3D_conformers_generation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit.Chem import Draw"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、构象生成算法概述\n",
    "\n",
    "先来了解两种rdkit生成3D构象的方法：\n",
    "\n",
    "## 1.基于距离（distance-based）\n",
    "\n",
    "传统的距离几何（Distance Geometry）法生成构象步骤：\n",
    "* (1) 通过分子的连接表信息和一套规则，生成分子的连接边界矩阵（molecule’s distance bounds matrix ）\n",
    "* (2) 使用三角形边界平滑算法（triangle-bounds smoothing algorithm），对边界矩阵进行平滑处理\n",
    "* (3) 根据边界矩阵，随机产生一个距离矩阵。\n",
    "* (4) 把产生的距离矩阵映射到三维空间中，并为每个原子计算坐标。\n",
    "* (5) 对计算的坐标结果使用力场和边界矩阵进行粗略的优化。\n",
    "\n",
    "## 2.基于知识（knowledge-based）\n",
    "\n",
    "Riniker和Landrum开发了一种（Experimental-Torsion Basic Knowledge Distance Geometry，ETKDG）的方法。他们从晶体结构数据库的小分子结构中总结了一些规则（扭转角的倾向性，torsion angle preference，符合某种SMARTS的结构更倾向于生成某种固定的构象），并用这套规则来修正距离几何算法产生的构象。如果使用了ETKDG，就可以不再使用力场进行优化了。\n",
    "\n",
    "在rdkit中，目前已经将ETKDG作为默认的构象生成方法。使用代码实现这种方法比上面的废话都要简单，两行代码就能搞定了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "m3d=Chem.AddHs(m)\n",
    "AllChem.EmbedMolecule(m3d, randomSeed=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、代码实现\n",
    "* 添加氢原子\n",
    "* 距离几何算法生成3D结构\n",
    "* 距离几何+ETKDG生成3D构象\n",
    "* 距离几何+ETKDG生成多构象\n",
    "* 距离几何+ETKDG+MMFF生成3D构象\n",
    "* 距离几何+ETKDG+MMFF生成多构象\n",
    "* 多线程执行\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.添加氢原子\n",
    "\n",
    "* **加氢：Chem.AddHs()**\n",
    "\n",
    "在rdkit中，分子在默认情况下是不显示氢的，但氢原子对于真实的几何构象计算有很大的影响，所以在计算3D构象前，需要使用Chem.AddHs()方法加上氢原子。\n",
    "* **减氢：Chem.RemoveHs()**\n",
    "\n",
    "相对应的，可以使用该方法把氢去除。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32 54\n"
     ]
    }
   ],
   "source": [
    "m = Chem.MolFromSmiles('CC1=CC=C(C=C1)NC2=C3C(=C(C=C2)NC4=CC=C(C=C4)C)C(=O)C5=CC=CC=C5C3=O')\n",
    "m3d = Chem.AddHs(m)\n",
    "print(m.GetNumAtoms(), m3d.GetNumAtoms())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.距离几何算法生成3D结构\n",
    "使用距离几何初始化3D坐标。\n",
    "* **生成3D构象：AllChem.EmbedMolecule(mol, randomSeed, clearConfs, useExpTorsionAnglePrefs, useBasicKnowledge, ...)**<br />\n",
    "mol：传入mol对象<br />\n",
    "randomSeed：随机种子，方便结果重复<br />\n",
    "clearConfs：清除已有构象，默认True<br />\n",
    "useExpTorsionAnglePrefs和useBasicKnowledge两个参数即控制是否使用先验知识，默认都为True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=250x250 at 0x7A5BB88>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllChem.EmbedMolecule(m3d, randomSeed=10, useExpTorsionAnglePrefs=False, useBasicKnowledge=False)\n",
    "Draw.MolToImage(m3d, size=(250,250))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.距离几何+ETKDG生成3D构象\n",
    "\n",
    "* **生成3D构象：AllChem.EmbedMolecule()**<br />\n",
    "先用距离几何初始化3D坐标，再使用ETKDG算法优化<br />\n",
    "参数同上，默认useExpTorsionAnglePrefs和useBasicKnowledge为True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=250x250 at 0x7A5C108>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllChem.EmbedMolecule(m3d, randomSeed=10)\n",
    "Draw.MolToImage(m3d, size=(250,250))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.距离几何+ETKDG生成多构象\n",
    "无论使用哪种方法，都可以对一个分子生成许多构象，实现也很简单，无非使用不同的随机种子，多跑几次距离几何的计算过程就行了。\n",
    "* **生成多个构象：AllChem.EmbedMultipleConfs(mol, numConfs, maxAttempts, randomSeed, clearConfs, pruneRmsThresh, useExpTorsionAnglePrefs, useBasicKnowledge, ...)**<br />\n",
    "其中numConfs控制了生成构象的个数。<br />\n",
    "mol：mol对象<br />\n",
    "numConfs：生成的构象数量<br />\n",
    "maxAttempts：尝试生成构象的最多次数<br />\n",
    "randomSeed：随机种子<br />\n",
    "clearConfs：清除已有构象<br />\n",
    "pruneRmsThresh：根据RMS进行合并<br />\n",
    "ETKDG相关参数同上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "cids = AllChem.EmbedMultipleConfs(m3d, numConfs=10)\n",
    "print(len(cids))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **获取某个构象GetConformer(id)**<br />\n",
