25kjRpSB6ESg7NGSd1YEJLapaamyuVyc3NzAwMDqbOQlDhrVI0eP0Z0NNq1K9zeqBEqVACLcKLi4/RpAOACQnpv9vb2YEFI6sSCkNSOhxCSqOCEes4aVb3YWACoVq2Ih2xtEROT/31eHqpUee1rzhzNhSSipCTcugUjI7RoIXUUKjHEgpAbjZL66EkdgEo/cQEhd5Shxo0b29vbh4WFBQUFtW3bVuo4pYueHgAUueVAbi4Klirp6hbeV+bYMfz6q5rDEdE/AgOhVKJlSxgbSx2FSgyePEHqxhFCUjuOEFIB8UBCzhpVvapVASAqqnC7ICAq6uXIoUyGgQNf+2rcWKM5ibQc54vSf8c1hKRuLAhJ7ThCSAU4a1RdbGxQvz4CAiAIr7WfPo2UFHTuLFEsInrdqVMA0KGD1DmoJOHJE6RuLAhJ7aKjowHcvXvX398/PT1d6jgkpSZNmtjb28fGxgYHB0uTICUFCQmFq6bSYc4cXLyIuXNRUGyHhWHsWNSpg/79JU1GRACA+Hjcvw8TEzRrJnUUKkl48gSpGwtCUrvDhw9bWFj8/fffPXv2LFeuXNOmTWfMmBEUFKRUKqWORhLo168fND9rNDcX8+bhk09QrhwqVkSFCvj2WyQlaTSDug0YgP/7PyxZgho10LMn2rRBvXowMIC/PwwNpQ5HRMCpUxAEODuDe27Tf8RZo6RWLAhJvYKDg8+cOSOXyz09PVu2bAng2rVrS5YscXZ2tra2HjJkyLZt2549eyZ1TNIccdboH3/8oblZo4KAfv2wdCnGjcONG7hzBwsWwM8PbdqglA1Zz5iB0FB4eqJuXbRvj717cesWatbMf7RXL2zcWPgpHTpg82aYmGg4KZE24gJC+lA8eYLUSiaUyqlTVGx07Njx1KlT8+bN8/LyApCenn7x4kV/f/9Dhw5FREQUXGZnZ+fi4uLi4tK1a1ezgh0RqZSyt7cPDw8/e/ass7OzJvr7/XcMGYI9e+Du/rLx2jW0aIEZM7BggSYyqFVEBJKS0KSJ1DmI6J0cHBAWhsuXOWWU/qvVq1dPnjz522+/XbdundRZqBRiQUhqFBwc3Lp167Jlyz5+/NjCwqLQo48ePTpx4sSJEyeOHz+ekpIiNhobGzs5OYnFYZMmTWQymcZTk9p5enouXbp04sSJPj4+Kr51djZkssIzJHv2xO3bePwYhf45deuGhw8RHq7iDJp3/DjGjEHlypg4Ef36cTYaUXEUHY1q1VC2LBIToasrdRoqYQ4fPtyjR4/OnTsfO3ZM6ixUCnHKKKnRnDlzAEyZMuXNahCAnZ3dmDFj9uzZk5CQcPXq1cWLFzs5OeXk5Jw4cWLGjBlNmza1trYeMGDA5s2bOae0lBFnjfr5+almHWlODs6cyf++Tx+cPFn4gjt3ULt24WoQQN26ePQIGRkqyCCtzp0RHo7p07F1K6pXh5cXcnKkzkRErxP3F23bltUgfQCuISS1YkFI6hIUFHT69OmyZct6eHi8+0o9PT1HR0dPT8+goKC4uLg9e/aMGTOmatWq8fHxfn5+Y8eOrVKlSr169WbMmHHixIkcvtMt+Zo2bVqzZs3Y2Njz589/+F3On8+v/bKz4eaW31i9Ol6ZipwvPR0VKxZxBysrAEhL+/AMxYeuLnr3xl9/4eRJ6OhwkJCo2OECQvoIPHmC1IoFIamLODw4derUcuXKiS33798XzyR8hwoVKri7u2/atCkqKio8PHzTpk3u7u5lypS5d+/ekiVLOnXqZGlp2alTpyVLlly7dk3tPwOpTd++ffFhe42OHYsnTwDg1i2ITy9bFrq6+VuG2toiMrLwU8zMEBdXxK1iYyGTwdz8P2coVtatw7ff4u7d/D/WqQMvryKGQ4lIWoGBAAtC+kA8eYLUigUhqUVQUFBgYGC5cuUmTpxY0DhixIgaNWq8/wF0BXNK4+Pj//rrL09PT0dHx6ysrII5pXZ2dmPHjvXz80tOTlbPz0Hq8j6zRpOSksS/2Vu3bnUoOMf5wQOIc2ZeHQwsqAOLHCFs2BAhIUWcPXj3LhwcSvwGm/37w9YW3bqhdWv4+SEvT+pARPSGjAw0bAg7OzRoIHUUKqk4a5TUhwUhqUXB6sGC4cGAgIArV66Ympo2btz4v97N2NjYxcVl8eLFV69ejYmJEeeU2tjYPH78ePPmzQMGDKhYsaJ4vOGJEyfkcrmKfxhSg2bNmlWqVCkmJmbz5s2FHvrtt9/Eza7mz5+/bds2AJUqVbp3717+wwUl36u1X5GNBYYMQXQ09ux5rfH6dZw8iaFDVfQDScfKCp6eCA/HxInw8cH9+1IHIqI3mJri0CGEh0OH77voA/HkCVIfvjCR6p0+ffrN4cEFCxYAmD59usnHDchYWVmJc0qfPHly5cqVRYsWtWnTRkdHRzzesFOnTlZWVoMHD+ap98Wfra0tgJ9//hnArVu3fvnlF7F9ypQpsbGxAKpXrx4dHQ3A2tpaEIT85aP16iEzEwCqV0eFCvn3atsW+voAULMmHB0L99S/P9zcMHIklizB3bt4+BA//4yuXVGvHqZNU/vPqVYFm+Lo62PgQJw7x/EHouJCEPDbb+jYEZUrw9oarVphzRrwI0v6UGJBGBYWJnUQKoV47ASpXrt27c6cObNgwYLZs2eLLQEBAW5ubtbW1uHh4R9ZEBYpMzPz/Pnz4iEW165da9Wq1ftPTCWpTJ06deXKldWrV3/8+HFKSkpOTk6lSpUAJCYmli9fHoBcLtcXy7yPl5uLBQvw88/5iwnLlsXgwfjpJxS1/21J4uWFdeswbBi+/RYODlKnIaJXeHhg7Vp88QW6doWBAc6exaZN6NABAQHcaJQ+AE+eIPVhQUgqdurUqY4dO5YrV+7x48cF80WbN29+5cqVVatWTZ48Wd0BHj16lJSU1LRpU3V3RB9pwoQJ69ats7W1jXhzkqeaCAKeP4dCgYoVoadX+NGgILRuraEkKhQVhY0b8csvWLwYI0dKnYaIAADHj6NLF6xcie++e9l44AD69IGPD16ZPkP0nkJDQz/99NMaNWo8evRI6ixU2nDKKKnY/PnzAUybNq2gGvT3979y5Yq1tfWYMWM0EMDOzo7VYIkgzn5p166d5rqUyVCpEmxsCleDeXn49lt0746tWzUXRlWqVcNPPyEyEv36SR2FiP7xyy+wtkahU5d690arVtiyRaJMVLKJJ09ERkby/C1SORaEpEqnTp06c+aMpaXlm6sHZ8yYoY7JolRypaSk4J+VhFKKi0PHjvj5Z6SlYdKkknSk+8KF2LYN2dkAYGRU4s/PICpNrl1D/fpFTA1t0gR37pSk1xkqNsSTJ5RKpeam1ZDWYEFIqvTjSgx5JAAAIABJREFUjz8CmDp1qvk/700PHTqkyeFBKkGSkpIAWFpaShkiIABt2uDs2fzTGpRKeHpKmec/ad4cfn6wtYWPj9RRiOh1L16gcuUi2itXhlKJ1FSNB6LSgCdPkJqwICSVOXny5NmzZ8uXL//q8ODChQsBzJgxw9jYWLpoVByJBaG4f4w0oqMxbhwePnzZIgjYtg3h4ZJF+k86d8affyI4GJ99JnUUInqdsTGSkopoFxs5X4Y+CE+eIDVhQUgqI64enDp1qpmZmdhy8ODBK1eu2NjYcHiQ3pSYmAhpRwirVsWiRbC2ftmSlQWFAt9+K1mk9/b8+fP87+zt0aaNpFmI6A116qDI4wFCQ1GtGkxNNR6ISgOePEFqwoKQVOPEiRPi8OCECRPEFkEQFi1aBA4P0luIBaGUI4QAhg/H9OmvHT6ho4MLF7Bzp3SZ3ourq2vr1q137drFnaKJiqM+fRASgqCg1xojI3HsGPd/og8mThllQUgqx4KQVEPcOabI4cHRo0dLGo2KqWKxhhDAd99h4kT88+8WWVmQy7F4cf6B78XV+fPnp06dev78eZlMJnUWInrDV1/hs8/g7o5Dh5CXB0HAuXPo2hUWFpg5U+pwVFJxDSGpCc8hJBX466+/OnfuXL58+cePH4sFoSAIzZs3v3r1qo+Pz0QeuERFsbS0TE5OTkxMlL4mBPDtt9i2DVlZAGBujrQ0jB6NTZukjkVEJVZCAsaOxYEDkMmgowO5HO3a4eefYW8vdTIqqfLy8kxMTBQKRWZmpqGhodRxqPTgCCGpgDg8OG3atFeHB69evWpjYzNq1ChJo1ExpVQqU1JSdHR0Cs6rlNj69XB3h4EBAAgCBAH+/rhzR+pYRVu0aNG9e/ekTkFE71ShAvbuRWwsTp3C8eOIjsbp06wG6WPw5AlSExaE9LGOHz9+7ty58uXLjx8/XmwpWD34ww8/cPUgFSk5OVmpVFpYWOjoFJtXoW3b0LMndHWRng5TU8TEoFgObsvlcrlc3qVLl5iYGKmzqNiZM2dK3w9F2q5iRbRpg6Qk/PorXryQOg2VeJw1SupQbN6KUYklHizx/fffFwwPHjhwQBwe/PrrryWNRsWX9FuMvkkmw2+/oUMHAPnHSV++jI0bpQ31Jn19/Xnz5kVGRtrY2EidRZXkcvmQIUNsbW3v3r0rdRYiVfvpJ8ydi5AQqXNQiceTJ0gdWBDSRykYHvz2n536BUEQj6efNWsWhwfpbaQ/hLBI+vo4eBCtWyMvDzIZMjOF1asz4+KkjvUauVwOoBiNrKrI3r17nz175uDgULduXamzEKmagwOAog+iIPovTE1NARw6dEjqIFSq6EkdgEo2cfXg9OnTC4YH9+/ff+vWLRsbm6+++krSaFSsFccRQpGxMfz90aIFnjwRsrL+jo7e4uHhs3u31LHyJSYm1qtX7/fff2/fvr3UWVRs3bp1ACZOnMh9U6kUEpcOsiCkjyZ+1P7333+/+VBycrL4TXp6uvjRYXZ2dlZWFgCFQpGamgogKysrKSlJEITc3NyUlBS5XK5UKlNTU7OzswFkZmamp6cbGBjIZLK0tDQABgYGz549MzU19fX1rVSpkqZ+StI0FoT04Y4dOxYUFFShQoVx48aJLYIgiMfTz549m8OD9A7FdIRQVLYszpzJtbM7YGw8ND29/JkzI65dc3R0lDoWAJQvX/7SpUvFZSce1bl161ZQUJCZmdnQoUOlzkKkBuIIIaf50UcrU6YMgIyMjIYNGz558kQul+vo6JiYmOjp6QmCIJPJFAqFXC6XyWSCIIjVoEwmS09P/8h+ly5dunz5chX8AFQssSCkDyeuHnx1eHDfvn23bt2qXLkyhwfp3YrvCKGoUqVEf/91o0blpafHxcVNmjTp3LlzxWTkytbWVuoIqrd27VoAX3/9dcGLCVGpwhFCUpGjR48CsLe3v337NoDk5ORHjx79/fff169fj4qKevHiRUpKSkpKSlpaWnJyskKhUFW/a9asmTJlSuXKlVV1QypWWBDSBzp69Oibw4PiDNJZs2YZGRlJmo6Ku+JyKv3b2bRvvzEgwM3NLTw8/OrVqytXrpw6daq0kTIzMwMDA7t37y5tDJV78eLF77//LpPJvvnmG6mzEKkHRwhJRfLy8gCMHDlS/KOFhYWjo6Ojo+OXX3756mXJyckPHz68cuXKnTt3kpOT09LSEhMTk5OTU1NT4+PjP+AQ8tzc3GXLlq1atUoVPwQVOywI6QOJB0tMnz5dnL0AYO/evbdu3apatSo3F6V/VaynjP6jTp06v/3224ABAyIjIzdt2jRixIgKFSpImCchIUFfX1/CAGqyZcuWjIyMrl271qpVS+osROpRsSLKlkVyMhITUbxf96g4y8nJuXTpkkwmGz58+LuvtLCw+Pzzzz///PNCT3/69OmDBw9u3LgREhISHx+fkpKSnJycnp6elJQkzi99h/Xr10+bNq1KlSof+2NQ8cOCkD7EkSNH3hweFGeQzpw509DQUNJ0VAIU9ymj/2jevPnPP//81VdfhYaGTpgwYdeuXRKGqVatWrVq1SQMoA5KpXLDhg0ACg4yJSqd7O1x7RpCQ1kQ0ge7cOFCVlZWo0aNPuzTSUNDQzs7Ozs7u27dur3a/uq809jY2PT09MTExISEhNTU1Ffnnebm5i5ZssTH5//Zu++4Jq81DuBPFnsvZQiI4ADFgRutgmgd4KYusHUUqwJukTriLq4WV1vqaKlaK44qUhdaBzhQVERUprIRQcKQmXHuH8cbI6AykpwEz/fTz/3gm5D3hxeT93nPOc/ZJYWfhFIwtCCkmgJvLBEQECAeHjxx4gQeHqSrB6mGUIoRQmzo0KE//PDD0qVLL1++fPXqVXm296yurs7IyIiLi4uIiCgvL//uu++GDh0qt7PLx7lz554/f25lZVXrAoWiWho7O7h/H1JToW9f0lEoZXX16lUAcMX75UpPvfNOhUJhdnZ2amrqgwcPnj59mpGRcfXq1ZCQkGXLlrVp00a6ASjiaEFINdq5c+diYmKMjIzEC37Ew4Pff/89HR6kGkJZRggxLy+v0tJSLpcbGBh48+ZNFt62Xnpqamqys7OfP3+em5ubl5f3/P8yMjIkWwKcPn365s2bfVvW1STebcLX11fqf6sUpVhoXxmq2f777z8AkM99SRaLZWVlZWVlNWTIEHxk6tSpR48e/eGHH37++Wc5BKDkidGEdaXUZ87BweHp06fbt28X99g4fvz4V1991aZNm5SUFFoQUg1hY2Pz4sWL58+ft23blnSWhtq0adP27dsXLVq0Zs2apr0CHvFLT09PT08Xf5Genp6Xl1fvWzGHw7GwsLCwsHj58mV2dnZlZWW7du0SExPZ7BZyLy81NbVDhw6qqqpZWVlKMVxMUU3355/w9dcwdSocOUI6CqWUKioqDAwMBAJBYWEhkc2HUlJS7O3tGQxGUlKSEn12Uw3RQq4qKLlZt27d06dPVVVV58yZg4+IhwdXrlxJq0GqgZRrhBBbuXLl69ev9+zZ8+2335qamn7kmQ0c8RPjcDhGRkZmZmY277O0tBTXfikpKQMGDEhLS1u5cuWWLVtk8hPK3Z49e0Qi0bRp02g1SLV8dISQap7o6Ojq6urevXuT2orWzs5uypQphw4dCgoKCgkJIZKBkhE6Qkg1TufOnZ88eeLi4oLnLQDA9evXBw8ebGlpmZKSoqKiQjYepRT4fL6qqiqLxaqpqVGQzf0absqUKfn5+fj3/0OFX3p6ukgkqvu9DSn8PiImJuaLL77g8/mnTp0aO3as9H82+aqoqLCwsODxeLGxsU5OTqTjUJSMFRSAiQno60NREekolFIKDAwMCgoKCAgICgoilSE1NbVTp04MBiMxMdHGxoZUDErq6Agh1Tj48r2qqkp8ZNCgQXfu3OHxeLQapBqoqKgIIWRgYKB01SAA/PXXX3379jUyMuLxePVWfQDA4XCs62NmZtaclXJ9+vTZtGnTsmXLZs6c2bVrV2WfsXP48BEej+fs7EyrQeqzQHeeoJpHngsIP8TW1tbLy+uPP/7YtGnTgQMHCCahpIuOEFKNs3PnzoULF5qbm2dnZ5POQimrZ8+e2dvbd+zY8dmzZ6SzNIWpqenLly8BgMVimZiYNHnErwkQQhMmTPjnn3969eoVHR2t1HdhevWqVFc/smCB5YQJw0hnoSi56NkT7t+H27dpo1GqscrKyvBdVB6Pp6mpSTBJenp6hw4dhELh06dP27dvTzAJJUVM0gEoJePr69uqVaucnJz4+HjSWShlhRcQKumysTt37rx8+VJXV/fgwYNVVVW5ubmxsbFhYWFBQUE+Pj5ubm42Njaya/rCYDAOHjzYtm3be/fuLV++XEZnkYOrVyE2Vj01dbaHB60Gqc/FTwMHOtrZHUtLIx2EUj7Xrl0TCAR9+vQhWw0CgLW1tbe3t1Ao3Lx5M9kklBTRgpBqHBaLNW7cOAA4fvw46SyUslKiTQjrwtskzJ079+uvvybS7VNPT+/YsWOqqqo7d+48efKk/ANIxd69AAA+PqDMY5wU1Tg8be3HKSnPUlJIB6GUj4x2IGya1atXq6ioHD58ODExkXQWSjpoQUg1mqenJwCEhYWRDkIpK2VsMYoVFBScOHGCxWL5+PgwmcTeP3v16rVt2zYAmDFjRlJSEqkYTZabC+HhwOHAt9+SjkIpoeLiYtIRmsjW1hYAUmhBSDUeLgjJLiAUs7KymjFjhlAoxE3mqRaAFoRUow0aNMjExCQ5Ofnx48eks1BKSXlHCENCQqqqqjw8PIg3dPHz85s6dWpZWdlXX31VWVlJNkxj/fwz8PkwYQKYm5OOQimbGzduWFlZHT16lHSQpqAFIdU0r1+/jo+PV1NT69OnD+ksb+Gdxv7+++8XytkLgKqFFoRUo9FZo1Qz4YJQ6UYIBQIB3nlp/vz5pLMAAISEhHTs2DE+Pn7JkiWkszRCTQ3s3w8A4OtLOgqlhCIiIkpLS728vLZu3Uo6S6PZ2dkBLQipxrt27ZpIJOrfv7+6ujrpLG+1adNm/7JlBY6ObdetI52FkgJaEFJNMXHiRKCzRqmmUtIpo6dPn87OzrazsxsyZAjpLAAAWlpaYWFh6urqv/zyy6FDh0jHaahjxyA/H7p1A2dn0lEoJbR169bg4GAACAgImDVrFp/PJ52oEYyNjXV1dYuLi/F7IEU1kELNFxXzmjdPPzERjh8H2mVQ+dGCkGoKFxcXExOTpKSkhIQE0lko5aOkU0b37NkDAP7+/oqzfWKXLl3wxfG8efOUZQ8P3E6GDg9STbZgwYITJ05oaGgcPHhwxIgRyrWkkM4apZoA70CoIB1l3jE1hW+/BZEINmwgHYVqLloQUk3BYrHGjBkDdNYo1STKOEL45MmTGzduaGtrT58+nXSW9/j4+EyfPv3NmzdfffVVRUUF6Tif8OABxMSAvj5MmUI6CqXMxo0bd/PmTQsLiytXrjg7O6enp5NO1FB41mhqairpIJTSyM/PT0xM1NLS6tWrF+ksdQQGgoYGnDwJcXGko1DNIteCcOXKlQcPHhSJRPI8KSUjuNfosWPHSAehlI8yjhDu3r0bIfT111/r6OiQzlLbzz//bG9vn5CQ4OfnRzrLJ+zaBQAwezZoaJCOQim5bt263blzp1u3bk+fPu3Vq1d0dDTpRA1CC0Kqsf777z+E0MCBAzkcDuksdbRuDd99BwgBXUmo5ORXED59+nTLli2zZs3q169fTEyM3M5LyYiLi4uxsXFSUtKTJ09IZ6GUTF5eHgDo6uqSDtJQxcXFhw8fBoA5c+aQzlIPTU3NsLAwTU3NgwcP/vHHH6TjfFBhIRw7BkwmfPcd6ShUi2Bubh4VFTVq1KjCwkI3NzelaD1Kp4xSjaWYCwjfCQwELS04cwbu3SMdhWo6+RWE9vb2//zzj5WV1d27d/v16zd9+vSXL1/K7eyU1LHZbDprlGqa/Px8ANDU1CQdpKF+//338vJyNze3zp07k85SPwcHh3379gHA3LlzHz16RDpO/fbtg6oqGDUKbGxIR6FaCi0trTNnzsyfP7+6unratGlr164lnegTaEFINRZeQKi4BaGREcydCwjRlYRKTa5TRj08PJ4+fcrlclVVVQ8dOmRra7t27drq6mp5ZqCkiM4apZoANyJiMBjGxsakszQIQujXX38Fhdlt4kOmTJkyc+bMqqqqr776qrS0lHSc2oRC+O03AADF/luklA+LxdqzZ09wcDCDwVi3bp2Ctx6lO09QjZKVlZWWlqarq9u9e3fSWT5s+XLQ1oazZ4FOAFRa8m4qo6GhsXbt2oSEBE9Pz/Ly8nXr1nXp0uXcuXNyjkFJhaurq7GxcWJi4tOnT0lnoZTGrFmzAEBbW