RC1 - Handle_Molecular_Data_2309.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basics For Molecular Data Handling\n",
    "\n",
    "1. S. C. Teguia Kouam, steve.teguia@facsciences-uy1.cm\n",
    "    * Department of Physics, Faculty of Science, University of Douala\n",
    "1. J-P. Tchapet Njafa, jean-pierre.tchapet@facsciences-uy1.cm\n",
    "    * Department of Physics, Faculty of Science, University of Maroua\n",
    "1. S. G. Nana Engo, serge.nana-engo@facsciences-uy1.cm\n",
    "    * Department of Physics, Faculty of Science, University of Yaounde I\n",
    "       \n",
    "*September 2023*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This Tutorial aims to aid in the process of handling molecular data to be used for **cheminformatics works**. For this purpose, as first step, we will learn how to deal with molecular **SMILES** (Simplified Molecular Input Line Entry System), being one of the most common practice to represent molecules as raw input for computer algorithms.\n",
    "\n",
    "## Download Our DataBase\n",
    "\n",
    "To move forward, we suggest downloading first a dataset containing molecular files. We choose an arbitrary dataset from the `Zenodo` repository (https://zenodo.org/record/4641960/files/Diversity.zip?download=1). This dataset is composed of several molecular files (with `.txt` extensions) each of them containing a list of SMILES.\n",
    "\n",
    "\n",
    "## Read Molecular Files "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# path to the current work directory\n",
    "main_path = os.getcwd()\n",
    "# path to the database\n",
    "#path_to_db = os.path.join(main_path + '/../Datasets/Diversity/diversity/')\n",
    "# path to a specific molecular file: first file containing 1000 SMILES\n",
    "mol_file = main_path  + '/chembl_24.1_smiles-n=1000-min_tc=0.0-sample_idx=1.txt'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. First Method**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(mol_file, 'r') as file:\n",
    "    file1 = file.read().split('\\n')\n",
    "\n",
    "type(file1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Select the first molecular smiles of the file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CCC[N+](CC#Cc1ccccc1)(CCC)CC(=O)c1ccc(Cl)cc1'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smiles1 = file1[0]\n",
    "smiles1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Second Method**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<_io.TextIOWrapper name='/home/mvoto/Téléchargements/RC1/chembl_24.1_smiles-n=1000-min_tc=0.0-sample_idx=1.txt' mode='r' encoding='utf-8'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# open the file while changing the encoding to 'utf-8'\n",
    "file2 = open(mol_file, encoding='utf-8')\n",
    "\n",
    "file2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the lines of the molecular file\n",
    "lin = file2.readlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(lin)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Select the first molecular smiles of the file**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CCC[N+](CC#Cc1ccccc1)(CCC)CC(=O)c1ccc(Cl)cc1'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smiles1 = lin[0].split()[0]\n",
    "smiles1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create Molecular Objects\n",
    "\n",
    "### **Using `OpenBabel`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries \n",
    "from openbabel import pybel as pb  \n",
    "from openbabel import openbabel as ob "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**create an `openbabel` molecular object**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "<text x=\"22.000000\" y=\"186.564065\" fill=\"rgb(30,239,30)\" stroke-width=\"0\" font-weight=\"bold\" font-size=\"16\">Cl</text>\n",
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       "<openbabel.pybel.Molecule at 0x7fc7806a3e20>"
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    }
   ],
   "source": [
    "# create an openbabel molecular object\n",
    "obmol = pb.readstring('smiles', smiles1)\n",
    "obmol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Add Hydrogen atoms to our molecule for 3D**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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       "<openbabel.pybel.Molecule at 0x7fc7806a3e20>"
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# add Hs to our Molecule\n",
    "obmol.addh()\n",
    "obmol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Generate 3D Structure-Coordinates of every atoms in the molecule**"
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  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "<openbabel.pybel.Molecule at 0x7fc7806a3e20>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate 3D structure \n",
    "obmol.make3D()\n",
    "obmol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6, 6, 6, 7, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 8, 6, 6, 6, 6, 17, 6, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
      "[[0.9823441738856182, -0.39515392753980394, 0.21392883680873484], [0.42073598683371144, 0.6115247151660266, 1.2055389927494309], [0.835746677177922, 2.0424374102806944, 1.0129978166761917], [0.33172794941638273, 2.6282980723781004, -0.314013092889948], [-1.199380131499873, 2.6583692087240016, -0.42407901612053994], [-1.7664316133781197, 4.055935661369218, -0.4595048246932311], [-2.0383675022229903, 5.197632854883514, -0.5370266515120291], [-2.472095922603632, 6.555190619220443, -0.7952375540286765], [-3.641271068837568, 6.660318828039775, -1.5408892048019993], [-4.1780623065292914, 7.907533008114493, -1.822959813878859], [-3.538105037435856, 9.054461862664965, -1.3613577914937143], [-2.3514409173138224, 8.954796090838181, -0.6302588882419004], [-1.8057074006561316, 7.6969666187667105, -0.3470745099777672], [0.6339849160011167, 4.048575571465508, -0.42021953172377535], [2.0193306157399675, 3.983597650192742, -0.40449476234382153], [2.3074205739325615, 3.9460796032195504, -2.255808998036636], [1.15204976066242, 1.9045149397886425, -1.480458932340794], [3.000369667739626, 1.7309386509624223, -1.2522289656936016], [3.421312145490815, 0.5731988648785499, -1.4018974783514078], [4.2808379718393565, 3.0737161576921963, -1.1955842984828062], [4.162488220760212, 4.618448120101636, -0.6924954285580699], [4.589577787904523, 5.863044720950114, -1.1246189672383253], [5.305389117020196, 5.710634616221792, -2.250879025935331], [5.898072747595283, 7.205486799406893, -2.821545340235051], [5.569516660741935, 4.384433458737383, -2.821976641459946], [5.0709359619469225, 3.0750922692528575, -2.340094702745495], [0.5733793287175694, -0.3265981899011159, -0.7834807674747398], [0.695075429384676, -1.4069466170939842, 0.4648595809885078], [2.0702497555282284, -0.3334142512817787, 0.17154306318624424], [-0.6606866684746353, 0.5609188440053459, 1.215781268030774], [0.8097152590672154, 0.3349060908146824, 2.1866735615857142], [0.5441303984299207, 2.747024209912331, 1.8008832766722547], [1.92756166324003, 1.9835975119236344, 1.0471194537709547], [-1.5754300265767545, 2.198242329944813, 0.47990371568722284], [-1.4592372045157331, 2.1306983373073547, -1.3343589268427314], [-4.142807872479201, 5.762488857945999, -1.8940692239627583], [-5.097958037022928, 7.975810876814774, -2.395142795068644], [-3.970919735726898, 10.029089795397613, -1.5735067566734406], [-1.8647641242928188, 9.862958544256136, -0.27866809092551253], [-0.8834818256308373, 7.644663540915172, 0.22602294604389697], [0.38693032631211866, 4.483459483789737, -1.3972493269180544], [0.30192356331821696, 4.687236394407415, 0.4020540677275939], [2.1776176830077882, 4.84035813659421, 0.3182070618874843], [2.624997905828669, 3.32497648291962, 0.2780874390515077], [1.681816175179273, 4.478954072618371, -2.944173040781187], [3.2244227596423, 4.361802234289863, -2.5947201067269603], [2.45656273778112, 2.9530656472950083, -2.7432104478632993], [0.5991285994798458, 1.0112382594257243, -1.7419831363276865], [0.8313358984726613, 2.3992531858773147, -2.411114177807454], [3.1985118085066824, 5.193664455227312, -0.8049468300288193], [3.9704223228411695, 6.781563962167488, -0.8850638617278244], [6.151247328569861, 4.3976992587634784, -3.736600948014445], [5.051183484995748, 2.211111486011313, -3.0226785289970004]]\n"
