read_write_visualize.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "参考文档：http://rdkit.org/docs/source/rdkit.Chem.rdmolfiles.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit.Chem import Descriptors\n",
    "from rdkit.Chem import PandasTools\n",
    "import pickle\n",
    "import gzip\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 简介"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取分子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读二进制"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 读取pkl文件：pickle.load(file)<br />\n",
    "file：文件对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0xa47d6f0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('data/output.pkl', 'rb') as f:\n",
    "    m = pickle.load(f)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 读取pkl字节：pickle.loads(bytes_object)<br />\n",
    "bytes_object：字节对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'\\x80\\x03crdkit.Chem.rdchem\\nMol\\nq\\x00C{\\xef\\xbe\\xad\\xde\\x00\\x00\\x00\\x00\\x0b\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x06\\x00\\x00\\x00\\x06\\x00\\x00\\x00\\x80\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x07@(\\x00\\x00\\x00\\x03\\x03\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x0b\\x00\\x01h\\x0c\\x01\\x02h\\x0c\\x02\\x03h\\x0c\\x03\\x04h\\x0c\\x04\\x05h\\x0c\\x05\\x00h\\x0c\\x14\\x01\\x06\\x00\\x05\\x04\\x03\\x02\\x01\\x17\\x00\\x00\\x00\\x00\\x16q\\x01\\x85q\\x02Rq\\x03.'\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcIAAACWCAIAAADCEh9HAAAABmJLR0QA/wD/AP+gvaeTAAAYEElEQVR4nO3deVSU9f4H8DeEwBU0mQxRwOXKpqOiMW4sXgUstyxRU9FQw9TcZoBRBEHQAJcU8S4uWSfNa3nV6lzb1TLqCuRJwWQZBtS84HqvqGzDMMvz+2P62XItnwd45jvL5/VnnU/nfU7Ht59nme/jwHEcCCGEtJUj6wCEEGLdqEYJIaRdqEYJIaRdqEYJIaRdnFgHIFaptLTUYDCwTmEObm5ufn5+rFMQi0Y1SoRpamratWtXZmZmc3Mz6yzmIJPJZs2apVQqWQchlsuBXngigmRkZGzcuNHDw8PX15d1FtFxHPfDDz80NDQcO3Zs+vTprOMQC0U1SgSoqakJCgrSaDT5+fkRERGs45jD7t27ly1b1q9fv/LycldXV9ZxiCWiR0xEgKSkpObm5tjYWDvpUACLFy8ODg6+cuVKbm4u6yzEQtE2Svg6c+ZMRESEq6urSqXq3bs36zjmc/r06cjIyM6dO6tUKnu4lUGEom2U8GI0GhUKBcdxa9eutasOBTBu3LiYmJjm5ua0tDTWWYglom2U8PL6668vWbLE19dXpVJ17tyZdRxzu3LlysCBA7Va7ddffx0eHs46DrEstI2SR6uvr8/IyACwbds2O+xQAP369UtMTOQ4TqFQGI1G1nGIZaEaJY+2YcOGmzdvhoWFzZw5k3UWZlJSUnr16nXu3LlDhw6xzkIsC13Uk0eorq4eNGiQTqf79ttvZTIZ6zgsvf322/Pnz/fy8qqsrOzatSvrOMRS0DZKHkGhUGi12vj4eDvvUAAvvvjiyJEjb968uXXrVtZZiAWhbZT8nlOnTo0fP75Lly5qtdrLy4t1HPaKiopCQ0OdnZ1LS0vpt/bEhLZR8pv0er1CoQCQkZFBHWoyatSoefPmabXatWvXss5CLAVto+Q35eXlJSQk+Pn5lZaWuri4sI5jKW7evBkYGFhfX3/ixInx48ezjkPYo22UPFxdXV1WVhaAvLw86tCf8/LyWrNmDYCEhAS9Xs86DmGPtlHycK+88sqePXuio6NPnjzZhvF79+7t37+/o0N1MCcnpxUrVrRhUKvVDho0qLq6evfu3UuXLu3wYMS6UI2ShygrKxs6dCiA4uLiQYMGteG/UFVVFRAQ0NG5Opirq6tGo2nb7LFjx2bOnCmRSNRq9RNPPNGxwYh1oWObyUOYLlcVCkXbOhSAh4eHXC7v2FQdztnZuc2zM2bMGD9+/MmTJ7Oysnbs2NGBqYjVoW2U/Np77703Y8YM2rMeqf07O7EN9IiJ/MKDV3mys7OpQ3+fVCpdtGiRXq9PSEhgnYWwRNso+YXs7Oy0tDSpVFpSUuLkRPd8HqGurs7f37+uru6jjz6aPHky6ziEDdpGyU8e/Mxxx44d1KF8SCQS0yGkpp/Mso5D2KAaJT9Zs2ZNfX399OnT6a1y/lauXCmVSqurq//617+yzkLYoIt68qNz586NGDGiU6dO9GtxoejkATtH2ygBAI7j5HK50WhMSkqiDhUqOjp68uTJDQ0N69evZ52FMEDbKAH+/yTNHj16qNVqOkmzDehUVntG2yhBY2NjSkoKgK1bt1KHto2fn9/y5csffPiPdRxiVlSjBJs2bbp+/XpISMi8efNYZ7FipuMEz5w5c/ToUdZZiFnRRb29o29ediA7/36q3aJt1N7l5V2TSDznzp1LHdp+ixYtkslkNTU127dvZ52FmA9to3btq68wbhy6d2++cKGlVy8J6zi24MyZMxEREa6uriqVqnfv3qzjEHOgbdR+GQxQKABAoehMHdpRwsLCZsyYodFoUlNTWWchZkLbqP3aswevvILevVFRAbqP14FqamqCgoI0Gk1+fn5ERATrOER0tI3aqXv3YHpVPDeXOrSD+fr6JiUlcRynUCiMRiPrOER0VKN2asMG/Oc/CA9HTAzrKLYoJSWld+/e58+ff/vtt1lnIaKji3p7pFJhyBAYDDh7FiEhrNPYqEOHDs2bN49+GGYPaBu1R4mJ0OmweDF1qIhiY2PDw8Nv3bq1adMm1lmIuGgbtTsff4wpU9CtG9RqPPkk6zQ27fz588OHD3dyciotLfX392cdh4iFtlH7otMhKQkAMjKoQ0X31FNPxcXFtba2mr5rT2wVbaP2Zft2KJUICsL336NTJ9Zp7MCtW7cCAgLq6+s/++yzZ555hnUcIgraRu3I7dvIygKA3FzqUDPp0aOH6fSsxMREvV7POg4RBdWoHUlLw717mDwZEyeyjmJPEhMT/f39y8vL9+7dyzoLEQVd1NuLkhLIZHB0xMWLCAxkncbOfPDBBzExMR4eHlVVVfTZattD26i9UChgMGDlSupQBqZNm/b000/fvXt3w4YNrLOQjkfbqF04cgSzZuHJJ6FWo1s31mnsUnl5eXBwMMdxxcXFgwcPZh2HdCTaRm1fSwuSkwEgK4s6lJmBAwcuWbLEYDAoTMdqERtC26jte/VVrF+P4GCcO4fHHmOdxo7dvXvX39//zp07//znP6dOnco6DukwtI3auGvXsGULAOTlUYcy5