9vKymrVqlWKv6n6hQsXkpOTLS0tPTw8SGf5hL1793bv3j05OdnHx4d0ltrOnIH0dLC1haFDSUehWiLJ1qMjR45U2NajdOcJqlGuXLkCAIMHD2axWKSzfJiR0dvO0XQlodKSR0G4efPmHTt2SN6xa9euXVhY2OXLlx0cHFJSUkaNGuXh4fH8+XM5hKGkiM1mjx49GuisUarBCgsL8Qjhnj17zp8/f+zYsfbt2/v6+hYWFpKO9kFdTp9e1bOn77x5Cv15DAAAampqx44d09HROXbs2G94PE5hsFjQvj34+gKTNremZGPcuHFXr15t3br15cuXBwwYoLCtR+msUarhFH0BodiSJaCjA+fPg5K0d6JqQzKWk5OjqqoKAB07drx48WKtR2tqaoKDg3HXPhUVFX9//9LSUllHoqTowoULAODg4EA6CKUcli1bBgCjR4/Gf+Tz+YsXLzYwMLCwsBg1alR8fDzZePVITUVMJlJVRfn5pKM0FJ7Fraamdv/+fdJZ3iMSoZoa0iGoli47O7tbt24AYGRkFBUVRTpOPSZPngwAv//+O+kglBKwsrICAEX8cKxr9WoEgIYNI52DagqZF4QIITwSiOtPd3f3tLS0Wk/Izc318fFhMpkAYGZmFhoaKhKJ5BCMar6amhq8ecCTJ09IZ6EUXUFBgZaWFgDcvXtX8nhUVFSPHj2YTKaJiYmzs/Pff/9NKmE9lixBAGjGDNI5Gmfu3LkAYGtrW1xcTCTA4MEIAEVEvHewdWu0dCmRONTnpbS0dNSoUfi2yNGjR0nHqW3gwIEMBsPR0ZF0EErRJScnA4ChoaFQKCSdpQGKi5G+PgJA16+TjkI1mjzm7gwZMuThw4d4JDAiIqJTp04LFiwoKysTP8HU1DQkJCQmJqZfv365ublff/314MGDFbZRHiWJw+HgXqMnTpwgnYVSdFu2bHnz5s2YMWNq7a47YMCA6Ojob775RiAQ3Lx5c+bMmX369Nm5cyf5zhCVlfD77wAA8+YRTtJIP/30k5OTU2pq6vTp0xFCpGIsWAAKv0SUaoG0tbXPnDkzb968qqqqqVOnKkLr0aKiopCQEEdHR0NDw+zsbIRQfHz89evXSeeiFBqeL+rq6spUiqn2urrg7w9AVxIqJ3lWn58cCRSJRKGhoa1atQIAJpPp7e396tUreSakmuD8+fMA0LlzZ9JBKIX2oeFBSadPn+7UqRN+a2Kz2fb29vPmzXv9+rU8c77nt98QAOrXj1iAZkhPTzcwMACAXbt2yf/sgwejIUOQgQFaterdQTpCSMlZcHAwvuSYOXNmjdznK1dXV1++fHn27NkmJiYcDkddXf2rr77Cb2i416iHh4ecI1HKBc8u/vnnn0kHaTA8SKiiglJSSEehGkeGBWFwcPD333//5s2bWsfv3bvXr18/fM33xRdfxMXF1XoCj8cLCAhQUVEBAH19/eDgYIFAILucVDPV1NTg686nT5+SzkIpriVLlgDAmDFjPv604uLiiRMnamtri29aWVtbT5w4MTExUT4539OjBwJAhw8TOLU0hIeHMxgMDodz69Yt2Z1FIEDx8WjzZjR2LFq48O3BwYPRxIlo2zakooLEbwy0IKTk79SpUxoaGgDg5ubG4/HkcMa0tLRNmzZ9+eWX+vr6HA6HzWabmJiEhIRIXsm8fPlSV1cXAM6dOyeHSJQyEolErVu3BgAyH39N9u+/KCODdAiq0WRVEJaUlOBWMRYWFn/99VcTRgITExNHjBiBrwg7depUtyENpThmzJgBAOvXrycdhFJQr1690tLSYjAY9+7da8jz9+3bhxvxiZmYmLi6ul65ckXWUd+5fh0BIGNjVFUlv5NK28KFCwHA0tKysLBQii9bXIzCw9G336Lhw5GDA9LRQQAIALm6vn0CLgirq1GHDmjQIIQ/AWhBSBERExODL6wdHBxevHghi1Pk5+fv27dv4sSJjo6OmpqaGhoaWlpaWlpaLi4u165dq/dbduzYAQC2trZVyvwOQ8kO7shtampKOkiTpKejHTuQry9auBDt34/kci+Gag4ZjhBKjgT27t07Jiam1hMaMhIYHh7etm1b/CLu7u7p6emyC0w1Gd5JskuXLqSDUApq8eLFADB27NiGf0tubq6rq6uamppkWairq9u3b1+8vFB2ad/66isEgFavlvmJZKmmpqZ///4AMGrUqOY06+LzUXw84nLRqFHIyQmZmyMm820RKPlfjx5vn48LQoRQZCQCQH/8gRAtCClyJFuPRkdHS+U1KyoqIiIivv766759+5qbmzMYDC0tLW1tbTU1NWtr6ylTpmRmZn7k2/l8fpcuXQAgKChIKnmoFmbXrl0A4OXlRTpI4+3ahVRUkKUlmjgRjR6N9PSQoSG6fJl0LOpjZLuGUCojgRUVFUFBQXj1kYaGBpfLrayslGlsqrHorFHqI16+fKmhodHw4UExkUgUFBSEm25LUlFRcXR0XLp0qQxbaObmIg4HsdkoK0tWp5CXzMxMIyMjANi2bVujvvHu3YyxYxP69UPt2yNra6SuXk8FWOu/du0QboYnLggRQl99hUxMUHExLQgpkkpLS0eOHAnNaz0qFArv3r27YMGCQYMG2djYcDgcANDW1tbR0dHQ0FBTU3N0dFy1alV5eXlDXg3vOa6lpYXbzFCUpHHjxgHAgQMHSAdppEuXEIOBFi5E4vu2PB4aPBjp6raAz9MWTB5NZcrKyrhcruRIYN27+58cCczOzvb29mYwGADQpk2b0NBQOSSnGu6bb74BgA0bNpAOQimcRYsWAcC4ceOa9u3JyckDBgzAF16SGAxGu3btJk+enCGL5Qpr1iAA5Okp/Vcm4d9//2UwGGw2++PbspWWlkZGRi5atGjkyJFdunQxMzNTV/8bAKmoIC0tpKuLdHWRjg7S0UEqKvUXhCYmbz/xJQvCnBykrY0CAmhBSBHG5/PnzZuH3z24XG7DvzE3N3f37t3u7u4dO3bEt6cBQFNTU0dHB//RxMRkwIABdRfIfNLEiRMBYOrUqY37SaiWTigU4ht5z58/J52lkVxcUPv2qNaMv6wsxGajwEBCmahPk1+X0aSkJPFIYL2b1DdkJPDatWuOjo74RVxdXRMSEuQVn/qEf//9FwDoxkpULXl5eXh48OHDh01+ET6fv3z5cjMzM6iPhYXF0KFDpbkDdU0NMjNrYZspLV++HL+11upPkJaWtnPnzsmTJzs7O1tZWeE7d2Lq6os+NBioooI0NZGOztsqUVsbqaggFgvhBVOSBSFC6McfkZoa0tCgBSFFnrj16KxZsz7SerSsrCw8PPzbb791dnbGl+aYmpqajo6OtrY2fhErK6tRo0Y9evSoaWEyMzM1NTUB4OrVq038eaiWCI8eW1lZkQ7SSNXVSEUFffddPQ/16IH69pV7IKqhGEi+W1SdPXt24cKFz58/BwB3d/c9e/bUmg+Wk5MTGBh4+PBhhFCbNm02btw4ffp0yScIBIKDBw+uXLmysLCQw+HMnTt3/fr1uFsXRRCfz2/dunVRUdGzZ886duxIOg6lKBYtWhQcHDx+/PiTJ08286Vu3rw5f/78+Pj4et+19PX1+/Xrh29MNNdff8G0aeDgAI8fA4MhhRdUAAKBwNTUtLCw0NTUNDg4+OzZs0lJSXl5eTwer7y8HD8HX5hqaGgghNTU1BBC1dU9AMJMTNQYDFBVBRYLOBxgs9/+L4sFqqrAZIKKCtTUAJ8P1dXwww/QvTu4uICRERw/Lj479OwJjx7B0qWwbRupvwOKeuvUqVPe3t4VFRVubm4nTpwQX0IIhcL79+8fPXr04cOHmZmZWVlZAoEAP4T3jUAIVVZWCgQCDodjY2Pj4uKyceNGQ0PD5oTZsGHDmjVrHBwc4uLi2Gx2c382qkUYPXr02bNnHRwccGsZpZGbC+bmsHkzBAbWfmj8eIiNhcxMWLYMMjNBTw90dEBb++1/urqgp/fuj9raoK9P4gf4fMm7IASAmpqaX375ZdWqVW/evNHQ0Fi2bNmKFStqtY64fv26v79/fHw8ALi6uu7atcvBwUHyCUVFRevWrdu7d69QKDQ0NFy9erWfn59ybNzZcn3zzTehoaEbN25cuXIl6SyUQnj58mW7du0qKysfPnzYtWvX5r9gVVWVr6/vP//8U1RUVOshFou1atUq6exA7ewMt25BSAj4+Ejh1RTGb7/9NmfOHCaTOXPmTG1tbX19fVVVVSMjI1VVVRMTE1VVVU1NTVVVVQ0NDfy/ampq6urqTTuXhwcYGsIff7w7Eh0No0eDry+sXy+Vn4aimuXu3bujR4/Oz893cHD45ZdfoqKibty4kZ6enpmZWVlZKX4ak8nU0tJCCFVXV9fU1ACArq5up06dpkyZMnfu3LpT2Zugurq6S5cuKSkpu3fv9vX1bf4LUsquqqqqVatWpaWl33333S+//EI6TmO8fAmmprBxI9S9Dhw7FuLiID0dunWDR48a9Go6OqCt/X3fvpczM3V1dXV1dfF6XW1tbfwRhr/AR8RPqDXPhWooUkOTn1wTyOfzQ0JC8DwNDoddaALzAAAgAElEQVTj7+9ft4HEgwcPBgwYgH8QJyenmzdvyis+VY+IiAgA6Nq1K+kglKLAex5MmDBBui975swZ3J1PUrdu3aSz8fTDhwgA6emhOnuoKruhQ4cCgK2tLdkYfD5SukUxVIuUlZWFW4/WvZvMZDJ1dHR0dHQk74mYm5u7uLjIYvObM2fOAIC+vn7dxnvUZ8jFxQVf+kp3uyB5qKlBampozpx6HurWDTk7I4TQ1avo6FEUEoK2bEGrVqEFC9DMmcjTE7m5od69UadOyMIC6eqK1yeM69OnUXUNi8XauXOnnH/uFoDACKGkWiOBO3fu7Ny5s+QTPjkSiBA6cuRIQEBAbm4uk8n09fXduXOnvH8MCgAA+Hx+q1ateDwenTVKgQyGByWVlJTMnj370qVLpaWlAKCtrR0SEjJlyhQpvPSPP8LSpbBgAfz0kxReTWFUVlZaWFgUFRWdPn16zJgxpGK8fg3jx0N6Ojx4AM2bZ0dRUlBcXDxx4kS8XgvT0tJiMplCoVA8lZrJZFpZWfXs2XPbtm11mx5Li7u7+7///jt79ux9+/bJ6BSUUjh//ry7uztC6KefflqwYAHpOI03bBg8fw6JiSA5/zkrC2xs4PvvYd26RrxUSQmUlqa/eZNfWlpWVsbj8crKysrKykpLS8vKykpKSkpKSiSP8Hi8kpISkUikpqZWVlZGJ2A3DuGCFCGhUBgaGtqokcBbt27VekJ5eTmXy1VTU1PW/XxEInTlCuJy0cKFaP16pLRDnXjB56ZNm0gHocjDn2RSHx6UtH//fjs7OwAYOHCgNF83KQnl5EjzBRXAb7/9BgD9+vUjG4PPRwMGIAA0cuTbDSooiiw+n29vb48vMPAyWjENDY0ePXps2LChgdtINEdqaqqqqiqTybxz546sz0UprKysLHw9vHnzZtJZmurKlbfbTogbjZaUIFdXpK+PcnNlffI3b97gOlD5tusgjXxBiL1+/drf35/FYgGAoaFhcHCwsM7FQnh4uKWlJQAwGAxvb++XL1/WekJKSkpVVZW8IktPURFycUFMJurTB3l6ou7dEQAaM0YZZ6ydPXsWALp37046CEVYbm6uuro6g8GIi4uT6YlycnIGDBgQGxvb9JcoL0chIeibb5CnJ5o3D4WHo2Zs4K6wevToAQC4XxdZWVnIyAgBICW9fUe1PM+ePat1r9zY2Lhv375Hjhxp7DYSzREYGIjvete9/qE+B3w+f+DAgQAwZMgQ5f4d2LULcTiobVvk5YUmTkQGBsjQEMmrj+6uXbsAoH379oJaW19QH6UoBSH24MED/I8BPrAmUDwSiO/kcblcpawAaxk7FunpvTcqePYsUlVF335LLlMTVVdX6+vrA0BKSgrpLBRJ/v7+ADBRcucBxZSYiKyskI4OmjwZLVyIhg9HTCYaOlQZb8d8xI0bN/A1roK8YZ47h5hMxGajGzdIR6E+PzweLyEhQXI/5GPHjolLQXNz8+HDh8v6Tla9ysvL8ZRUOrjxeVqxYgUAtG7dOi8vj3SWZktLQ1u3ou++Q/Pno19/RUVFcjtzTU2NjY0NABw5ckRuJ20BFKsgxGqNBNb9h5GSkuLp6Ynfu9u3b79//34iOaUjIQEBoJ9+qn18+XLEZstheF3qvL29AeCHH34gHYQiRjw82OS9ueREIEAODsjO7r0JopcvIxUVNH8+uVjSN2nSJABYvXo16SDvfP89AkCtW6MWcOVDKZcjR47Uul21ZcsWANDX1/fz8yPbxuPo0aMAYGJiwuPxCMag5O/cuXNMJpPFYsmia9Hn5uDBgwBgZ2cned+H+jhFLAhRw0YCIyIi8PIhfEvvupJuIb1rFwJAT5/WPn79OgJAR4+SyNQsp06dAoBWrVqRDkIR4+fnBwCenp6kg3xKRAQCQOHhtY/7+SFVVdRSLshyc3M5HA6bzc7KyiKd5R2hEA0ZggCQiwui83ooeVq2bBkArFu3Tnxk6tSpAPDrr78STCWGO0z6+/uTDkLJT0tYOqhIBAJBhw4dAKDuFgbUhyjoxn0aGhpr1659/Pixp6dneXn5unXrHB0dz507J/mcUaNGPXnyZObMmQwGIycnZ9CgQUOHDn3z5g2pzE2UnQ0AYGlZ+zhuZZaTI+88zTZkyBAGg5Gfn493oaA+N3l5efv372cwGKtWrSKd5VOiogAABg2qfdzVFaqr4e5d+SeShV9//ZXP548bN87CwoJ0lneYTDhyBExN4epV2LSJdBrqcxIXFwcAeMMJySNOTk7EMknYvXs3h8PZu3fvowbu1UYpOYFAMHXq1MLCwiFDhgQEBJCO0xKwWKzvv/8eANavXy8QCEjHUQ4KWhBitra2YWFhkZGR9vb2ycnJo0aN8vDweP78ufgJHA7nwIED9+/ft7W1BYDLly87ODiEhYWRi9x4DAYAQN3NP/AR/KhSYTKZrVu3BoBp06bhLQGoz8rmzZsrKys9PT0dHR1JZ/mU/HzQ1QUdndrH8Q2aly/ln0jq+Hz+/v37AWD+/Pmks9TWqhUcOQIsFqxbB5GRpNNQnw1caIkLwsrKyuTkZDabXWvXK1IcHBzmzZsnFAp9fX0R0Y3BKPlYvXp1VFRU69atDx8+XHdLTKpppk2b1rFjx7S0tD///JN0FuWgBL95bm5ucXFxwcHBOjo6ERERnTp1WrBgQVlZmfgJ3bt3T0lJOX36dN++fTMzMydNmjR48GC8t6ESMDcHAMjKqn08M/Pdo0ri9evXc+bM6dix45w5c1gsVmlpqbe3N/08+6zk5eUdOHCAyWSuXLmSdJYGYLGgurqe2zHV1QAAHI78E0nd8ePHc3NzHRwcvvjiC9JZ6uHiAqtXg0gEXl6Qm0s6DfUZyMnJefXqlb6+fps2bfCRx48fCwSCjh074lUqimD9+vWmpqbR0dGS3W6oFun8+fNbt25lsVhHjhzBN9MpqWCxWKtXrwaA9evX19TUkI6jBJSgIAQADoezYMGCxMREHx8fgUCwa9euTp06/fnnn5LFxpgxY27duhUaGtqqVavr16937959+vTpBQUFBGN/QloaAICLCwDUc3v8wgVgswFfwyn8OFt2dvasWbPs7e0TEhLi4+O5XO7Jkye1tbXDw8PXrFlDOh0lP8o0PAgAVlZQVQV13yXS098+qvz27t0LAP7+/gxFnW6wejUMGwavXsHUqUCn9lCyhmeHdu/eXfwvou4MUuJ0dHQ2btwIAEuXLlW+hTBUg2VnZ0+fPl0kEm3YsMHV1ZV0nJZm8uTJXbp0ycjICA0NJZ1FGRBew9h4N2/eFE/0d3V1rampqfUEHo8XEBCgoqICAAYGBsHBwQq3FUlGBvL2Rmw2io9HCCF3d2RoiB48ePeEq1eRhgaaNQshhBISkI4OCghAZWVk0n5USkqKl5eXmZmZlZVVrXavFy5cwNuD/vLLL6TiUfKEm4symcx4/Iut+G7fRgDot99qH/f0REZGSDF2aGiOhw8fAoCent4bxd5FIz8fmZsjALRyJekoVEuHC63FixeLj8ybNw8Atm3bRjBVXSKRqG/fvgCwYsUK0lkomWg5uw4qsL///hsALC0tFWTLJUWmfAUhQkgkEuGRQB8fnw89JzExcfjw4bhu7Nat2w0F2e6quBgtW4ZUVREA0tR820T01SvUuzficNDw4cjHB7m6IiYTDRv2tgL84QfEYCAAZG2NTpwgG1/S/fv3x4wZ06pVK01NTTc3t4KCgrrPwYuXOBzOpUuX5J+QkjO8Sm3SpEmkgzTGl18iA4N3u4CKRGjXLsRgoO3bicaSjpkzZ9a69lVYN24gNhsxmSgyMoN0Fqolw3tWSfYe7N+/PwBERkYSTFWv2NhYJpOpoqKSmJhIOgslfS1q10FFJRKJunbtCgA///wz6SyKTikLQqyoqKjoUztdhoeHt23bFpeF7u7u6enp8slWD6EQhYaiVq0QAGIwkKcnkgwjFKJTp5C/P/L2RosWofPnkUj07tGYGNS7NwJ426Od9PBLZGSkm5ubgYEBALRr1+63ugMsEnDLLB0dHUXfko5qnpycHCUbHsRev0YuLojBQD16oGHDUNu2iMVCS5ci5b9fW1RUpKGhwWAwkpKSSGdpkG3bKvv3n2xkZJSZmUk6C9Vi4d2qxJ9HQqFQS0sLAOq9p0nc7NmzAWDo0KGkg1BSRncdlJsTJ04AgJmZWUVFBeksCk2JC8IGqqioCAoKwu/4GhoaXC63srJS3iGuXEFdu76t6Hr3RrduNfoVcD1pbIwAEJuNfHwQiU+vCxcuuLq66urqAoCmpuaQIUNyc3M//i0ikWjatGkAYG5urlDboFHShaddTZ48mXSQJomKQkFBKDAQ7d6NkpNJp5EOvNf2yJEjSQdpKJFINGbMGADo06dPdXU16ThUC1RWVobH3MS/YElJSQBgYWFBNtiHFBYWGhoaAsCZM2dIZ6GkJjs7m+46KDcikQivEN69ezfpLAqt5ReEWFZWlre3Nx4qbNeuXVhYmJxOnJyMPD3floJt2qDQ0PeG/hqrqAj5+yM2GwEgAwMUHCyfHZ2FQuGhQ4f69u2rqamJ/w7bt2+/devWBn57ZWWls7MzAPTo0aNMIVdCUs2UmZmpqqrKZDIfP35MOov0pKai1atJh2gioVCIJ0f8+++/pLM0QlFRkbW1NQAsXbqUdBaqBYqOjsafROIjuI2nu7s7wVQft3v3bgCwsbEhcC+bkgHx0kFXV1e6dFA+Tp8+DQCmpqZ0kPAjPpeCELt69aq4+eGQIUMSEhJkeLKiIhQQ8G65IJeLpPVu/uwZGjbsbZHZoweKjpbOy9anurr6xx9/7NGjh6qqKv57wwOD2dnZjXqdwsLC9u3b4/EKhevxQzXb3LlzAWDKlCmkg0hPdTWytEQAaM8e0lGaAn/+tWvXTukuOGJiYlRUVBgMxqlTp0hnoVqaPXv2AMDMmTPFR/Du1atWrSKY6uMEAgFeBLV+/XrSWSgpCAwMpEsH5a9Xr14A8NNPP5EOorg+r4IQIcTn80NCQvBgPYfD8ff3Lykpkfo5UEgIMjFBAIjJRN7eSBb/7MPDkZXVuxWJ0l51U1ZWtmrVqs6dO7NYLHFPWjs7uy1btjTtBVNTU42NjQFgzpw50o1KkdUyhwcRQmfOIAYDcThNmeNN2tChQwFgx44dpIM0xY4dO3Bz1OfPn5POQrUo3377LQDs3