     ]
    }
   ],
   "source": [
    "ob_mol = obmol.OBMol\n",
    "sp = []\n",
    "coords = []\n",
    "for atom in ob.OBMolAtomIter(ob_mol):\n",
    "    sp.append(atom.GetAtomicNum())\n",
    "    coords.append([atom.GetX(), atom.GetY(), atom.GetZ()])\n",
    "\n",
    "print(sp)\n",
    "print(coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ob_mol.NumAtoms()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Using `RDKit`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit import Chem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**create `RDKit` molecular object**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7fc7559f4f20>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdmol = Chem.MolFromSmiles(smiles1)\n",
    "rdmol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Add explicit Hydrogen atoms to our molecule for 3D generation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7fc7559f5000>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdmol = Chem.AddHs(rdmol)\n",
    "rdmol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the number of atoms in the molecule\n",
    "rdmol.GetNumAtoms()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Try to generate `conformers` and embed them to the 3D structure**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7fc7559f5000>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confid = AllChem.EmbedMolecule(rdmol)\n",
    "rdmol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# returns the id of the generated conformer,\n",
    "# and -1 if no conformers were generated\n",
    "confid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Generate 3D atomic coordinates of the molecule**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C -1.028131340943831 -4.282208908827857 -1.0468566473063163\n",
      "C -1.876558689353685 -3.1692416350768453 -1.5336271995584714\n",
      "C -1.9817449146341122 -1.9504568061158376 -0.6825408716546919\n",
      "N -0.8307965073688657 -1.196767238145799 -0.4148037366059319\n",
      "C 0.030205696216546906 -1.2952343388875707 -1.635501050105182\n",
      "C 1.3051482138689026 -0.5880404866040065 -1.3400114761416488\n",
      "C 2.330814767868759 0.0005253065201858681 -1.1065672460515559\n",
      "C 3.534602594765519 0.7178800959336887 -0.8334682228604449\n",
      "C 4.5290528965083245 0.15086822776749007 -0.0627046454578239\n",
      "C 5.692041852807918 0.8378264832427833 0.20513018635427113\n",
      "C 5.891963441481194 2.1153023088557013 -0.2928947161638436\n",
      "C 4.911069224296034 2.694019058507135 -1.062295541822523\n",
      "C 3.758175978429987 1.9928577039794149 -1.3183513946928802\n",
      "C 0.015186221485924686 -1.485808357469353 0.6621916149041327\n",
      "C -0.4669426165834004 -2.0644865714041347 1.91679998354642\n",
      "C 0.7173429081579362 -2.2748249722655274 2.885899744954497\n",
      "C -1.1668791943063983 0.22283561313515549 -0.422895574992899\n",
      "C -2.3067451203141993 0.6278478018875142 0.36652290450302616\n",
      "O -3.0045353508820423 -0.1710950540750251 1.0141555228398682\n",
      "C -2.68375877818043 2.0400032117504034 0.42658251340379205\n",
      "C -1.9718257569227793 3.01044299880629 -0.26317061076914805\n",
      "C -2.346571040902971 4.334157807145069 -0.19228808067054\n",
      "C -3.4270855119346804 4.756339285314595 0.5527576072166842\n",
      "Cl -3.878164015073642 6.458603409624592 0.6189749832031777\n",
      "C -4.136769765845988 3.7941141116717167 1.2395803983559865\n",
      "C -3.754889156195203 2.4718922994627404 1.1630431141327295\n",
      "H -0.30993433190165975 -4.6609840054624 -1.8394081875364152\n",
