scaffold_and_side_chain_decomposition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、骨架分解"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.Murcko Scaffold\n",
    "Murcko骨架由Murcko等人设计并用药物的形状、结构分析。他们将药物分子拆解成四种单元：环系结构（ring system）、接头（linker）、骨架（scaffold）、侧链（side chain），其中scaffold又由ring system和linker组成；scaffold和side chain又组成了药物分子。这么拆解可以提供以下几点好处：\n",
    "1. Scaffold可以为药物的整体设计提供参考\n",
    "2. Ring system和linker可以为药化提供合成单元\n",
    "3. 可以通过形状的分析，评价分子库的类药程度\n",
    "\n",
    "Rdkit中也提供了一些用于骨架和侧链分析的工具\n",
    "- 获取Murcko骨架：GetScaffoldForMol()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=500x250 at 0x77F0948>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem.Scaffolds import MurckoScaffold\n",
    "m = Chem.MolFromSmiles('O=C(NCc1cc(OC)c(O)cc1)Cc1cocc1CC')\n",
    "core = MurckoScaffold.GetScaffoldForMol(m)\n",
    "m_core = [m, core]\n",
    "img = Draw.MolsToGridImage(m_core, subImgSize=(250, 250), molsPerRow=2)\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "img.save('data/scaffold_sidechain_dec_1.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - 直接获取smiles的murcko骨架，并输出smiles：MurckoScaffoldSmilesFromSmiles(smiles, mol, includeChirality)\n",
    " - 同MurckoScaffoldSmiles()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'O=C(Cc1ccoc1)NCc1ccccc1'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MurckoScaffold.MurckoScaffoldSmilesFromSmiles('O=C(NCc1cc(OC)c(O)cc1)Cc1cocc1CC')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.Generic Framework\n",
    "\n",
    "将分子以图的形式表示，对结构进行抽象，即所有的原子作为顶点，所有的键作为边，主要反映了原子间的连接属性。\n",
    " - 产生generic骨架：MakeScaffoldGeneric()<br />\n",
    "所有重原子都以碳来代替，所有的键都以单键代替"
   ]
  },
  {
   "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=500x250 at 0x7A31C88>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gen_mcore = map(MurckoScaffold.MakeScaffoldGeneric, m_core)\n",
    "img = Draw.MolsToGridImage(list(gen_mcore), molsPerRow=2, subImgSize=(250, 250))\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "img.save('data/scaffold_sidechain_dec_2.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、侧链分离"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.rdRGroupDecomposition\n",
    " - 先读进来一组分子\n",
    " - 定义一个母核，并指定侧链位置。\n",
    " - 也可以不标注侧链的序号，系统会自动生成序号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7a4f6c0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "from rdkit.Chem import rdRGroupDecomposition as rdRGD\n",
    "from rdkit.Chem import RDConfig\n",
    "fName = os.path.join(RDConfig.RDDocsDir, 'Book\\\\data\\\\s1p_chembldoc89753.txt')\n",
    "suppl = Chem.SmilesMolSupplier(fName, delimiter=\",\", smilesColumn=9, nameColumn=10)\n",
    "ms = [x for x in suppl if x]\n",
    "core = Chem.MolFromSmiles('[*:1]c1nc([*:2])on1')\n",
    "core"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "Draw.MolToFile(core, 'data/scaffold_sidechain_dec_3.png', size=(200, 200))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - rdRGD.RGroupDecompose(cores, mols, asSmiles , ...)<br />\n",
    " cores：母核<br />\n",
    " mols：要拆解的分子<br />\n",
    " asSmiles：返回smiles还是mol对象<br />\n",
    " - 变量unmatched用来存放没有匹配上母核的分子\n",
    " - 变量res是一个字典的列表，每个字典对应一个分子匹配上的母核和的侧链。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "40 0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Core': 'n1oc([*:2])nc1[*:1]',\n",
       " 'R1': 'O=C(O)CCCC1NCCOc2c1cccc2[*:1]',\n",
       " 'R2': 'CC(C)Oc1ccc([*:2])cc1Cl'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res, unmatched = rdRGD.RGroupDecompose([core], ms, asSmiles=True)\n",
    "print(len(res), len(unmatched))\n",