LlTfGTkyJEAcPz4cYKpPik6OprBYKirq7948YJ0FqpZzp8/T5cOEnH27Flch5eXl5POoqA+u4IQe/36tb+/Py51TE1NQ0JCpHUf/dy5c5UDB75rABMXJ5WXrd+bN2jlSqSmhgCQtnbhzp1SWXhTUFAwZ86ctm3bSm5cpqamNmjQoGYuAoyKisI7/wYHBzc/J6Ug8DYwStZctIH8/REAsrREhYWkozRCcnIyg8HQ0NB4/fo16SxNIRKJxo8fDwC9evWivcIpKcKjBNevXxcfMTMzA4CUlBSCqRpi6tSpADBhwgTSQaimy87OxrfFN23aRDrL56hPnz4AsL1FtBCXhc+0IMQePHgwYMAAXPD07NnzVvPGARITE93d3QHgF2dnZGuL5LZMMTMTeXsjgDG9e9vZ2UVERDT5lV6+fOnr64ubsEmytraW1tLnY8eOMZlMJpNJ54O1DGFhYfiXpDm/eIqrpgb164cAkLt7s1b/ypefnx8AfGRXHsXH4/FsbGwAwM/Pj3QWqoUQCAS47y6Px8NHXr16BQDa2tqKP7M6Ly9PR0cHAM6fP086C9UUkksH6cIZIs6fPw8ARkZGpaWlpLMoos+6IEQIiUSisLAwS0tLAGAwGN7e3i9fvmzsixQUFMybNw9vwq6np/fjjh1I7i3yeJGRHTt2xJfmHh4eqampjfp2vL+8hYVFrVJQTU3N2dlZutt1bNq0CQDU1dVv374txZel5O/w4cNMJhMAunbtSjqLzGRkIENDBICUZPplWVkZ7gOs7Bu9xMXFqaurA8CRI0dIZ6FagoSEBNydRXzk4sWLADBgwACCqRoO9w3u1KkTHTZXRnTpoCLANXlQUBDpIIrocy8IsTdv3nC5XNw3RU9PLygoqIFzL2tqasQrEplMpre3d35+vqzTfiRMcHAwvhbEyyMbchckNjYW7y8PdVhZWW3atEkkg4ER3IPE2NhY8SfqUB9y/PhxfL1uZGSEdx+Rxa+KQoiIQAwGYrNl2sBJWnDbjEGDBpEOIgX4Z9HS0nr27BnpLJTSO3z4MACMHz9efASXWL6+vgRTNVx1dTUeNvfw8CCdhWocunRQQVy6dAkADA0N6SBhXbQgfCclJQXP+QSADh06nDt37uPPj4yMdHBwwM93c3NTkPvxhYWF/v7+eNzGzMwsNDT0Q5fpkvvL16Kqqurs7Cy79es1NTXDhg3DNzuLiopkdBZKdoKDg/HvmJeXF+7jzOfzx48fz+fzSUeTjaVLEQCysCCy/2ejdO7cGQDkt7OOjOFdTLt06ULbhVPNtGzZMgBYt26d+AhemLdv3z6CqRoFz69hMpl79+4lnYVqKLp0UKEMGjQIADZu3Eg6iMKhBWFtkZGR9vb2uC5yd3evt83d06dPR4wYgZ/Tvn17Bbz2io2N7d+/P07Yu3fvO3fuSD565syZgQMH4rHEutq2bcvlcmU92lNaWopbaQ8aNIhOgFEiAoFg/vz5eIo1l8ut9RD+ogWWhXw+GjAAAaARI5ACLze6fPkyvhNUU1NDOot0lJWV4cnwtDsx1Uy49a7kDu/4s/7evXsEUzUW3mIbAJYtW9ZiJ2W0IHTpoKKJiorCkwHFa4kpjBaE9cBzL/ECbnV19YCAAPF26nj8DbcnNTAwaPjkUvkTiUShoaGtW7cWT2fNy8s7dOhQnz59NDQ06i0FVVVV+/fvn5iYKJ+E2dnZeNXilClT6AebUigrK8Oj6KqqqkePHq33OampqR07dnzz5o2cs8lcVhYyNkYASEoNlmRh3Lhx0OL2K3v06BGenHz58mXSWSglZmJiAgAZGRn4jxUVFWw2m81mK9eG75WVlYMGDcI9wN3c3JrQ9YCSJ7p0UAENHjy41mQBCtGC8CNyc3N9fHzwvDg9Pb05c+Zs375dvELPx8fn1atXpDN+muTySBaLJbmpYC2Wlpbr16+Xc2F2//59LS2tlncJ2yLl5OT06NEDAAwNDaOioj70tLy8vH/++Qd/3dKm+V2+jFgsxGIhhaxMMjIy2Gy2iopKy7vyOHjw4NatW+ltI6rJsrOzAUBfX1/8WxQTEwMAnTt3JhusaS5duoRX/ltYWHzk3Zgiiy4dVEzR0dEAoKurS5csSaIF4SdER0d36dJFsnDy8PBISkoinatxkpKSxHNcPzQwSKptw7lz59hsNoPBCA0NJRKAaoj4+HjcjNfW1raBv/+ZmZnW1tYt7Qb2ypUIALVqhXJzSUepbcWKFQAwbdo00kGoFmLfPqSvj2pt/LF8OXJ0JBSoGSIiIvC0PfGRkJAQAPDy8iKYqjmysrKcnZ0BgM1mc7lcxd8543NDlw4qMjc3NwBYs2YN6SAKhBaEn8bn86dNm6aurm5oaBgZGUk6TtOFh4dbW1vXqgatrKwCAgLIfpbgD2YOh0OnhCmmS5cu4bHx/v37FzS4q4qvr++WLVvw1yUlJTJLJ18CAXJxedS+vc/kyQp1BVZVVYVnxDVzP1WKEtu9GwEgJhNJ7hDk54esrIhFarKNGzcCwOLFi8VH5s2bBwDbtm0jmKqZ+Hw+l8vF85jo9FGFQpcOKrhbt24BgI6OzuvXr0lnURS0IGyokpKSFvCvurKycsOGDXhBDpPJdHJyUpB+7osWLcIj+I8fPyadhXrP/v37ORwOAHh6ejZqsU1NTQ2enZWfn29paal04+ofws/La2ttDQC1euqQ9fvvvwNA9+7dSQehWg5cEHp4oC5dkLhLkZIWhJ6engAgOQ8F911T6pu8WGRkJJ0+qmi++eYbunRQwQ0fPhwAVq5cSTqIoqAF4ecoMzNz0qRJa9euVZwhDqFQOH78eABo27Ytvc2pIEQiEZfLxSPJ/v7+TVvBJRAIhg4d+m5iBrmNOqXo6tWrLBaLyWRevHiRdJa3evXqBQC///476SBUy4ELwmfPEIeDtm9/e1BJC0I7OzsAEO8OJRQK8fL1hk95UGR0+qhCWbt2LQAwGAy6dFCRxcbGMhgMLS0tgvuHKxRaEFKKoqKiol+/fgDQs2fPFtijUtlUVVVNmTIFX2H8+uuvTX6dmpqavXv3vr1AKSpCNjbo5k2ppSRn/fr1AGBiYpKdnU06C7p9+zYAGBkZKVe/RErB4YIQIbR4MdLSQpmZCClnQVhWVsZkMlVUVMRdwZOTk/GQGtlgUkSnjyqC0tLSAQMG4B6wQ4cOJR2H+oRRo0YBQEBAAOkgCoH5oUYjFCVn6urq4eHhtra2sbGxkydPFgqFpBN9vl6/fj106NCjR49qa2ufPXt2zpw5TX4pDoczb948JpMJIhF4ecHYsYB3yMzJAYFAaonlbuXKlV9++eWrV6+mTp0qIP2D7N27FwBmz56tpqZGNgnVIq1dCzo64O9POkdTPXr0SCQSde7cWUVFBR+Ji4sDAPGefi0Am81eu3btxYsXW7Vqdfny5Z49e+JWipTchIWF2dvbR0dHI4QGDRp07tw50omoT1i/fj2DwdizZ09+fj7pLOTRgpBSIEZGRuHh4fr6+hEREbhlIiV/aWlp/fv3j4qKMjc3j4qKwvPspeDCBSgrg6AgAICyMhg6FM6fl84rk8BkMg8dOmRubn7jxo01a9YQTPL8+fPjx4+zWCwfHx+CMagWTFsbduyA06eV9Z9s3fLv0aNH0LIKQszNzS02NtbZ2Tk7O9vFxWXt2rUikYh0qJYvKSlp+PDhkyZNys7OdnBw2Ldv37Vr19hsNulc1Cf06NFj9OjR5eXl27ZtI52FPFoQUoqlU6dOp0+fVlVV3b59+549e0jH+ezcvn27X79+ycnJXbt2jYmJ6dq1q9ReeuRIuHQJOBxACGbNgi++AA8PAIDsbCgrk9pZ5MjY2PjEiRMcDicoKOjMmTOkYsycObO6urpz585t27YllYFq8SZPBjc3WLJEKcf1cfkn+W6GS0Rpvr8pDAsLi2vXrnG5XJFItG7dui+//JKOfshOSUnJihUrHB0dL168aGBgEBwc/OjRo9mzZ5PORTXUhg0bmEzm3r17c3NzSWchjIEQIp2Bomr7+++/p06dymQyT548OWbMGNJxPhfHjx+fPn16VVXV8OHDw8LCtLW1ZXKaHTvg8GG4dQvU1aGyEpydwd8fvvlGJueSvS1btqxYsUJfX//Bgwd1t3WRIh6Pl5ubm5eX91zCgwcP8Ht4UFBQQECA7M5OfVb4fOBwYM8e8PMD8TVCUhJ07Qra2qCpCenpcOMG2NqCmRnRoA3Tu3fve/fuXb9+/YsvvsBHLCwscnJyUlJSbG1tyWaTncuXL3t5eeXn51tYWBw9enTAgAGkE7UoCKFDhw4tX748Pz+fyWROmzbtxx9/NDIyIp2LarQJEyacOnXK2dl5ypQp2v+nr6+vo6ODv9bU1CSdUR5oQUgpqLVr165bt05dXf3gwYOTJ08mHafl27lz5+LFi0Uikb+//48//shisWR1psOHYeBAsLICAJg5E6qq4K+/AABycgAAzM1ldV7ZQAiNHz/+9OnTvXv3joqKEi9SarLCwsL09PSMjIz0971586be5zMYjL59+0ZHR+N+EhTVTNHR4OUFf/0FDx68VxACwMqVsHkzWFnB3bvQpQvU1MCuXeDtTS5rAwiFQh0dncrKyqKiIj09PQAoKCgwMTHR1tYuLi5u2f9qsrOzp0yZEh0dzWazV65cuWbNmpb988rNgwcP/Pz88F52X3zxxe7dux0dHUmHopro4MGDCxcuLPvoTCU1NTV9fX19fX11dXXx13X/KHnE2NgYb9mlLGhBSCkohNCkSZPw4qjTp0+7u7uTTtRiCQQCPz+/X3/9lcVi/fjjj/5y6x0REgJ79sCdO6CpCTU1MHgweHrCokVyOrv08Hg8JyenFy9eLF68eMeOHQ3/rrojfqmpqSUlJfU+X01NzczMzOZ9+vr6NjY20vtRqM8aQrBjBwQGgkAA3t7Qu3ftgrCyEhwcQCSCmBiYNQv+/RcAYMoU2LsX9PVJpf4EoVD48OHDxMRELy8vfCQyMnLYsGEDBgyIiooim00OBALBxo0bN2zYIBKJ3NzcDh8+jDctpJqmqKho3bp1uHW2mZnZDz/84O3tjduKUsqooqKia9euqampAwcO7Ny5c9n/lZSUFBcX468rKyub9uJ4gBGPNOrq6urq6uIjVVVV2dnZXbp0WbhwoampqXR/oiajBSGluMrLy9u0acPj8ZhM5uzZs9esWWOubMNHiq+srGzSpEnnz5/X1NQ8cuSI/CboFhdD164QGQnt2wMA+PpCVhacPg3K+cl69+7dgQMH8vn8EydO4B01xXg83vPnz2vVfo0t/ExNTc0+OjkvISHh8uXLCxculOZPRX1OSkth1iw4cQIYDPDzg+3b4ZN3t//8E+bPhzdvoHVr2L8fRo2SS9Bm27Zt2/Lly319fXfv3k06i5zQ6aPNJxKJDh8+vGTJksLCQg6HM3fu3A0bNujo6JDORTXL4sWLf/rpJ0dHx9jY2I8M6FVWVvJ4PB6PV1VVJf663iPiPxYUFHyyA7mTk1NsbKy0f6YmogUhpdAyMzNHjhz57NkzkUikpqbm4+OzYsUKxbmhouxycnLc3d3j4uJat2599uzZnj17yvX05eWAp+YfPQpcLty7B7q6bx+qqYHISHj8GPh8aNsWRo4EA4O3D5WWQlgYDBkCkj1Uamrgzz/B2Rk6dZLrjyAhODh40aJFmpqavXr10tbWFolEeKpneXl5vc/X09Oztra2tra2srJq27Yt/sLa2hrPamsshFBOTo6FhUWzfgbqc1aX0loAACAASURBVPXwIXh6Qloa6OrCwYPw/j2Nj0lPh6+/hhs3gMGAb7+FH38ExV9u4+bmduXKlZ9//nnu3Lmks8gPnT7aHPfu3fP19b179y4ADBkyZNeuXfb29qRDUc0VExPj7OzMYDDu3Lnj5OQk9dcvKSnBY4ylpaVlZWU8Hg//8enTp7dv3378+DEA3Lp1C2/BTR6h/Q8p6tPEu2wnJCR4e3vjVW0qKio+Pj45OTlks7UAcXFxuH5wcHBIT08nliM+HhkaogcP3h158AC1bYs4HNSnDxo0COnqIm1tdOjQ20eTkxEAOnbsvRcpKkIAaM8e+cWuQyQSibcklqSmpmZjY+Pm5ubj4xMUFBQWFhYbG1tUVCSjGK9evZLRK1MtVWgo0tBAAKhHD5SW9t5D+fno3r1PfLtQiIKDkaoqAkA2NigqSnZJpYDP5+OFvqGhoaSzyBvdvL4JcnNzxZNCLSwsPsNfm5aqurrawcEBAAIDA4kEwCXorFmziJy9LloQUopr2bJlkn+kZaEUnT9/HjcRHTJkCI/HIxklNhadPPnuj4WFqHVr5OSEMjPfHnnzBnl5ISbz7ZWmohaECKHHjx+bmpp279595syZZ86ciYuLKy4ulmeAixcvtmnT5s2bN/I8KaW8KirQrFkIAAEgb29UUfHeozduIDMz1KoVasgb7ePHqFs3BIDYbBQQgKqrZRS56RISEgIDA/HucAwG4/Xr16QTkREZGYmXEVpYWER9oHx/+PBh69atPT09Q0JCnj9/LueECqKmpiY4OBhPClVXV+dyueKb1FQLsGrVKgDo0KGD5P+tQqFQbgFevHihoqLCYrGSkpLkdtKPoAUhpWRwWYjvcaqqqtKysAlCQkLwVdE333xTU1NDOs77Nm1CTCZKTn7vYFUVsrJCw4YhpNAF4eTJkw8cOEAwQEVFRUZGBsEAlBJJSkJduiAApKWF/vrrvYdEIrR1K2KzEQAaPBg1cCSppgZxuYjFQgDI0RE9eiSL1I2WnJy8bt26Dh06iAftORzO0qVLSeciKSsrCy8jZLPZXC637kXwrl27JKc5tGvXbvbs2X/99VdeXh6RwPJ35coVPHwEAO7u7p9tVdxSPXr0iMPhMJlMyXsi165d69KlS0xMjNxizJo1CwC+/vpruZ3xI2hBSCmc8vLyM2fOfPw5jx8/pmVhE4hEIi6Xi2+Qc7lc0nHqM3gwsrev5/jcuUhVFVVVKWxBmJeXp6WlVV5eTjCD2H///ffw4UPSKSjFdeoU0tVFAKhjR/T48XsPlZSgiRMRAGIwkL8/auwto1u3kK0tAkBqaigoCMnxhvt7CgsLQ0JC8AIhfFlvaGjo4+MTEREhEonIZFIkn5w+mpaWFhIS4unpaSBevw0AADY2Nj4+PmFhYQUFBUSSy1pWVpb3/7dSsbOzO3fuHOlElJTx+XzcMcHf3198sLy8HO9KumHDBpmeXfIfTnp6Oh4kTExMlOlJG4IWhJTCSUtLmzRpkkAg+OQz4+Lixo0bhz/vNTQ0Fi9eXJafL4eESurVq1eurq54wu0h8ZI8RWNjg0aOrOf4li0IAGVmvi0IO3dGrq7v/vviC+IF4ZQpU1xcXAgGEAsNDTUzM7tx4wbpIBSyt7d3d3cPCQlRnDtWfD4KCHg7TXTaNFRrfvGDB6hdOwSAdHXfm8rdKKWl72aienpmynPUuri4ODQ01N3dHU+CAABdXV1vb+/w8HCFmw2hABoyfVQgECQkJODiUFfc9+v94lB2i6LlqaKiIigoSEtLCwA0NTW5XG5VVRXpUJT0/fDDDwBgZWVVVlYmPrh48WIAcHR0lOkbxbx589TU1CTLPx8fHwDw8vKS3UkbiBaElNJ7/Pixp6cng8GwMjev1tND/v4oN5d0KIXzxx9/aGhoAICent7169dJx/kwGxvk4VHP8e3bEQBKT39bEHp6orVr3/23YgXZgjAnJ8fIyEhB+g1kZWXl0n8CCiA5OVl86cxkMvv06bNx48a4uDiCkV68eDFiRAUevgsJqf3oR7rLNMH588jcXNit22AdHZ2QuieTqqqqqvDwcG9vb/wuh2eOuLu7h4aG0iW1Hyc5fXTEiBEfvxUrEAhiY2ODgoLc3NzU1dXFv94sFsvJycnf3z8sLEzOC6elJTw8vO3/O1e7u7tnihexUy1LUlKSuro6g8G4ePGi+GBMTAyLxWKz2fc+2USreb777jsAmDp1qvhIRkaGqqoqi8V6+vSpTE/9SbQgpBTImjVrtm7d2rT5PA8fPoz080MMBgJAmppo6VJERwsRSkhI4HK5+v/fNJrD4Zw/f550qI9ydkZdu9Zz3N8fsdmookIxp4xOnDixbdu2FbWacpAWERHh4uIiz1XyVC35+fmhoaGenp542AFr1aqVt7d3WFiY5P1pOYiIiDAwMHBwmGFrK7pz572HJLvL+PggaY2LvHpVNnbsWPxTT5gwQeqTDIVCYVRUlI+Pj3g7OCaT6ezsHBwc3FInNMoCn89fvnw5/gs0MDCYNGlSQ5ZR8fl8cXGoqqoq/vVms9lOTk4BAQGRkZFK0YUlOTl55MiROHy3bt0+NFJKtQBCoXDgwIHwfm/P6urqzp07A8CKFStkHSAzM1NVVZXJZMbHx4sPzps3DwAmT54s67N/HC0IKQWSmZk5Y8aMZn2ExMcjT893ZeHnOlp4//79wMBAPCFe8jpJEeapf0JgIOJwanexEIlQly7I2RkhRWwq8+LFC3Nzc496BzbJ2bp1q6Wl5c2bN0kHoRBCqKKiIjIy0t/fX3KvSHV1dTc3t+Dg4KysLJmeXSAQBAYG4tn1Y8aM4fFKJR9NTPxgdxmpCAsLw/ekTExMTp8+LZXXTEhICAgIkNyT1t7ePigoiI6NN9nGjRvF82zx/1m4y2hCQsInv7e8vDwyMpLL5bq5uUnu7i1ZHCrg9Mvy8nIul4urWX19/eDg4IasVaGUF+6WZGpqKjnJefXq1QDQvn17+dzSnT9/PgBMmjRJfCQ3N1ddXZ3JZD4i2omLFoSUQpDyWHndsvDz6I2GxwMl++kZGhp6e3v/888/CvhhXL/0dKSpicaMeW+QYvNmBID++QchRSwIx44dy2azjxw5QuTsH/Lw4cN3gySpqUgZbtV/JhISEoKCgpydnSV3Bre3tw8ICIiKipJ6y5NXr14NHToUPtBS8tgxvpYWAkAODujZM+me+Z2MjAwXFxf8k3p7e5eWln76e+rz9OlTLpfbvn178d+btbV1QECAgvRtV3ZZWVnTp0/v1KlT69atQYKVldU333zz559/NuTORVlZWWRkZEBAgJOTk+RvuIaGhpubW1BQUFRUlCKs5wwPD7e0tMR3S729vfPplKKWLj09He+2derUKfHBR48eqaioMJlMua26x+Ufg8GQXD7g5+cHAJ6envLJUC9aEFLkVVZWtm/f3t/fX8ofEo8efSZlIa4DJS+ScB0YHh7O5/NJp2u8f/5B6urI2hrNm4cWL0a9eyMGA61c+fZRBSsInz171rp1azs7O8WdHBUWhkxM0LVrpHNQtb169QpPKBXPeAQAY2NjPKG0yVWTpOvXr+NhNBMTk8uXL0s+VFVV5e/v3737Iharnu4yUicSiUJCQvAaP2tr62uN+YXMzs4ODg52dnYW/y2Zm5v7+/vT2X2yI+4yamRkJFkc2tjYeHt7h4SENKQ4LC0tFReH4navAKClpYWLw9jYWPnPaT99+rT49kTPnj1v374t5wAUEV9++SW8PzNT3G7Uz89PnkkWLFgAABMmTBAfyc3N1dDQqFUlyhktCCmFwOPx/Pz8ZLKi5pNlYUUF+vtvFBiIFi5EP/6IUlOln0E26taBRkZGSlwHSsrMROvXowkT0OjRaNEidPfuu4cKCtDixajWm2ZFBVq8GJGYHjlixAgAGD16tPxP3SBLliBbW/TgAekc1MdUVlbiCaV4yAJTU1PDE0qb1t9CJBIFBwfj+XuDBw+utYPcixcvevXqhc9y6NDjD72I1D158sTJyQkAGAyGv7//x2cu8Hg83DKUxWLhvxM9Pb0W8hanVMTFoXg5urg4xF1GCwsLP/kieXl5R44cmT17drt27SRfxNjYWNa7/PH5/JSUlLCwsIkTJ9rY2ODztmrV6vfff6d7kHwmDhw4gO+VSw4FBwUF4QFwqdx9a7i8vDxc/knuDrVo0SIAGDdunDyTSKIFIUXS3bt3t2/fLo935Hv3kLv7254JWlpoxQqEZ5A/fIjatEHq6mjoUDRuHLKyQmw2kvEuNM2E60A7O7sWWAcqm/j4+FatWnE4nGO1RiwVx8WLSNz07/FjtGUL0TTUp6WlpQUHB7u5uUku6BJPKG3gcEpJScmECRNw3RUQEFBrZRTuLgMAlpaW8tyFGePz+UFBQbhSdXBweFDnbkVlZWV4eLinp6eKioq4NnZ3dw8LC6uurpZzWkoS7jIaHBxcawsKJpNpb2+Pi0Mej/fJ18nNzQ0LC/Px8bG2ttbT05Piyj0+n5+WlhYZGRkSEhIQEODp6enk5KSmpgbvc3JyUtJuqFQT5Obm4nsZf0ksksbtRgHgwoUL8o+0ZMkSABg7dqz4yKtXr7S0tBgMxl3JO+ByRAtCiqSsrKy+ffuOHj26IR8hUhAX93a0UE0N5eSgkhJkYYG6d3/XeEYgQN9/jwDQ33/LI09j0DpQAeGlWR06dFCC69Q//0RGRmjfPtI5qIYqKCgICwvz9vaWvPI2MjLy9PQMDQ39yOXs/fv38TCIoaFhrW21BQKBeDtyDw8PgtvHxcTE4NXOHA6Hy+UKBAKBQBAZGent7Y3X+QAAi8VydnYOCQmR8/17qiEku4xKVlx4C4qAgIDw8PCSkpJPvk5eU5dy4HG/8+fP796928/Pb8SIEXZ2dpItbSTrVWtr6yFDhnTu3HncuHF0jujnZty4cQAwatQo8RGhUPjFF18AwMyZM4lEKigowK2nJcu/pUuXArkJR7QgpMgQ7w1VVVW1evVqORWEWEwMOnAAIYR27UIAqFZbJ5EI9eyJunSRX56PwnWgZL9QCwsLf3//yMhI2g+NrPv37xsbGwPAmDFjSGf5KKEQzZyJ7O3Rkydvjyh++UpJ4PP5UVFRAQEBHTt2FL8PsNlsZ2fnoKCgWq2DQ0ND8W3vnj171pqJ9/HuMvL35s2buXPn4qVl7dq1E69VYzAYzs7Oe/bsefXqFdmEVAPJeguKesf9JDdClKSvr+/s7Ozj4xMUFBQWFhYbG1teXi7FH5ZSLn///TcA6OjoSK563bNnD9RpNypneK8Xd3d38ZGCggJ8O0z+EzcQLQgpUjw8PBYsWEC41djYscjMrJ7jXC4CQESvRXAdKLnWAteBDZ8zRsmaq6srAKioqJw8eZJ0lk/58893PUPu3UPt2r0rDimlIp5QKjkSYmNj4+/vHx4ePmnSJHzEx8en1qj1R7rLkHXp0iU9PT0rKysAsLe353K5KSkppENRTYe3oAgICHB2dpb8LVVXV3d2dsbF4UemVDSz9nsj6+ZIlFIpLCxs1aoVAOyTmB2TkZFRt90okWw4xh2JbWFXrFgBACNHjpR/HgZCqN5/ZhQlUzweb/r06cXFxcePH6/V4Vp+evUCDgdu3ap9/PffYeZMePgQCgvh6lUwNAQDAzAweO8LieU9UvTkyZPjx48fPnw4LS0NH2nTps24ceM8PT2dnZ0lu7RRZN26dQvPuOvQoUN8fLx4sZOi+/NPWL4cdu2Cr74iHYVqlqKioitXrpw9e/bs2bPFxcXi4zo6Ovv37/f09BQfQQjt2rVr2bJlfD5/8ODBR48eJfaW+wH29vbPnj3bv3//rFmzSGehpInH4924ceO///67evUq3s8QH9fW1u7Xr5+amtqwYcOKi4tramqKi4tTU1NTUlLS09P5fH6t12EymZaWlrb/Z2dnZ2dnZ2NjIzkaSVF1TZs27a+//nJxcbly5Yr4CmrEiBEXLlyYNGkSHjwkaOXKlZs3bx4xYsS5c+fwkdevX9vY2JSWlt6+fbtv377yDEMLQkquampqjh075uXlxWAwEEI7d+709vY2NDQkk6Z3b2Cx4Pbt2scPHoRZsyAuDk6cgI0b6/9eHR0wNHz73/+rxHwLi0tqaoaGhgYGBgYGBviLT1ZxIpHo4cOHZ8+ePXTo0PPnz/FBWgcquEGDBt24cQMAxo8ff/LkSdJxGqCiAmbMgNRUCAsDPPJcUAAvXkDv3qSTUc2CJ5RGREQcOnSosLDw4MGDM2bMED9aWlo6c+bMkydPMhiM5cuXb9q0Sdyxk4icnJy0tDRnZ2dxjIyMDGtra11d3YKCgnoXgFEtQ0FBwZ07d27evHn58mXcSehDz9TX17e3t3dwcLD5v44dO2pqasozLdUCnDt3btSoURoaGvHx8eL5Vn/88ceMGTMMDQ2fPHmCBw8JKi4ubtu2bXFx8Y0bNwYOHIgPrl69euPGjV9++eWFCxfkGUYmoxwU9SHFxcW7d+8+e/bsgQMHtLW1Fy5cSDKNuTncvVvP8czMt49++SWoqUFREbx+DUVF731RWgqlpfDiheT3lQ0YMD06utaLiSvDul/ExsZevXo1MzOzqKgIP9nKymrixImenp69e/emdaDCunLlSnx8PACoqqpOnz6ddJyGUVGBPn0gNBRw+4foaJg6Ffz8aEGo7Dgcjqurq6urq66u7tq1a6OjoyULwt9+++3kyZOGhoaHDh3CW6SQdejQocDAwLlz5/7888/4SHh4OAAMHz6cVoMtm7GxsYeHh4eHBwDk5ubu27fvyJEjxcXFQqGwW7duI0eOtLW1bd++PR33o6SitLT0u+++A4DNmzeLq8GXL1/i9p67du0iXg0CgJ6enp+f34YNGzZs2HDp0iV8cMmSJXv27Ll48eKNGzdw5xs5kf8sVeozV1VV5ePj06lTJ/ILRXbuRADocZ09uPr0QY6On/je4mKUlobu3kUXLqAjR9Du3WjdukcbNnh5eY0cObJv3752dnaGhoYNL+rs7e2joqLonkhKYcCAAfj/tU6dOilfi1eRCAUHI1NT9H7/SUrZpaamMhgMHR2diooK8UGhULhkyZKMjAyCwST1798fAP755x/xkWHDhgHAoUOHCKaiKKqF+V97dx4f073+AfyZJZlElklIQnZJRmgRae9Yf0GJSpNQDarErmlQbQmlVaJEtffqpS5KrVXbRV0VYqkg1B4Ra2wJiWyIyGSyZ7bz++O40wh6s805k8zn/fLqa84zJ/k+p68XZ5453+/3+eijj4ioa9euVbffGzx4MPG0Qu9VCgsL2ZYYJ06c0Ae//vprIgoMDOQyExSEwJElS5ZERUXpPz1v3bq1antQfigUjLMz07Ur8/Tps4hOx3z3HUPEVGlWUx86nS4/P//u3bvnzp07cODAli1bvvnmGyKSSCRDhgzx9vbu2rUru9NUnz59GmREMLTY2Fj9tvhDhgzhO51aKihgBg5kevRg6tTrHIwc22t+9+7dfCfycvn5+SKRSCKR6NtIFBcXSyQSkUj05MkTfnMDgCYjISFBIBBIJBJ25Spr165dRGRra5tpZLe/+fPnV/sQWFhYyLaKrVolGhoKQuCIUqkMCwsLCAjI1Tf9MwbnzzNOToy9PTNsGDNhAtOxIyMUMnPmGHRMtvmMvo3Y3bt3icjLy8ugg0JD6d69O1sNSiSSuLg4vtOppfx8ZsECht/dfcFgli5daszfU/zyyy9EFBwcrI+w628DAgJ4zAoAmpLS0lK2U9e3336rD+q3G127di2Pub2UUqlky7+EhAR9MCYmhuN/G4X1mG0KUCNXr14lIltb2927d7/99tudO3dOSkriO6n/6tqVUlPpu+/I3p40GgoLoytXXrmRTAPx8PAgogcPHrCHnp6eQqEwKytLo9EYdFyov19//fX69evsa29v76CgIH7zqbUWLWjePKq6WCstjaZOpW7dqEMHCgykpUuptPTPd4cOpbVrq/+SCRPo+++5yBZqafjw4SKR6MCBA1X3HTUecXFxRDRgwIC/iAAA1MecOXPS0tI6derE9nlnTZs27fHjx2+99VZERASPub2Ura1tVFQUEc2dO1cfjIqKcnR0PH369PHjxznKg7PSE0yTRqPp3r37sGHDiouL2cjvv/+ek5PDb1b8CgkJIaLY2Fh9xNXVlYgyMjJ4zAr+J51O16XKFizvv/8+3xnVW3w806wZ064ds3Ah89NPzKefMtbWTIcOjH7+nr09M3169Z9q04YZPZrjTKGGAgMDiWjjxo18J1KdSqWys7MjovT0dDai1WrZBhgp6IoJAA3h/PnzIpFILBYnJSXpg2xTh2bNmvG/dcUrFBcXOzo6ElF8fLw+uGjRIiLq0aMHNzngCSEYlkgkSkhIaNWqlVwuT0lJIaL+/fu7uLjwnRefWrduTUQZGRl/EQEjtHnz5hs3brCvLS0tG33PtJISCg+nzp3p8mWaO5cmTqTly+ncOcrIoKgovpODOhoxYgQR/fvf/+Y7kepOnjxZWFjo5+fH/nNHRBcvXnz06JGXl9frr7/Oa2oA0BRUVlZ++OGHWq32iy+++Nvf/sYGi4qKJk6cSESLFi1ip5IaIWtr6+nTpxPRvHnz9MGpU6c6OTmdPXs2Pj6egxxQEIKhpKSkzJ49W6PRSCSSf/3rX7NmzerTp49+X11T5unpSVWmjBIKwsaAYZiVK1eWlZWxh97e3v369eM3pfravZuePKHvv3/WiILVoQNNnkw7dtB/W6FA4/L+++9bWFgcO3YsNzeX71ye86r5omwfAgCAelq4cGFKSkrbtm2rzr2cNWtWVlZW165dP/30Ux5z+58+/fRTJyenc+fO/f7772zEysqKbZLBbjpqaCgIwVDc3d1