      "H -1.6518730557856338 -5.188597599005832 -0.8274410333940676\n",
      "H -0.3954554483940317 -4.02575311002023 -0.19293898923514977\n",
      "H -2.9242539703661414 -3.578966234622283 -1.6614517480033208\n",
      "H -1.5692287227107868 -2.949091562442029 -2.5793734350338147\n",
      "H -2.5756372583083422 -2.23747839682422 0.22451535617411333\n",
      "H -2.703779695667287 -1.2738918989566437 -1.2379658934712996\n",
      "H 0.25895756008846754 -2.3589522642680776 -1.7531472385221527\n",
      "H -0.4396772198858097 -0.7972937616844468 -2.4784069431718447\n",
      "H 4.3798988407111965 -0.8556101415203271 0.3340063983910172\n",
      "H 6.480494167393944 0.40326917862198086 0.8089894150920068\n",
      "H 6.820829220391727 2.6276390461203856 -0.06162562963655928\n",
      "H 5.098883620914156 3.7018224081629825 -1.4400488928094628\n",
      "H 2.9891102487726884 2.446174351001524 -1.9212395978859491\n",
      "H 0.5429133101211199 -0.5153102221841861 0.9433310448367184\n",
      "H 0.9008523652671948 -2.1044938907239548 0.32341539605286374\n",
      "H -0.9937436485709533 -3.009030316051444 1.8892123447708837\n",
      "H -1.1211326083946864 -1.3672428476064955 2.5249092560525837\n",
      "H 0.512617610733874 -3.0613208618865118 3.6080226764872796\n",
      "H 0.9477834718335896 -1.3340314259514032 3.4236728185135967\n",
      "H 1.612605289437973 -2.55684150615973 2.3057023654100237\n",
      "H -0.26881731216740157 0.8151033110477298 -0.11227452767263806\n",
      "H -1.2818669214603364 0.49486571612119823 -1.5194119761202511\n",
      "H -1.1129316296485148 2.753451392615593 -0.8681228807968536\n",
      "H -1.7576998068757783 5.068382872131197 -0.7509172792373686\n",
      "H -4.974464665153224 4.095473940650879 1.82194390693828\n",
      "H -4.318655446819767 1.721356474164767 1.7067363232055117\n"
     ]
    }
   ],
   "source": [
    "for i, atom in enumerate(rdmol.GetAtoms()):\n",
    "        positions = rdmol.GetConformer().GetAtomPosition(i)\n",
    "        print(atom.GetSymbol(), positions.x, positions.y, positions.z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`Optimze` the molecular strcuture with `MMFF94` ForceField**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7fc7559f5000>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AllChem.MMFFOptimizeMolecule(rdmol)\n",
    "rdmol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Generate 3D atomic coordinates of the optimized structure**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C -0.6104183331212564 3.384529259856784 1.9005861405112794\n",
      "C 0.31266999264687323 2.4435982570995085 1.1368872206582126\n",
      "C -0.38616842266309725 1.1102161748340444 0.8611377422631076\n",
      "N 0.45037323384349787 0.12486650001241581 0.006294179926529202\n",
      "C -0.30436871198916776 -1.2422932165526581 -0.13076280098828977\n",
      "C -1.7545758718264663 -1.097169261664286 -0.2700574178270318\n",
      "C -2.94583154808634 -0.9887441641564582 -0.38350011781695037\n",
      "C -4.361439655276625 -0.840282075847442 -0.5210809681703382\n",
      "C -4.892969861392241 -0.24693179943564525 -1.6687920288087645\n",
      "C -6.2747400735899905 -0.09580278804227071 -1.7999027370607983\n",
      "C -7.124307561362467 -0.536910830086628 -0.7858345398930605\n",
      "C -6.595062122467409 -1.1307033845628232 0.35961583901074634\n",
      "C -5.2136272657781655 -1.2830811199386234 0.49323643978743725\n",
      "C 0.810358502415507 0.6753919851546742 -1.39325853724551\n",
      "C -0.38551371341691987 1.1484630270068124 -2.2181279344479177\n",
      "C 0.0761648946627231 1.712904958127379 -3.555962902548493\n",
      "C 1.8098463898051198 -0.16684162264829128 0.7810026892588606\n",
      "C 2.705831558665493 -1.273713425814298 0.22755736770513516\n",
      "O 2.373868902811205 -1.9607188427542115 -0.7355957505002236\n",
      "C 4.023384286965379 -1.5270976487118897 0.8884523326006354\n",
      "C 4.447174103134606 -0.8250426029019993 2.025477985549045\n",