    "通过传入id获取指定构象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf1 = m3d.GetConformer(id=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **计算不同构象间的差异：AlignMolConformers(mol, RMSlist, ...)**<br />\n",
    "对同一分子不同的构象先进行重叠排列，再计算RMS（root mean square）值<br />\n",
    "RMSlist：用于接收RMS的列表，它由第一个构象与剩余9个构象依次比对产生"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n"
     ]
    }
   ],
   "source": [
    "rmslist = []\n",
    "AllChem.AlignMolConformers(m3d, RMSlist=rmslist)\n",
    "print(len(rmslist))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **也可以计算任意两个指定构象的RMS值：GetConformerRMS(mol, confId1, confId2, atomIds, prealigned)**<br />\n",
    "confId1：第一个构象<br />\n",
    "confId2：第二个构象<br />\n",
    "atomIds：需要对比的原子，默认全部<br />\n",
    "prealigned：构象是否已经对齐，默认False（没有时，函数会自动将它们对齐）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5515373147254072"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllChem.GetConformerRMS(m3d, 1, 9, prealigned=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.距离几何+ETKDG+MMFF生成3D构象\n",
    "对距离几何产生的构象，进行ETKDG优化后，还可以继续使用MMFF94等力场进行优化。不过需要注意的是，MMFF力场中的原子类型编码采用了自身的芳香性模型，因此在使用MMFF相关方法后，分子的芳香属性（aromaticity flags）会改变。\n",
    "\n",
    "* **使用MMFF94进行优化构象：MMFFOptimizeMolecule(mol, mmffVariant, maxIters, ...)**<br />\n",
    "mmffVariant：可选\"MMFF94\"或\"MMFF94s\"，默认MMFF94<br />\n",
    "maxIters：最多迭代次数，默认200"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=250x250 at 0x7A6A788>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllChem.EmbedMolecule(m3d, randomSeed=10)\n",
    "AllChem.MMFFOptimizeMolecule(m3d)\n",
    "Draw.MolToImage(m3d, size=(250,250))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.距离几何+ETKDG+MMFF生成多构象\n",
    "其实当使用ETKDG算法生成3D构象时，通常无需再使用MMFF94力场优化。如果执意要这么做，有一个方便的函数可以调用\n",
    "* **使用MMFF94优化多个构象：MMFFOptimizeMoleculeConfs()**\n",
    "* 返回的结果是元组组成的列表，每个元组表示每个构象的优化结果和能量(not_converged, energy)。如果not_converged是0，则收敛，接近最稳态，否则没有到达最稳态。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 102.41616335129055),\n",
       " (1, 102.33441584635808),\n",
       " (0, 102.42225287052568),\n",
       " (0, 102.32720200607255),\n",
       " (0, 102.33441556018424),\n",
       " (1, 102.41616372820093),\n",
       " (0, 102.4222529195194),\n",
       " (1, 102.33475547464988),\n",
       " (1, 102.33463971478054),\n",
       " (1, 102.32720685759661)]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cids = AllChem.EmbedMultipleConfs(m3d, numConfs=10, randomSeed=5)\n",
    "res = AllChem.MMFFOptimizeMoleculeConfs(m3d)\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 可以看到部分构象还没有收敛，尝试把maxIters参数调高到500，全部收敛。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 102.4161632241324),\n",
       " (0, 102.33441575541717),\n",
       " (0, 102.42225287052568),\n",
       " (0, 102.32720200607255),\n",
       " (0, 102.33441556018424),\n",
       " (0, 102.41616359925332),\n",
       " (0, 102.4222529195194),\n",
       " (0, 102.33441440334724),\n",
       " (0, 102.33441442488092),\n",
       " (0, 102.32720166633483)]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cids = AllChem.EmbedMultipleConfs(m3d, numConfs=10, randomSeed=5)\n",
    "res = AllChem.MMFFOptimizeMoleculeConfs(m3d, maxIters=500)\n",
    "res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.多线程\n",
    "* EmbedMultipleConfs()\n",
    "* MMFFOptimizeMoleculeConfs()<br />\n",
    "默认情况下，这两个方法都是单进程。当想使用多进程时，可以通过numThreads参数进行设置。<br />\n",
    "numThreads：默认为1。设置为0表示将会使用本机最大线程数执行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "cids = AllChem.EmbedMultipleConfs(m3d, numThreads=0)\n",
    "res = AllChem.MMFFOptimizeMoleculeConfs(m3d, numThreads=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 三、结语\n",
    "\n",
    "最后需要注意的是，构象生成是一项复杂且漫长的工作。rdkit生成的3D结构并不能取代真实的构象，它仅仅为需要3D结构的场景提供了一种快速实现的方法。不过，rdkit提供的ETKDG方法还是可以满足大多数3D需求场景的。"
   ]
  }
 ],
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