v377dt2/fhw8fEtGECRMOHz7crl07vvPiH54QNkZr167VPx4kovbt2/Pb4LsBJCaSSERvvFE93qULaTR06dKzw4oKevr0uT86HceZQs3Z2toGBwfrdLpff/2V71yec+DAAXq+INy/fz9hASEANIRr164tXrxYKBSuX7/e4r/fcp48eXLt2rUSiWTDhg1Gfsu2srKaOXMmEc2dO5dhGDY4ZcqUli1bnjt37tChQ4ZOAAUhGIqtre2ePXtCQ0PffPNNdlHsm2++ye6nYuJeLP9efGYIRkWr1a5Zs6aiooI9tLS0ZHscNW5Pn1LLliQWV4+7uxMR5ec/O1y1ihwcnvtz7x6neUIthYeHk5HNGr1161ZaWpqjo6N+FW5WVta1a9esra057bwMAE1RRUXFmDFj1Gr1Z599pm8UXFZWFhERwTBMdHR0+/bt+c2wJqZMmeLi4pKUlMQueqQqVWJ0dLS+SjQQFIRgQAKB4Isvvti4cWN4eLhRfTrhF54QNjrLly9nV8CyvL29+/bty2M+DcPCgoqKXhJXKomILC2fHY4bRzdvPvfH05O7JKH2BgwYIJVKL1y4wLa0MQbs7NDQ0FD9l/Rsy5agoCCJRMJragDQ6A0aNOjq1atOTk4LFy7UB6Ojo9PS0vz8/Nhuz8bP0tKS3Rl13rx5VR8Surq6Xrp0iZ1kYTgoCMGAHj16RETBwcHnz5/v1q0b3+kYCycnJysrq4KCAiX7yRsFoXFTq9WbNm1SqVT6iJ+fn1DY+P/xlMmopITy8qrH2QeAvr7PDps3p9dee+6PuTmneUItWVhYhIWFEdGOHTv4zuUZNJwAAANJT08/duwYEc2cOZPt80xE2dnZK1euFIvFGzduNKvaacm4TZ482dXVNTk5mZ1RT0QWFhbsQ8Kvv/7aoA8JG/9nGjBWI0eOdHNzY5+rtG7d2svLi++MjEi1OaJoRWjMli5deuvWLf1hs2bNmsJ8USJiN/PYtu25IMPQ1q3Upg299hovSUGDMKq9RgsKCs6ePWtubt6/f382Ul5efuLECaFQGBwczG9uANDYOTo6CgQCItIv6yAiNze3kydP/vDDD/rtRhsFCwuLL774gojmzp2r++9y/YkTJ7q5uSUnJ8fGxhpuaBSEYCh2dnZarXbti12t4YVHgubm5s7OzhqNJicnh8es4EUqlWrbtm1qtVof8fHx6d27N48pNRh/fwoPpzlzaMsW0mqJiAoLaeJEOneO/vEPEgj4zg/qLjAw0MXFpWVLy7y8K3znQocOHdJoNL1797axsWEj8fHxZWVlXbt2bdmyJb+5AUBjZ21tzX61tGPHjqrP0Lp16/bJJ5/wl1cdRUZGurm5Xb9+fe/evWykapWoZW/WBoCCEAyFbfzyyy+/lJaW8p2L0cEywsbiu+++u3nzZtVIE5kvytqwgcLDadw4srcnLy9ycqL//Ic2baKwML4zg3oRiUQnT4YvWXJZpdr2v882sFfNFw0NDeUtJwBoQnbt2uXi4pKSkqLfjqXxkkgks2fPJqJ58+bpHxJGRkY6OTmlpKR8+eWXBhq3qXysAePj5+fXo0cPpVK5c+dOvnMxOmhF2ChUVFTs2rWr6hdyVlZW7DcdTYSFBa1fT9nZtHkzxcTQwYOUnU1jxvx5Qnw8TZ1a/ad276b58znMEurC2XkIERUUbGcYQ32jXBMajYZtqxUSEsJGGIZhP7ShAyEANAj9Qruq27E0XhERET4+Pt27d9e3PjY3Nw8MDCSi9evX6wzT+QkFIRgQ+9F5zZo1fCdidF4s/3y9vXu5u1s/fcpXSvCimJiY27dvV434+Pjot7RuOpyd6b33aPRo6tfvz81FWX/7G73YKsbPj7y9OcsO6sbKqptE0katzi0pOcVjGqdPn1YoFK+//rpMJmMjycnJOTk57u7uHTt25DExAGhKJk2axG7Hsm/fPr5zqS9zc/OUlJR169bp98ghoidPnhCRQCCousVdA0JBCAb0wQcfODo6JiYmXtI3uQYiIvL38VkfEDCjyofvee7uJ7OyhlRpfQ78Kikp2bNnT7Wv4vz8/ARYXAeNRPPmHxBRQQGfgAAQXAAAGBZJREFUW8uws0OrPgxkd88bOHAg/ioBQEPRL7SLjo420DM0LlXrx3Pnzp2TJ08S0ezZsy0sLAwxIgpCMCCJRDJ69GgiwtYy1fi6u394+nSPQ4f+DLVuTUSEKaNGo6SkpH379lV3x7WxsZk8eTKPKQHUSvPmI4lIofiVYSr5ygENJwCAG5GRke7u7tevX//tt9/4zqUh6XS6iIgItVo9btw4dmasIQiawFxbMGZpaWm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",
|
||
"text/plain": [
|
||
"<IPython.core.display.Image object>"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"# 步骤6: 如果找到了酯基，尝试对齐（现在可以继续后续步骤）\n",
|
||
"if ester_atoms1 and ester_atoms2:\n",
|
||
" print(f\"\\n准备对齐:\")\n",
|
||
" print(f\" 参考分子(1109)酯基原子: {ester_atoms1}\")\n",
|
||
" print(f\" 目标分子(1110)酯基原子: {ester_atoms2}\")\n",
|
||
" \n",
|
||
" # 创建分子副本用于对齐\n",
|
||
" mol2_aligned = Chem.Mol(mol2)\n",
|
||
" \n",
|
||
" # 构建原子映射: (mol2_atom_idx, mol1_atom_idx)\n",
|
||
" # 注意：AlignMol的atomMap格式是 [(query_idx, ref_idx), ...]\n",
|
||
" atom_map = list(zip(ester_atoms2, ester_atoms1))\n",
|
||
" print(f\"\\n原子映射: {atom_map}\")\n",
|
||
" \n",
|
||
" # 执行对齐\n",
|
||
" try:\n",
|
||
" rmsd = rdMolAlign.AlignMol(mol2_aligned, mol1, atomMap=atom_map)\n",
|
||
" print(f\"\\n对齐成功! RMSD: {rmsd:.4f}\")\n",
|
||
" \n",
|
||
" # 显示对齐后的分子\n",
|
||
" AllChem.Compute2DCoords(mol2_aligned)\n",
|
||
" img = Draw.MolsToGridImage([mol1, mol2_aligned], molsPerRow=2, subImgSize=(600, 400), \n",
|
||
" legends=[\"参考分子(1109)\", \"对齐后(1110)\"])\n",
|
||
" display(img)\n",
|
||
" \n",
|
||
" except Exception as e:\n",
|
||
" print(f\"对齐失败: {e}\")\n",
|
||
" import traceback\n",
|
||
" traceback.print_exc()\n",
|
||
"else:\n",
|
||
" print(\"\\n无法对齐: 未找到酯基原子\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 10,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\n",
|
||
"对齐后的分子已保存到: /home/zly/project/macro_split/output/test_aligned/mol_1110_aligned.sdf\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# 步骤7: 如果对齐成功，保存结果\n",
|
||
"if ester_atoms1 and ester_atoms2:\n",
|
||
" try:\n",
|
||
" output_dir = project_root / \"output\" / \"test_aligned\"\n",
|
||
" output_dir.mkdir(parents=True, exist_ok=True)\n",
|
||
" \n",
|
||
" # 保存对齐后的分子\n",
|
||
" writer = Chem.SDWriter(str(output_dir / \"mol_1110_aligned.sdf\"))\n",
|
||
" writer.write(mol2_aligned)\n",
|
||
" writer.close()\n",
|
||
" \n",
|
||
" print(f\"\\n对齐后的分子已保存到: {output_dir / 'mol_1110_aligned.sdf'}\")\n",
|
||
" except Exception as e:\n",
|
||
" print(f\"保存失败: {e}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"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",
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||
"version": 3
|
||
},
|
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"file_extension": ".py",
|
||
"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.14.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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||
}
|