      "C 5.693069417996632 -1.1079523217987186 2.59915008807384\n",
      "C 6.515085964105509 -2.087312607151569 2.0437045560845157\n",
      "Cl 8.041289683598674 -2.4256242842320335 2.7558077913218524\n",
      "C 6.102848846087358 -2.7907414183450583 0.9160539658712429\n",
      "C 4.860067063793462 -2.5120945122495577 0.3396793103344072\n",
      "H -1.5151758364350343 3.6006227665324935 1.323347832845579\n",
      "H -0.10209908030832424 4.332686571159273 2.1021756937010627\n",
      "H -0.9091070173775836 2.94829557293591 2.859184488249538\n",
      "H 1.2150323922251223 2.2971458503344877 1.7381569030769242\n",
      "H 0.6092716660373112 2.9342638621538244 0.20507113979676253\n",
      "H -0.6081202392891984 0.5961278472195831 1.8044598581812683\n",
      "H -1.3292398242979169 1.2978512654607022 0.34130971148383915\n",
      "H -0.08300694441595396 -1.8175566447329328 0.774924412960129\n",
      "H 0.09507147930860296 -1.7661940354160686 -1.002360531371242\n",
      "H -4.243917414317078 0.09697754846390506 -2.469875882705821\n",
      "H -6.6916235990110176 0.36205251969782076 -2.6939863551248013\n",
      "H -8.201037377080366 -0.42183506678343186 -0.8902582492448006\n",
      "H -7.261045322777683 -1.4780200685803153 1.1462135040194352\n",
      "H -4.816475918026742 -1.752241143600967 1.3897063153461704\n",
      "H 1.515130800061451 1.499875115228944 -1.239332512094391\n",
      "H 1.3430651471155322 -0.11263359624230361 -1.9340175841455869\n",
      "H -1.0711636593452485 0.3218991016917289 -2.421444784906049\n",
      "H -0.9452177532531904 1.9302751192296466 -1.6967788344525794\n",
      "H 0.7401556652833616 2.5711817941801454 -3.411327752470717\n",
      "H -0.7851498567688749 2.0451782309324398 -4.144024451234516\n",
      "H 0.612619046300384 0.9560328940531895 -4.13717539208227\n",
      "H 1.517677919618401 -0.4223572932572619 1.8051754002849558\n",
      "H 2.3744328372072943 0.7694915575785293 0.7580969789068835\n",
      "H 3.846633009832789 -0.05553390103187913 2.500513092915549\n",
      "H 6.021583051087353 -0.5627056322217304 3.482407046628154\n",
      "H 6.741929641205247 -3.5564910727553576 0.4807990338779718\n",
      "H 4.556767487866819 -3.0753013974236496 -0.5427269960923009\n"
     ]
    }
   ],
   "source": [
    "for i, atom in enumerate(rdmol.GetAtoms()):\n",
    "        positions = rdmol.GetConformer().GetAtomPosition(i)\n",
    "        print(atom.GetSymbol(), positions.x, positions.y, positions.z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save molecular structures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`.xyz` File**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "Chem.MolToMolFile(rdmol, 'RC1_rdmol.xyz')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`.sdf` File**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "Chem.MolToMolFile(rdmol, 'RC1_rdmol.sdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**`.mol` File**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "Chem.MolToMolFile(rdmol, 'RC1_rdmol.mol')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "The molecular files generated above only contain information on _spatial atomic coordinates_ and _bond connectivities_. However they are capable of carrying more information than that, such as molecular properties (_internal energy_, _enthalpy_, etc.).\n",
    "\n",
    "We are interested in the fact that all the information contained in each molecular file can be used to infer insights of significant importance when dealing with __structure-property relationships__. Indeed, all of this information leads to several categories of molecular representations which can be useful, each with a _specific level of content_, to molecular description. \n",
    "\n",
    "A deep understanding of quantitative structure-property relationships (QSPR) finds applications in real current global challenges such as **materials discovery**, **drug discovery processes**, **chemical property predictions**, etc.\n",
    "\n",