uHhYfoCc2JiolarZR2HdBiqURuXnIymJsycibFjWUchwLJlywYPHnzp0qWdO3eyzkI6DF3U27KiIoSGwsUFFRXo25d1GgIA+OKLL6Kjo7t06VJZWdmzZ0/WcUgHoG3UZnEc5HJwHJRK6lALEhUVNXXq1IaGhrS0NNZZSMegbdRm7d+PhQvh7Q2VCu7urNOQn7l06ZJUKtXpdEVFRcOHD2cdh7QXbaO2qbER69YBwObN1KEWp3///qtWrTIajXK5nPYYG0A1apuys3H9OkaNwty5rKOQh0lPT+/Zs2dhYeHhw4dZZyHtRRf1NujyZUil0GpRVIQRI1inIb/hzTffXLRokY+Pj0qlcnNzYx2HtB1tozZIqURLC+LiqEMt2sKFC4cPH15bW/vaa6+xzkLahbZRW/Pll4iKgrs7KivRqxfrNOR3FRYWhoWFubq6VlRU9OnTh3Uc0ka0jdoUgwEJCQCQmkodagVGjx49a9YsjUazdu1a1llI29E2alNu3cILL6CmBuXlcHVlnYbwUFtbGxQU1NTUlJ+fP2bMGNZxSFvQNmpTevRAfj7+9S/qUKvh4+OjVCoBKBQKg8HAOg5pC9pGCWFMo9EMGDDg6tWrb7zxRnx8POs4RDCqUULYO3z48Jw5czw9PdVq9eOPP846DhGGLuqtSVgYHBzg4IBfHQ/06ac//nMHB4SFMQpH2mH27Nljxoy5fft2dnY26yxEMNpGrUx1Nfz9AeB//78tWYLVq+HnZ/5QpAMUFxfLZDInJ6eLFy8GBASwjkMEoG3UyjxoySVLfv2vBg6kDrViw4YNW7hwYWtrq+mJE7EiVKPWJy8Pixfj9ddRXc06CulQOTk5jz/++IcffvjZZ5+xzkIEoBq1SqtXAwD9htDGeHp6rlu3DkBiYqJOp2Mdh/BFNWqV/PxoIbVNcrk8ICCgoqJi9+7drLMQvqhGrZVpIZ0/n3UO0qGcnZ1NJ5V8+umnrLMQvqhGrZVpIS0oAP1xI4QtqlErtncvAGRlsc5BHqa1tbVtU6tXrwYwadKkjk5ExEI1at3y8lBQ8Ou38YkleOmll5599tmrV68KmsrLy1Or1QMGDFi6dKlIwUiHoxq1bnI5ACgUrHOQXyosLHznnXdOnTol6Octt2/fzsnJAbBjx45OnTqJlo50MKpRq5eXB4AWUgtiNBoVCgXHcatXr+4r5NvWKSkp9+/fnzp16jPPPCNaOtLxqEatHi2klubAgQNnz5719vZOTk7mP1VcXLx///4HT+qJFaEatQWffAJQk1qGxsZG0yv0W7Zs4f+hOo7j5HK5aY2lH9RbHapRWzBxIkJDWYcgAIDs7OwbN26MGjUqNjaW/9Thw4e/+eYbT0/P1NRU8bIRkVCNWpnqapSXP+Sfp6WZPQr5H5cvX87Ly3NwcNi5c6eDgwPPKY1Gk5KSAmDTpk102Kg1ohq1JmFh8PfH66/DweHXb93/fCG9ft380QgAJCUltbS0zJ8/f4SQb1tv2bLl6tWrw4YNW7BggWjRiIjovFGb0p5P2lVWws0NPj7iJLMDX375ZVRUlLu7e2VlZS/e32WlT9rZANpGbUr37rh3D1euIDdX2OChQxg8GHTQZZsZDAaFQgFg3bp1/DsUgFKpbGpqmjNnDnWo9aJt1NacPo3ISLi7o7JSwKfqa2sRFISmJuTng/44t8Hf/va3FStW/PGPfywrK3PlfSFQUFAQHh7u6upaUVHRp08fURMS8dA2amvGjUNMDBobkZIiYMrH58dVVKEAfeVXqLt372ZmZgLYtm0b/w598JZ+cnIydah144jNuXyZc3XlHBy4oiIBU83NXJ8+HMDt2ydaMhu1cuVKAJGRkYKm9u3bB8DHx6exsVGkYMQ86KLeNqWmYtMmjBqFggLwfvEGhw9jzhx4ekKtBr14w1NFRUVwcLDRaDx//vyQIUN4TjU0NAQGBt64cePdd9+dPXu2qAmJ2Oii3jalpqJXLxQV4e9/FzA1ezbGjMHt26Cv/PKXkJCg0+mWLl3Kv0MBbNy48caNG6GhobNmzRIvGzEP2kZt1oEDWLAA3t5QqeDuznequBgyGZyccPEi6EeJj3T8+PHnnnvOw8NDrVZ3796d59SlS5ekUqlOpysqKho+fLioCYkZ0DZqs+LiMHIkrl3D5s0CpoYNw8KFaG2ll58e7cERy5mZmfw7FEBCQoJWq124cCF1qG2gbdSWFRUhNBTOzigtFfAJ+9u3ERCA+/fx6aeYMEHMfFZu69atycnJAwYMuHDhAv/jQb/44ovo6OguXbpUVlb27NlT1ITEPGgbtWWjRmHuXGi1wl5+8vTEunUAkJgI+srvb3lwxHJubi7/DtXr9aa39NPT06lDbQfjNwWIyGprOXd3DuBOnBAwpdVyAQEcwO3cKVoyK/fSSy8BmDp1qqCpvLw8AP37929paREpGDE/uqi3fVlZSE+HVIqSEjg58Z06fhzPPQcPD6jVEHLfzy4UFxfLZDInJ6eLFy/yPx60rq4uICDgzp07x48ff/bZZ0VNSMyJLuptn1KJvn1RVoY33xQwNXUqJkzA3bvIyBAtmdVSKBRGo1Eulws6Yjk9Pf3OnTtRUVHUoTaGtlG7cPQoXngBEgmqqiCR8J2qqEBwMIxGnD8PIe9E2rh33303NjbW09NTrVbzPx60vLw8ODgYwPnz5wcPHixmQGJutI3ahZkzMXYs6uqEfdR+wAAsXQqDAQkJoiWzNg+OWM7JyRF0xHJCQoJer1+2bBl1qA1ifXOWmElJCffYY5yTE1daKmCqro7r3p0DuPffFy2ZVcnIyAAwbNgwg8HAf+q9994DIJFI/vvf/4qXjbBC26i9CA5GfDz0emGrpYcHMjMBQKlES4s4yaxHbW3ttm3bAOTl5Tk68v2z09raunbtWgCvvvrqE088IWI+wgjVqB3JykK3bjh58scvifK0dCmGDMHly9ixQ7RkVmL16tVNTU2zZ88WdMTy9u3bq6qqBg4cuHjxYvGyEYboEZN9yc1FUhL8/FBaChcXvlNffomoKMFHQduYth2xfOvWrYCAgPr6+s8///zpp58WNSFhhbZR+7JyJQIDUV2NXbsETEVG4vnn0diIzEy9aNEs2oMjltesWSPoiOXk5OT6+vpp06ZRh9oy1jdnibl9/DEHcF27cjduCJiqruYiI7+XSPzOnj0rWjTL9cYbb0D4Ecvfffedo6Ojs7OzWq0WLxthjrZRuzNpEiZORH39j8+OeOrfHyNGvFNXV71q1SrOzm4ENTQ0pKenA3jttdfc3Nx4TnEcZ3pLPykpyd/fX8yAhDXWPU4YqKrinJ05R0fuu+8ETDU0NJi+eXnw4EHRolki02l4o0ePNhqN/KcOHjwIoEePHvfv3xcvG7EEVKN2KiGBA7iwME5IM3BvvfUWAG9vb/v5fFB1dbWLi4ujo6OguxlNTU29e/cGsH//fvGyEQtBNWqn7t/nvLw4gDt6VMCU0WgcMWIEgPT0dNGiWRbT79/j4+MFTaWlpQF46qmnBL2lT6wUvfBkv/buxdKl8PWFSoXOnflOFRYWhoWFubi4VFRU9O3bV8R8FqBtRyzX1NQEBQVpNJqvv/46PDxc1ITEEtAjJvv18ssICUFNDXJzBUyNHj06Nja2paVlzZo1okWzCHq9PiEhAcKPWE5MTGxubp47dy51qJ2gbdSuffUVxo1D9+7chQv3evXy4Dl17dq1wMDApqam06dPjx07VsyALP35z3+Wy+X9+/cvKytz4f1bhTNnzkRERPzhD3+oqKgw3R4lNo+2Ubs2dixWrbrs7ByanLyK/5S3t3dycjIAhUJhMBhES8dSXV3dxo0bAeTm5vLvUNMhpBzHpaSkUIfaEcb3Zglr//73vzt37uzg4PDNN9/wn9JoNKYbo3v37hUvG0PLli0DEBUVJWhqz549AHx9fZuamkQKRiwQ1Sjh1q1bByAkJETQY+V//OMfAJ588sm7d++Kl42JsrIyJycnJyen77//nv/U/fv3vby8ABw5ckS8bMQCUY2Str/k+Kc//QlAYmKiSMFYMf3+feXKlYKmTM+jwsLCBL2lT2wAPWIiAHDw4MG4uLgePXqo1equXbvynCopKZHJZI6OjhcvXgwMDBQ1odl88MEHMTExHh4eVVVV/I8Hra6ulkqler3+7NmzISEhoiYkloYeMREAmDdvXnh4+K1btzZv3sx/aujQofHx8TqdLjExUbxs5tTa2mp6eib0iGW5XN7a2rpo0SLqUHvEeh0mlqJtxxHdvn27W7duAD755BPxsplNTk4OgIEDB+p0Ov5TJ06cANC1a9cbgk7NIraCapT8JC4uDkBMTIygKdN3Nfz8/LRarUjBzOPmzZumGxqff/45/ymdTieVSgFs375dvGzEklGNkp+0rUdaW1tNN0Zzc3PFy2YGCxYsAPD8888Lmtq+fbvpb5GWlhaRghELRzVKfiE7O7sNV7UfffSRtV/Vnjt3rj33ND7++GPxshELR4+YyC8olUp/f//y8vJ9+/bxn5o8efLEiRPr6+szBZ0FbUneeusto9GYkJAg6Ijl9PT0e/fuRUdHT5o0SbxsxMLRC0/k195///3p06dLJBK1Ws3/abVKpRoyZIjBYLDSN344jjt69OjEiRO7dOnCc+TChQshISEODg4lJSWm26PEPj1mvesDEcmAAQMKCgrKysq0Wu2ECRN4TnXv3r2urk4ikUybNk0ikYiaUAwODg5SqZT/z+cBzJkz58qVK3K5/MUXXxQvGLF8tI2ShygrKxs6dCiA4uLiQYMG8ZzS6/VOTk5i5rIgR48efeGFFyQSSVVVlTX+tUE6EN0bJQ8hlUpffvllvV6vUCj4T9lPhz44bjUnJ4c6lNA2Sh6urq4uICDgzp07H3744ZQpU1jHsSxZWVnp6elSqbSkpMR+/vIgv4W2UfJwEonE9FVhhUKh1WpZx7Eg165d27JlC4AdO3ZQhxJQjZLfsXz58kGDBl26dOkvf/kL6ywWZO3atY2NjTNmzBg/fjzrLMQi0EU9+T2nTp0aP3680G+62bCioqLQ0FAXF5fy8vJ+/fqxjkMsAm2j5PdER0dPmTKloaFh/fr1rLOwx3Gc6RshSqWSOpQ8QNsoeYRLly5JpVKdTvftt9/KZDLWcVg6cODAggULvL29VSqVu7s76zjEUtA2Sh6hf//+K1asePCxNtZxmGlsbExNTQWwefNm6lDyc1Sj5NEyMjK8vLwKCgqOHDnCOgszOTk5169fHzVq1Ny5c1lnIZaFLuoJL/v27Vu8eLGPj49KpXJzc2Mdx9wuX74slUq1Wm1hYeHIkSNZxyGWhbZRwkt8fLxMJqutrTUd0mxvlEplS0tLXFwcdSj5X7SNEr4KCgrCw8NdXV0rKir69OnDOo75nD59OjIy0t3dvbKyslevXqzjEItD2yjhKzQ0dObMmRqNxvSkxU4YDAbTwQKpqanUoeShaBslAtTU1AQFBWk0mvz8/IiICNZxzGHXrl3Lly/v169feXm5q6sr6zjEElGNEmEyMjI2btzo4eHh6+vLOovoOI774YcfGhoajh07Nn36dNZxiIWiGiXCNDU17dq1KzMzs7m5mXUWc5DJZLNmzVIqlayDEMtFNUraorS01GAwsE5hDm5ubn5+fqxTEItGNUoIIe1CT+oJIaRdqEYJIaRdqEYJIaRdqEYJIaRd/g9L1P+uigP0iAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0xa4568f0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('data/output.pkl', 'rb') as f:\n",
    "    pklStr = f.read()\n",
    "print(pklStr)\n",
    "m = pickle.loads(pklStr)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 转换二进制m.ToBinary()\n",
    "* 读取二进制Chem.Mol()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'\\xef\\xbe\\xad\\xde\\x00\\x00\\x00\\x00\\x0b\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x06\\x00\\x00\\x00\\x06\\x00\\x00\\x00\\x80\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x07@(\\x00\\x00\\x00\\x03\\x03\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x0b\\x00\\x01h\\x0c\\x01\\x02h\\x0c\\x02\\x03h\\x0c\\x03\\x04h\\x0c\\x04\\x05h\\x0c\\x05\\x00h\\x0c\\x14\\x01\\x06\\x00\\x05\\x04\\x03\\x02\\x01\\x17\\x00\\x00\\x00\\x00\\x16'\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0xa50b870>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binStr = m.ToBinary()\n",
    "print(binStr)\n",
    "m = Chem.Mol(binStr)\n",
    "m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读SMILES/SMARTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 直接读取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从SMILES/SMARTS直接读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = Chem.MolFromSmiles('C[C@H](O)c1ccccc1')\n",
    "m = Chem.MolFromSmarts('Cc1ccccc1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 文件批量读取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从.smi批量读取：SmilesMolSupplier(data, delimiter, smilesColumn, nameColumn, titleLine, sanitize)<br />\n",
    "data：数据文件<br />\n",
    "delimiter：分隔符，默认为' '<br />\n",
    "smilesColumn：SMILES所在列，默认为0<br />\n",
    "nameColumn：SMILES名称所在列，默认为1<br />\n",
    "titleLine：是否含有标题行，默认True<br />\n",
    "sanitize：是否检查正确性，默认True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['C1=CC=CC=CC=C1', 'c1ccccc1', 'c1ccoc1']\n"
     ]
    }
   ],
   "source": [
    "suppl = Chem.SmilesMolSupplier('data/batch_smiles.smi', delimiter='\\t')\n",
    "mols = [Chem.MolToSmiles(mol) for mol in suppl]\n",
    "print(mols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 文本批量读取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从文本批量读取SmilesMolSupplierFromText()<br />\n",
    "参数基本同上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['C1=CC=CC=CC=C1', 'c1ccccc1', 'c1ccoc1']\n"
     ]
    }
   ],
   "source": [
    "with open('data/batch_smiles.smi', 'r') as f:\n",
