substructure_that_meet_criteria.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem.Draw import rdMolDraw2D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、简单子结构搜索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.初始化\n",
    "* 初始化分子及子结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = Chem.MolFromSmiles('C2NCC2CC1C(CCCC)C(OCCCC)C1c2ccccc2')\n",
    "p = Chem.MolFromSmarts('C1CCC1*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 获取所有匹配的结构\n",
    "* 获取所有匹配的结构：GetSubstructMatches()<br />\n",
    "返回值是原分子中与子结构匹配的原子索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5, 6, 11, 17, 18), (5, 17, 11, 6, 7), (6, 5, 17, 11, 12), (6, 11, 17, 5, 4))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches = m.GetSubstructMatches(p)\n",
    "matches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 查看匹配的结构\n",
    "* 查看匹配的结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300 at 0x6378A48>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Draw.MolToImage(m, highlightAtoms=matches[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 绘制保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = rdMolDraw2D.MolDraw2DCairo(250, 250)\n",
    "rdMolDraw2D.PrepareAndDrawMolecule(d, m, highlightAtoms=list(matches[1]))\n",
    "d.FinishDrawing()\n",
    "d.WriteDrawingText('data/simple_structure.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、带条件的子结构搜索\n",
    "2020.03版本以后，rdkit提供了一个可选功能，用于检查并筛选出符合条件的子结构。使用的时候把这段代码粘过去，按流程操作就好了。条件可以在matchers中定义，符合matchers中指定条件的子结构才会被返回。\n",
    "\n",
    "这个函数需要提供原始的分子和匹配原子的索引。\n",
    "\n",
    "## 1.定义条件\n",
    "* 假设要寻找一个侧链都带芳香性的子结构<br />\n",
    "只需要修改matchers字典，向matchers中添加一个键值对，键为\"all_aromatic\"，值为判断每个原子的芳香性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class SidechainChecker(object):\n",
    "    # 添加条件\n",
    "    matchers = {'alkyl':lambda at: not at.GetIsAromatic(),\n",
    "                'all_carbon':lambda at: at.GetAtomicNum() == 6,\n",
    "                'all_aromatic':lambda at: at.GetIsAromatic()}\n",
    "    \n",
    "    def __init__(self, query, pName='queryType'):\n",
    "        # atom: [(idx, prop), ()]\n",
    "        self._atsToExamin = [(x.GetIdx(), x.GetProp(pName)) for x in query.GetAtoms()\n",
    "                              if x.HasProp(pName)]\n",
    "        self._pName = pName\n",
    "        \n",
    "    def __call__(self, mol, vect):\n",
    "        seen = [0] * mol.GetNumAtoms()\n",
    "        for idx in vect:\n",
    "            seen[idx] = 1\n",
    "        for idx, qtyp in self._atsToExamin:\n",
    "            midx = vect[idx]\n",
    "            stack = [midx]\n",
    "            atom = mol.GetAtomWithIdx(midx)\n",
    "            stack = [atom]\n",
    "            while stack:\n",
    "                atom = stack.pop(0)\n",
    "                if not self.matchers[qtyp](atom):\n",
    "                    return False\n",
    "                seen[atom.GetIdx()] = 1\n",
    "                for nbr in atom.GetNeighbors():\n",
    "                    if not seen[nbr.GetIdx()]:\n",
    "                        stack.append(nbr)\n",
    "        return True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 对侧链设置条件\n",
    "* 给原子设置条件：SetProp(key, values)<br />\n",
    "key：属性名<br />\n",
    "values：属性值<br />\n",
    "注：p中第五个原子（索引为4），即\"*\"，表示与p连接的侧链。\n",
    "* 这里需要侧链带有芳香性的子结构"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "atom = p.GetAtomWithIdx(4)\n",
    "atom.SetProp(\"queryType\", \"all_aromatic\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 查看所有属性：GetPropNames()\n",
    "* 查看属性值：GetProp(key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['queryType']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'all_aromatic'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(list(atom.GetPropNames()))\n",
    "atom.GetProp('queryType')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 使用条件进行筛选\n",
    "* 返回侧链都带有芳香性的子结构："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5, 6, 11, 17, 18),)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params = Chem.SubstructMatchParameters()\n",
    "checker = SidechainChecker(p)\n",
    "params.setExtraFinalCheck(checker)\n",
    "matches = m.GetSubstructMatches(p, params)\n",
    "matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300 at 0x790CCC8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Draw.MolToImage(m, highlightAtoms=matches[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = rdMolDraw2D.MolDraw2DCairo(250, 250)\n",
    "rdMolDraw2D.PrepareAndDrawMolecule(d, m, highlightAtoms=list(matches[0]))\n",
    "d.FinishDrawing()\n",
    "d.WriteDrawingText('data/advance_structure.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 其他条件举例\n",
    "* 返回侧链只含有碳的子结构，重新设置属性：SetProp(\"queryType\", \"all_carbon\")<br />\n",
    "注：all_carbon需要提取在matchers中定义好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5, 6, 11, 17, 18), (5, 17, 11, 6, 7))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atom = p.GetAtomWithIdx(4)\n",
    "atom.SetProp(\"queryType\", \"all_carbon\")\n",
    "params = Chem.SubstructMatchParameters()\n",
    "checker = SidechainChecker(p)\n",
    "params.setExtraFinalCheck(checker)\n",
    "matches = m.GetSubstructMatches(p, params)\n",
    "matches"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=300x300 at 0x78F2F48>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Draw.MolToImage(m, highlightAtoms=matches[1])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}