    "## Molecular Descriptors - Dimensional Representations\n",
    "\n",
    "Molecular descriptors can be classified in two main categories:\n",
    "\n",
    "1. **Experimental descriptors**: Physicochemical properties from __numerical simualtions__ or __experimental measurements__\n",
    "\n",
    "2. **Theoretical descriptors**: Obtained from __symbolic representations__ of the molecules, such as structural formula, empirical formula, or `SMILES`. \n",
    "\n",
    "This category can be divided in **five** classes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](MolecularDimension.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. **0D descritors**. Related to `Composition` discriptors, do not provide any information about the molecular structure or connectivity of atoms.\n",
    "\n",
    "1. **1D descritors**. Related to sets of substructures such as functional groups.\n",
    "\n",
    "1. **2D descritors**. Features providing information on molecular topology based on the graph representation of the molecules.\n",
    "\n",
    "1. **3D descritors**. Geometrical features providing information about spatial coordinates of atoms of a molecule.\n",
    "\n",
    "1. **4D descritors**. Also known as **grid-based descriptors**, introduce a fourth dimension characterizing the interactions between the molecules and the active sites of a receptor or the multiple conformational states of the molecules. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# `Tartarus` Basic Usage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this first session, we will introduce the basics of `Tartarus`: A benchmark platform for practical inverse molecular design. To this end, after the installation task of the module `tartarus`, we will look at its framework. \n",
    "\n",
    "![](tartarusoverview.png)\n",
    "\n",
    "## In Practice\n",
    "\n",
    "**1. Design of `OPVs` - Framework**\n",
    "\n",
    "![](opv.png)\n",
    "\n",
    "**2. Design of `Organic Emitters` - Framework**\n",
    "\n",
    "![](emitters.png)\n",
    "\n",
    "**3. Design of `Protein Ligands` - Framework**\n",
    "\n",
    "![](docking.png)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practical Coding For `OPV` Design - Use `Tartarus` As A Blackbox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1. Load the data used for this purpose into the original work**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# path to the hce.csv data \n",
    "path_to_hce = os.path.join(main_path + '/hce.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>smiles</th>\n",
       "      <th>pce_pcbm_sas</th>\n",
       "      <th>pce_pcdtbt_sas</th>\n",
       "      <th>pce_1</th>\n",
       "      <th>pce_2</th>\n",
       "      <th>sas</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c1sc(-c2[SiH2]c(cc2)-c2ccc(-c3scc4occc34)c3csc...</td>\n",
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       "      <td>c1cc2sc3cc4ccc5cscc5c4cc3c2c2cCcc12</td>\n",
       "      <td>-0.767370</td>\n",
       "      <td>-3.361486</td>\n",
       "      <td>2.594116</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.361486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[nH]1c2cc(-c3ccc[se]3)c3c[nH]cc3c2c2c1c1[se]cc...</td>\n",
       "      <td>-3.572659</td>\n",
       "      <td>-4.618362</td>\n",
       "      <td>1.045703</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.618362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[SiH2]1C=CC=C1c1cc2sc3c4[se]ccc4c4cocc4c3c2c2c...</td>\n",
       "      <td>-1.675279</td>\n",
       "      <td>-4.853093</td>\n",
       "      <td>3.177815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.853093</td>\n",
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       "      <td>[SiH2]1C=CC=C1C1=Cc2cc3ccc4ccc5c[nH]cc5c4c3cc2...</td>\n",
       "      <td>-2.797039</td>\n",
       "      <td>-4.418456</td>\n",
       "      <td>1.621417</td>\n",
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       "      <td>4.418456</td>\n",