    "    mols_text = f.read()\n",
    "suppl = Chem.SmilesMolSupplierFromText(mols_text, delimiter='\\t')\n",
    "mols = [Chem.MolToSmiles(mol) for mol in suppl]\n",
    "print(mols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DataFrame批量读取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 读取DataFrame中的SMILES：PandasTools.AddMoleculeColumnToFrame(frame, smilesCol, molCol, includeFingerprints)<br />\n",
    "frame：DataFrame对象<br />\n",
    "smilesCol：SMILES所在列<br />\n",
    "molCol：新列名，将存放产生的rdkit mol对象<br />\n",
    "includeFingerprints：是否生成指纹"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Name                                             SMILES  \\\n",
      "0    Lanreotide  c1(c2c(cccc2)[nH]c1)C[C@@H]1NC(=O)[C@@H](Cc2cc...   \n",
      "1  Lansoprazole        Cc1c(OCC(F)(F)F)ccnc1CS(=O)c2nc3ccccc3[nH]2   \n",
      "\n",
      "                                                 mol        MW  \n",
      "0  <img data-content=\"rdkit/molecule\" src=\"data:i...  1096.347  \n",
      "1  <img data-content=\"rdkit/molecule\" src=\"data:i...   369.368  \n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('data/smiles_df.csv')\n",
    "PandasTools.AddMoleculeColumnToFrame(df,'SMILES','mol',includeFingerprints=True)\n",
    "df['MW'] = df['mol'].apply(Descriptors.MolWt)\n",
    "print(df.head(2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读.sdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 文件批量读取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从.sdf里批量读取：Chem.SDMolSupplier(fileName, sanitize, removeHs, strictParsing)<br />\n",
    "fileName：文件名<br />\n",
    "sanitize：检查化合价，计算芳香性、共轭、杂化、kekule，默认True<br />\n",
    "removeHs：是否隐藏氢原子，默认True<br />\n",
    "strictParsing：是否使用严格模式进行解析，默认True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['C1=C\\\\C=C/C=C\\\\C=C/1', 'c1ccccc1', 'c1ccoc1']\n"
     ]
    }
   ],
   "source": [
    "suppl = Chem.SDMolSupplier('data/batch.sdf')\n",
    "mols = [Chem.MolToSmiles(mol) for mol in suppl if mol]\n",
    "print(mols)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 压缩包批量读取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从file object/.gz里读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['C1=C\\\\C=C/C=C\\\\C=C/1', 'c1ccccc1', 'c1ccoc1']\n"
     ]
    }
   ],
   "source": [
    "gz_file = gzip.open('data/batch.sdf.gz', 'r')\n",
    "suppl = Chem.ForwardSDMolSupplier(gz_file)\n",
    "mols = [Chem.MolToSmiles(mol) for mol in suppl if mol]\n",
    "print(mols)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读.mol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从.mol里读取：Chem.MolFromMolFile(fileName, sanitize, removeHs, strictParsing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c1cocc1\n"
     ]
    }
   ],
   "source": [
    "m = Chem.MolFromMolFile('data/output.mol')\n",
    "print(Chem.MolToSmiles(mol))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读.mol2<br />\n",
    "不推荐，容易出bug"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从.mol2里读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[C]1[C][C][C]1\n"
     ]
    }
   ],
   "source": [
    "m = Chem.MolFromMol2File('data/output.mol2')\n",
    "print(Chem.MolToSmiles(m))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读其他格式：pdb, fasta, ...\n",
    "其他格式大同小异，不再赘述了，感兴趣可自己尝试"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从.pdb里读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Chem.MolFromPDBFile()\n",
    "Chem.MolFromPDBBlock()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 读.fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Chem.MolFromFASTA()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 读peptide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Chem.MolFromSequence()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 输出分子"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 输出二进制"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 可以使用python的pickle将分子转成二进制<br />\n",
    "相对于SMILES和文件，二进制pkl格式体积更小，读取速度更快<br />\n",
    "* 输出pkl文件：pickle.dump(obj, file, ...)<br />\n",
    "obj：要封装的对象<br />\n",