    "res[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=750x250 at 0x7A67888>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img = Chem.Draw.MolsToGridImage([ms[0], Chem.MolFromSmiles(res[0]['R1']), Chem.MolFromSmiles(res[0]['R2'])], molsPerRow=3, subImgSize=(250, 250))\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "img.save('data/scaffold_sidechain_dec_4.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - 还可以直接将res结果转换成dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Core</th>\n",
       "      <th>R1</th>\n",
       "      <th>R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>n1oc([*:2])nc1[*:1]</td>\n",
       "      <td>O=C(O)CCCC1NCCOc2c1cccc2[*:1]</td>\n",
       "      <td>CC(C)Oc1ccc([*:2])cc1Cl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>n1oc([*:2])nc1[*:1]</td>\n",
       "      <td>O=C(O)CCC1NCCOc2c1cccc2[*:1]</td>\n",
       "      <td>CC(C)Oc1ccc([*:2])cc1Cl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>n1oc([*:2])nc1[*:1]</td>\n",
       "      <td>O=C(O)CCC1COc2ccc([*:1])cc2CN1</td>\n",
       "      <td>CC(C)Oc1ccc([*:2])cc1Cl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>n1oc([*:2])nc1[*:1]</td>\n",
       "      <td>O=C(O)CCCC1NCCOc2c1cccc2[*:1]</td>\n",
       "      <td>CC(C)Oc1ncc([*:2])cc1Cl</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>n1oc([*:2])nc1[*:1]</td>\n",
       "      <td>O=C(O)CCCC1NCCOc2c1cccc2[*:1]</td>\n",
       "      <td>CC(C)Oc1ncc([*:2])cc1Cl</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Core                              R1  \\\n",
       "0  n1oc([*:2])nc1[*:1]   O=C(O)CCCC1NCCOc2c1cccc2[*:1]   \n",
       "1  n1oc([*:2])nc1[*:1]    O=C(O)CCC1NCCOc2c1cccc2[*:1]   \n",
       "2  n1oc([*:2])nc1[*:1]  O=C(O)CCC1COc2ccc([*:1])cc2CN1   \n",
       "3  n1oc([*:2])nc1[*:1]   O=C(O)CCCC1NCCOc2c1cccc2[*:1]   \n",
       "4  n1oc([*:2])nc1[*:1]   O=C(O)CCCC1NCCOc2c1cccc2[*:1]   \n",
       "\n",
       "                        R2  \n",
       "0  CC(C)Oc1ccc([*:2])cc1Cl  \n",
       "1  CC(C)Oc1ccc([*:2])cc1Cl  \n",
       "2  CC(C)Oc1ccc([*:2])cc1Cl  \n",
       "3  CC(C)Oc1ncc([*:2])cc1Cl  \n",
       "4  CC(C)Oc1ncc([*:2])cc1Cl  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(res)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.ReplaceCore\n",
    "除了上述方法，还可以通过Chem.ReplaceCore分离侧链\n",
    " - 先定义一个母核\n",
    " - 查看分子是否包含母核：HasSubstructMatch(core)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core = Chem.MolFromSmiles('n1cnoc1')\n",
    "ms[0].HasSubstructMatch(core)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - 去除母核，得到侧链：Chem.ReplaceCore()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[1*]c1cccc2c1OCCNC2CCCC(=O)O.[2*]c1ccc(OC(C)C)c(Cl)c1'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs = Chem.ReplaceCore(ms[0], core)\n",
    "Chem.MolToSmiles(rs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - 根据分子中原子序号对侧链进行编号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[1*]c1cccc2c1OCCNC2CCCC(=O)O.[4*]c1ccc(OC(C)C)c(Cl)c1'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rs = Chem.ReplaceCore(ms[0], core, labelByIndex=True)\n",
    "Chem.MolToSmiles(rs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - 获取分离的侧链：Chem.GetMolFrags()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[4*]c1ccc(OC(C)C)c(Cl)c1'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res = Chem.GetMolFrags(rs, asMols=True)\n",
    "Chem.MolToSmiles(res[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=900x300 at 0x7A62388>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Chem.Draw.MolsToGridImage([ms[0], res[0], res[1]], molsPerRow=3, subImgSize=(300, 300))"
   ]
  }
 ],
 "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"
  },
  "toc-autonumbering": false
 },
 "nbformat": 4,
 "nbformat_minor": 4
}