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       "    <tr>\n",
       "      <th>24949</th>\n",
       "      <td>c1cc2ccc3cc4cc(-c5ncncn5)c5=C[SiH2]C=c5c4cc3c2...</td>\n",
       "      <td>1.610517</td>\n",
       "      <td>-3.828723</td>\n",
       "      <td>5.439241</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.828723</td>\n",
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       "    <tr>\n",
       "      <th>24950</th>\n",
       "      <td>c1sc(c2C=CCc12)-c1cc2ncc3c4ccccc4c4ccccc4c3c2cn1</td>\n",
       "      <td>-0.239583</td>\n",
       "      <td>-2.952342</td>\n",
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       "    <tr>\n",
       "      <th>24951</th>\n",
       "      <td>c1sc(-c2ccc(s2)-c2Cc(cc2)-c2cncc3nsnc23)c2ccsc12</td>\n",
       "      <td>4.575720</td>\n",
       "      <td>-3.706835</td>\n",
       "      <td>8.282555</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.706835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24952</th>\n",
       "      <td>c1[SiH2]c(cc1)-c1sc(-c2sc(-c3cncc4nsnc34)c3[nH...</td>\n",
       "      <td>0.892807</td>\n",
       "      <td>-4.533846</td>\n",
       "      <td>5.426653</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.533846</td>\n",
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       "<p>24953 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  smiles  pce_pcbm_sas  \\\n",
       "0      c1sc(-c2[SiH2]c(cc2)-c2ccc(-c3scc4occc34)c3csc...     -1.061257   \n",
       "1      c1ccc(C2=Cc3oc4c(c3C2)c2c[nH]cc2c2ccc3=CCC=c3c...     -3.188265   \n",
       "2                    c1cc2sc3cc4ccc5cscc5c4cc3c2c2cCcc12     -0.767370   \n",
       "3      [nH]1c2cc(-c3ccc[se]3)c3c[nH]cc3c2c2c1c1[se]cc...     -3.572659   \n",
       "4      [SiH2]1C=CC=C1c1cc2sc3c4[se]ccc4c4cocc4c3c2c2c...     -1.675279   \n",
       "...                                                  ...           ...   \n",
       "24948  [SiH2]1C=CC=C1C1=Cc2cc3ccc4ccc5c[nH]cc5c4c3cc2...     -2.797039   \n",
       "24949  c1cc2ccc3cc4cc(-c5ncncn5)c5=C[SiH2]C=c5c4cc3c2...      1.610517   \n",
       "24950   c1sc(c2C=CCc12)-c1cc2ncc3c4ccccc4c4ccccc4c3c2cn1     -0.239583   \n",
       "24951   c1sc(-c2ccc(s2)-c2Cc(cc2)-c2cncc3nsnc23)c2ccsc12      4.575720   \n",
       "24952  c1[SiH2]c(cc1)-c1sc(-c2sc(-c3cncc4nsnc34)c3[nH...      0.892807   \n",
       "\n",
       "       pce_pcdtbt_sas     pce_1  pce_2       sas  \n",
       "0           -4.382105  3.320848    0.0  4.382105  \n",
       "1           -3.607924  0.419659    0.0  3.607924  \n",
       "2           -3.361486  2.594116    0.0  3.361486  \n",
       "3           -4.618362  1.045703    0.0  4.618362  \n",
       "4           -4.853093  3.177815    0.0  4.853093  \n",
       "...               ...       ...    ...       ...  \n",
       "24948       -4.418456  1.621417    0.0  4.418456  \n",
       "24949       -3.828723  5.439241    0.0  3.828723  \n",
       "24950       -2.952342  2.712759    0.0  2.952342  \n",
       "24951       -3.706835  8.282555    0.0  3.706835  \n",
       "24952       -4.533846  5.426653    0.0  4.533846  \n",
       "\n",
       "[24953 rows x 6 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the .csv file as dataframe\n",
    "import pandas as pd\n",
    "hce = pd.read_csv(path_to_hce)\n",
    "hce"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2. Use the `SMILES` from the diversity data loaded above to run `Tartarus`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creat an empty dictionary\n",
    "from collections import defaultdict\n",
    "data = defaultdict(list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Get a database similar to the one from the original work**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smiles</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CCC[N+](CC#Cc1ccccc1)(CCC)CC(=O)c1ccc(Cl)cc1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NC(=S)NN=Cc1ccc(O)cc1O</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         smiles\n",