    "file：要写入的文件<br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "m = Chem.MolFromSmiles('c1ccncc1')\n",
    "with open('data/output.pkl', 'wb') as f:\n",
    "    pickle.dump(m, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 输出pkl字节：pickle.dumps()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\x80\\x03crdkit.Chem.rdchem\\nMol\\nq\\x00C{\\xef\\xbe\\xad\\xde\\x00\\x00\\x00\\x00\\x0b\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x06\\x00\\x00\\x00\\x06\\x00\\x00\\x00\\x80\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x07@(\\x00\\x00\\x00\\x03\\x03\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x0b\\x00\\x01h\\x0c\\x01\\x02h\\x0c\\x02\\x03h\\x0c\\x03\\x04h\\x0c\\x04\\x05h\\x0c\\x05\\x00h\\x0c\\x14\\x01\\x06\\x00\\x05\\x04\\x03\\x02\\x01\\x17\\x00\\x00\\x00\\x00\\x16q\\x01\\x85q\\x02Rq\\x03.'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pkl = pickle.dumps(m)\n",
    "pkl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 输出二进制m.ToBinary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\xef\\xbe\\xad\\xde\\x00\\x00\\x00\\x00\\x0b\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x06\\x00\\x00\\x00\\x06\\x00\\x00\\x00\\x80\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x07@(\\x00\\x00\\x00\\x03\\x03\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x06@h\\x00\\x00\\x00\\x03\\x03\\x01\\x0b\\x00\\x01h\\x0c\\x01\\x02h\\x0c\\x02\\x03h\\x0c\\x03\\x04h\\x0c\\x04\\x05h\\x0c\\x05\\x00h\\x0c\\x14\\x01\\x06\\x00\\x05\\x04\\x03\\x02\\x01\\x17\\x00\\x00\\x00\\x00\\x16'"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "binStr = m.ToBinary()\n",
    "binStr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 输出SMILES/SMARTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出默认式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 输出SMILES：MolToSmiles(mol, isomericSmiles, kekuleSmiles, canonical, ...)<br />\n",
    "kekuleSmiles：默认False，不使用kekule时：脂肪族碳用\"C\"表示（大写），芳香族用\"c\"表示（小写）<br />\n",
    "isomericSmiles：默认True，区分同分异构体（\"@\"表示手性，\"\\\"和\"/\"表示顺反异构）<br />\n",
    "canonical：默认True，输出标准SMILES <br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['C1=CC=CC=CC=C1', 'c1ccccc1', 'c1ccoc1']\n"
     ]
    }
   ],
   "source": [
    "m1 = Chem.MolFromSmiles('C1=CC=CC=CC=C1')\n",
    "m2 = Chem.MolFromSmiles('C1=CC=CC=C1')\n",
    "m3 = Chem.MolFromSmiles('C1=COC=C1')\n",
    "mols = [m1, m2, m3]\n",
    "print([Chem.MolToSmiles(mol) for mol in mols])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出kekule式"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 输出kekule形式<br />\n",
    "kekule形式：在符合4N+2规则的芳香体系中，通过使用双键代替小写的碳原子来表示芳香性<br />\n",
    "4N+2规则：也叫Hueckel规则，在闭环共轭体系中，当π电子数为4n+2时，才具有芳香性<br />\n",
    "注：m1有共轭结构，但不属于芳香系统。m3中氧提供了2个π电子，碳各提供1个，总数为6，属于芳香系统"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['C1=CC=CC=CC=C1', 'C1=CC=CC=C1', 'C1=COC=C1']\n"
     ]
    }
   ],
   "source": [
    "for mol in mols:\n",
    "    Chem.Kekulize(mol)\n",
    "print([Chem.MolToSmiles(mol, kekuleSmiles=True) for mol in mols])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 设置立体参数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 不区分同分异构体<br />\n",
    "通过isomericSmiles控制"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C[C@H](O)c1ccccc1\n",
      "CC(O)c1ccccc1\n"
     ]
    }
   ],
   "source": [
    "m4 = Chem.MolFromSmiles('C[C@H](O)c1ccccc1')\n",
    "print(Chem.MolToSmiles(m4))\n",
    "print(Chem.MolToSmiles(m4, isomericSmiles=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 批量输出SMILES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 批量输出SMILES：SmilesWriter(fileName, delimiter, includeHeader, nameHeader, isomericSmiles, kekuleSmiles) <br />\n",
    "fileName：输出文件名<br />\n",
    "delimiter：分隔符，默认为' '<br />\n",
    "includeHeader：是否写入表头，默认True<br />\n",
    "nameHeader：分子名一列的列名，默认'Name'<br />\n",
    "isomericSmiles：立体信息，默认True<br />\n",
    "kekuleSmiles：kekule形式，默认False<br />\n",
    "返回一个writer对象<br />\n",
    "* 写入SMILES：writer(mol, confId) <br />\n",
    "mol：mol对象<br />\n",
    "conFId：写入的第几个构象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer = Chem.SmilesWriter('data/batch.smi', delimiter='\\t')\n",
    "for i, mol in enumerate(mols):\n",
    "    writer.write(mol)\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SMILES\tName\t\n",
       "C1=CC=CC=CC=C1\t0\n",
       "c1ccccc1\t1\n",
       "c1ccoc1\t2\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%more data/batch.smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 批量输出SMILES和属性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 批量输出SMILES及属性，通过以下函数进行操作：<br />\n",
    "mol.GetPropNames()，查看分子属性列表<br />\n",
    "mol.GetProp()，获取相应属性<br />\n",
    "mol.SetProp(key, val)，新增属性名key、对应属性值val<br />\n",
    "writer.SetProps()，设置哪些属性要输出\n",
    "\n",
    "* 以输出分子量和LogP为例<br />\n",
    "使用Descriptors计算属性，并添加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of mols: 3\n",
      "mol properties: ['MW', 'LOGP']\n"
     ]
    }
   ],
   "source": [
    "writer = Chem.SmilesWriter('data/batch_smiles.smi', delimiter='\\t', nameHeader='mol_id')\n",
    "writer.SetProps(['LOGP', 'MW'])\n",
    "for i, mol in enumerate(mols):\n",
    "    mw = Descriptors.ExactMolWt(mol)\n",
    "    logp = Descriptors.MolLogP(mol)\n",
    "    mol.SetProp('MW', '%.2f' %(mw))\n",
    "    mol.SetProp('LOGP', '%.2f' %(logp))\n",
    "    mol.SetProp('_Name', 'No_%s' %(i))\n",
    "    writer.write(mol)\n",
    "writer.close()\n",
    "print('number of mols:', writer.NumMols())\n",
    "print('mol properties:', [i for i in mol.GetPropNames()])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SMILES\tmol_id\tLOGP\tMW\n",
       "C1=CC=CC=CC=C1\tNo_0\t2.22\t104.06\n",
       "c1ccccc1\tNo_1\t1.69\t78.05\n",
       "c1cocc1\tNo_2\t1.28\t68.03\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%more data/batch_smiles.smi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出SMARTS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 输出SMARTS：MolToSmarts()<br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[#6]1:[#6]:[#8]:[#6]:[#6]:1'"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Chem.MolToSmarts(m3, isomericSmiles=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 输出.sdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 批量输出到.sdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 批量输出到文件：SDWriter()<br />\n",
    "使用方法类似于SMILES的批量输出<br />\n",
    "可以自定义属性信息，并记录在.sdf文件中<br />\n",
    "返回writer对象\n",
    "* 写入sdf：writer(mol, confId) <br />\n",
    "mol：mol对象<br />\n",
    "conFId：写入的第几个构象（不同构象坐标不一样）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "writer = Chem.SDWriter('data/batch.sdf')\n",
    "writer.SetProps(['LOGP', 'MW'])\n",
    "for i, mol in enumerate(mols):\n",
    "    mw = Descriptors.ExactMolWt(mol)\n",
    "    logp = Descriptors.MolLogP(mol)\n",
    "    mol.SetProp('MW', '%.2f' %(mw))\n",
    "    mol.SetProp('LOGP', '%.2f' %(logp))\n",
    "    mol.SetProp('_Name', 'No_%s' %(i))\n",
    "    writer.write(mol)\n",
    "writer.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "No_0\n",
       "     RDKit          2D\n",
       "\n",
       "  8  8  0  0  0  0  0  0  0  0999 V2000\n",
       "    1.9598    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "    1.3858   -1.3858    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "   -0.0000   -1.9598    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "   -1.3858   -1.3858    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "   -1.9598    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "   -1.3858    1.3858    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "    0.0000    1.9598    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "    1.3858    1.3858    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "  1  2  2  0\n",
       "  2  3  1  0\n",
       "  3  4  2  0\n",
       "  4  5  1  0\n",
       "  5  6  2  0\n",
       "  6  7  1  0\n",
       "  7  8  2  0\n",
       "  8  1  1  0\n",
       "M  END\n",
       ">  <LOGP>  (1) \n",
       "2.22\n",
       "\n",
       ">  <MW>  (1) \n",
       "104.06\n",
       "\n",
       "$$$$\n",
       "No_1\n",
       "     RDKit          2D\n",