       "0  CCC[N+](CC#Cc1ccccc1)(CCC)CC(=O)c1ccc(Cl)cc1\n",
       "1                        NC(=S)NN=Cc1ccc(O)cc1O"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(lin)):\n",
    "    smiles = lin[i].split()[0]\n",
    "    if i==2: break\n",
    "    data['smiles'].append(smiles)\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save the dataframe to `.csv` file\n",
    "df.to_csv('my_hce.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "smiles = df['smiles'].tolist()\n",
    "smi = smiles[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install tartarus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install pce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install --upgrade tartarus\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#conda activate tartarus\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#conda list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tartarus in /home/mvoto/anaconda3/lib/python3.10/site-packages (0.2.3)\n",
      "Requirement already satisfied: Click in /home/mvoto/anaconda3/lib/python3.10/site-packages (from tartarus) (8.1.7)\n",
      "Requirement already satisfied: colorama in /home/mvoto/anaconda3/lib/python3.10/site-packages (from tartarus) (0.4.6)\n",
      "Requirement already satisfied: hexrec in /home/mvoto/anaconda3/lib/python3.10/site-packages (from tartarus) (0.3.0)\n",
      "Requirement already satisfied: bytesparse>=0.0.6 in /home/mvoto/anaconda3/lib/python3.10/site-packages (from hexrec->tartarus) (0.0.6)\n",
      "Requirement already satisfied: Deprecated in /home/mvoto/anaconda3/lib/python3.10/site-packages (from hexrec->tartarus) (1.2.14)\n",
      "Requirement already satisfied: wrapt<2,>=1.10 in /home/mvoto/anaconda3/lib/python3.10/site-packages (from Deprecated->hexrec->tartarus) (1.14.1)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install tartarus\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tartarus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pce"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install pce\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mType:\u001b[0m        module\n",
       "\u001b[0;31mString form:\u001b[0m <module 'pce' from '/home/mvoto/anaconda3/lib/python3.10/site-packages/pce/__init__.py'>\n",
       "\u001b[0;31mFile:\u001b[0m        ~/anaconda3/lib/python3.10/site-packages/pce/__init__.py\n",
       "\u001b[0;31mDocstring:\u001b[0m   <no docstring>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pce?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "cannot import name 'pce' from 'tartarus' (/home/mvoto/anaconda3/lib/python3.10/site-packages/tartarus.py)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[62], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m## use full xtb calculation in hce module\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtartarus\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pce\n\u001b[1;32m      3\u001b[0m \u001b[38;5;66;03m#from tartarus import docking\u001b[39;00m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m#from tartarus import tadf\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;66;03m#from tartarus import reactivity\u001b[39;00m\n\u001b[1;32m      7\u001b[0m dipm, gap, lumo, combined, pce_pcbm_sas, pce_pcdtbt_sas \u001b[38;5;241m=\u001b[39m pce\u001b[38;5;241m.\u001b[39mget_properties(smi)\n",
      "\u001b[0;31mImportError\u001b[0m: cannot import name 'pce' from 'tartarus' (/home/mvoto/anaconda3/lib/python3.10/site-packages/tartarus.py)"
     ]
    }
   ],
   "source": [
    "## use full xtb calculation in hce module\n",
    "from tartarus import pce\n",
    "#from tartarus import docking\n",
    "#from tartarus import tadf\n",
    "#from tartarus import reactivity\n",
    "\n",
    "dipm, gap, lumo, combined, pce_pcbm_sas, pce_pcdtbt_sas = pce.get_properties(smi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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