       "\n",
       "  6  6  0  0  0  0  0  0  0  0999 V2000\n",
       "    1.5000    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "    0.7500   -1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "   -0.7500   -1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "   -1.5000    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "   -0.7500    1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "    0.7500    1.2990    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "  1  2  2  0\n",
       "  2  3  1  0\n",
       "  3  4  2  0\n",
       "  4  5  1  0\n",
       "  5  6  2  0\n",
       "  6  1  1  0\n",
       "M  END\n",
       ">  <LOGP>  (2) \n",
       "1.69\n",
       "\n",
       ">  <MW>  (2) \n",
       "78.05\n",
       "\n",
       "$$$$\n",
       "No_2\n",
       "     RDKit          2D\n",
       "\n",
       "  5  5  0  0  0  0  0  0  0  0999 V2000\n",
       "    1.2760    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "    0.3943    1.2135    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "   -1.0323    0.7500    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "   -1.0323   -0.7500    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "    0.3943   -1.2135    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "  1  2  2  0\n",
       "  2  3  1  0\n",
       "  3  4  1  0\n",
       "  4  5  2  0\n",
       "  5  1  1  0\n",
       "M  END\n",
       ">  <LOGP>  (3) \n",
       "1.28\n",
       "\n",
       ">  <MW>  (3) \n",
       "68.03\n",
       "\n",
       "$$$$\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%more data/batch.sdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 批量输出到.gz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 批量输出到.gz<br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "outf = gzip.open('data/batch.sdf.gz','wt+')\n",
    "writer = Chem.SDWriter(outf)\n",
    "for mol in mols:\n",
    "    writer.write(mol)\n",
    "writer.close()\n",
    "outf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 输出.mol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出为连接表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 直接输出：MolToMolBlock()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "     RDKit          2D\n",
      "\n",
      "  4  4  0  0  0  0  0  0  0  0999 V2000\n",
      "    1.0607    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -0.0000   -1.0607    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "   -1.0607    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "    0.0000    1.0607    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
      "  1  2  1  0\n",
      "  2  3  1  0\n",
      "  3  4  1  0\n",
      "  4  1  1  0\n",
      "M  END\n",
      "\n"
     ]
    }
   ],
   "source": [
    "m1 = Chem.MolFromSmiles('C1CCC1')\n",
    "print(Chem.MolToMolBlock(m1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 输出到.mol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 输出到文件：MolToMolFile(mol, filename, includeStereo, ...)\n",
    "mol：mol对象\n",
    "filename：文件名\n",
    "includeStereo：立体信息，默认True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "m1.SetProp('_Name', 'cyclobutane')\n",
    "Chem.MolToMolFile(m1, 'data/output.mol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cyclobutane\n",
       "     RDKit          2D\n",
       "\n",
       "  4  4  0  0  0  0  0  0  0  0999 V2000\n",
       "    1.0607    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "   -0.0000   -1.0607    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "   -1.0607    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "    0.0000    1.0607    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
       "  1  2  1  0\n",
       "  2  3  1  0\n",
       "  3  4  1  0\n",
       "  4  1  1  0\n",
       "M  END\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%more data/output.mol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 输出其他格式:pdb, fasta, xyz...\n",
    "其他一些格式，大同小异，不多赘述了，感兴趣的自己可以尝试下"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 输出.pdb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Chem.MolToPDBBlock()\n",
    "Chem.MolToPDBFile()\n",
    "Chem.PDBWriter()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 输出.fasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Chem.MolToFASTA()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 输出.xyz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Chem.MolToXYZBlock()\n",
    "Chem.MolToXYZFile()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 分子可视化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 单个展示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从mol对象到图片：MolToImage(mol, size, kekulize, wedgeBonds, fitImage, ...)<br />\n",
    "mol：mol对象<br />\n",
    "size：图片尺寸，默认(300, 300)<br />\n",
    "kekulize：是否展示kekule形式，默认True（True：芳香系统用实线表示，False：虚线表示）<br />\n",
    "wedgeBonds：是否展示楔形键，即立体构型，默认True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=150x150 at 0xA795F08>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mol = Chem.MolFromSmiles('C[C@H](O)c1ccccc1')\n",
    "Draw.MolToImage(mol, size=(150,150), kekulize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 在新窗口中展示图片：ShowMol()<br />\n",
    "参数基本同上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Draw.ShowMol(mol, size=(150,150), kekulize=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 保存图片MolToFile(mol, filename, size, kekulize, wedgeBonds, ...)<br />\n",
    "参数基本同上"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Draw.MolToFile(mol, 'data/output.png', size=(150, 150))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 批量展示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从DataFrame中展示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从df中展示：FrameToGridImage(frame, column, molsPerRow, subImgSize, legendsCol, ...)<br />\n",
    "frame：DataFrame对象<br />\n",
    "column：rdkit mol对象所在列<br />\n",
    "molsPerRow,：每行显示的分子数<br />\n",
    "subImgSize：图片大小<br />\n",
    "legendsCol：标题所在列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x200 at 0xA079788>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/smiles_df.csv')\n",
    "PandasTools.AddMoleculeColumnToFrame(df,'SMILES','mol',includeFingerprints=True)\n",
    "img = PandasTools.FrameToGridImage(df, column='mol', molsPerRow=5, subImgSize=(200,200), legendsCol='Name')\n",
    "img"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 保存"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "img.save('data/df.png')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从mol列表中展示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 从列表生成分子结构：MolsToGridImage(mols, maxMols, molsPerRow, subImgSize, legends, ...)<br />\n",
    "部分参数和上面的一致<br />\n",
    "mols：mol对象列表<br />\n",
    "maxMols：最多显示的分子数 <br />\n",
    "molsPerRow,：每行显示的分子数<br />\n",
    "subImgSize：图片大小<br />\n",
    "legends：标题<br />"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=600x300 at 0xA077E08>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "warnings.filterwarnings(\"ignore\")\n",
    "mols = df.mol.tolist()\n",
    "legends = df.Name.tolist()\n",
    "img = Draw.MolsToGridImage(mols, maxMols=2, molsPerRow=2, subImgSize=(300,300), legends=legends)  \n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img.save('data/mols.png')  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3D展示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 转换3D时，为了得到靠谱的三维构象，一般先加氢：AddHs(mol)\n",
    "* 通过距离几何算法计算3D坐标：EmbedMolecule(mol, randomSeed, ...)<br />\n",
    "mol：mol对象<br />\n",
    "randomSeed：随机种子\n",
    "\n",
    "* 转换完后再进行一步力场优化，比如MMFF94：MMFFOptimizeMolecule(mol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3d = Chem.MolFromSmiles('CNC(=O)N(N(CCCl)S(C)(=O)=O)S(C)(=O)=O')\n",
    "m3d = Chem.AddHs(m3d)\n",
    "AllChem.EmbedMolecule(m3d, randomSeed=3)\n",
    "AllChem.MMFFOptimizeMolecule(m3d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=250x250 at 0x92FC188>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Draw.MolToImage(m3d, size=(250,250))"
   ]
  }
 ],
 "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": true
 },
 "nbformat": 4,
 "nbformat_minor": 4
}