DeleteSubstructs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    from cresset import flare\n",
    "except ImportError:\n",
    "    flare=None\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem import AllChem, rdmolfiles, rdFMCS\n",
    "from rdkit.Chem.Draw import IPythonConsole"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def DeleteSubstructs(mol, submol):\n",
    "    matches = mol.GetSubstructMatches(submol)\n",
    "    res = []\n",
    "    for match in matches:\n",
    "        match = [m for m in match if mol.GetAtomWithIdx(m).GetAtomicNum() > 1]\n",
    "        exp_hs_to_add = []\n",
    "        indices_to_remove = set()\n",
    "        bonds_to_remove = set()\n",
    "        mol_copy = Chem.Mol(mol)\n",
    "        for b in mol_copy.GetBonds():\n",
    "            is_ba_in_match = (b.GetBeginAtomIdx() in match)\n",
    "            is_ea_in_match = (b.GetEndAtomIdx() in match)\n",
    "            if (is_ba_in_match or is_ea_in_match):\n",
    "                bonds_to_remove.add((b.GetBeginAtomIdx(), b.GetEndAtomIdx()))\n",
    "            if ((b.GetBeginAtom().GetAtomicNum() == 1 and is_ea_in_match)\n",
    "                or (b.GetEndAtom().GetAtomicNum() == 1 and is_ba_in_match)):\n",
    "                indices_to_remove.add(b.GetBeginAtomIdx() if is_ea_in_match\n",
    "                                      else b.GetEndAtomIdx())\n",
    "                continue\n",
    "            if ((is_ba_in_match and (not is_ea_in_match))\n",
    "                or (is_ea_in_match and (not is_ba_in_match))):\n",
    "                if (is_ba_in_match):\n",
    "                    a = b.GetEndAtom() if is_ba_in_match else b.GetBeginAtom()\n",
    "                try:\n",
    "                    exp_h_add = a.GetIntProp('__exp_h_add')\n",
    "                except KeyError:\n",
    "                    exp_h_add = 0\n",
    "                exp_h_add += 1\n",
    "                a.SetIntProp('__exp_h_add', exp_h_add)\n",
    "        indices_to_remove_sorted = sorted(indices_to_remove.union(match),\n",
    "                                          reverse=True)\n",
    "        rwmol = Chem.RWMol(mol_copy)\n",
    "        [rwmol.RemoveBond(ba, ea) for (ba, ea) in bonds_to_remove]\n",
    "        [rwmol.RemoveAtom(i) for i in indices_to_remove_sorted]\n",
    "        for a in rwmol.GetAtoms():\n",
    "            try:\n",
    "                exp_h_add = a.GetIntProp('__exp_h_add')\n",
    "            except KeyError:\n",
    "                continue\n",
    "            a.SetNumExplicitHs(a.GetNumExplicitHs() + exp_h_add)\n",
    "        mol_copy = Chem.AddHs(rwmol,\n",
    "                              addCoords=(mol.GetNumConformers() > 0),\n",
    "                              explicitOnly=True)\n",
    "        Chem.SanitizeMol(mol_copy)\n",
    "        mol_copy.ClearComputedProps()\n",
    "        mol_copy.UpdatePropertyCache()\n",
    "        res.append(mol_copy)\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load molecules:\n",
    "lig02_pdb = open(\"/home/paolo/support/JenkeScheen/mcs/02.pdb\", \"r\").read()\n",
    "lig12_pdb = open(\"/home/paolo/support/JenkeScheen/mcs/12.pdb\", \"r\").read()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "if flare:\n",
    "    ligands = flare.main_window().project.ligands\n",
    "    lig02 = ligands.extend(flare.read_string(lig02_pdb, \"pdb\"))[-1]\n",
    "    lig12 = ligands.extend(flare.read_string(lig12_pdb, \"pdb\"))[-1]\n",
    "    lig02_mol = lig02.to_rdmol()\n",
    "    lig12_mol = lig12.to_rdmol()\n",
    "else:\n",
    "    lig02_mol = rdmolfiles.MolFromPDBBlock(lig02_pdb)\n",
    "    lig12_mol = rdmolfiles.MolFromPDBBlock(lig12_pdb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x14a8c18d40d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lig02_depict = Chem.RemoveHs(lig02_mol)\n",
    "AllChem.Compute2DCoords(lig02_depict)\n",
    "lig02_depict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x14a8f8127990>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lig12_depict = Chem.RemoveHs(lig12_mol)\n",
    "AllChem.Compute2DCoords(lig12_depict)\n",
    "lig12_depict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make list of molecules to map the MCS to:\n",
    "perturbation_pair = []\n",
    "perturbation_pair.append(lig02_mol)\n",
    "perturbation_pair.append(lig12_mol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "MCS_object = rdFMCS.FindMCS(perturbation_pair, completeRingsOnly=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "MCS_SMARTS = Chem.MolFromSmarts(MCS_object.smartsString)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove MCS from each molecule:\n",
    "lig02_stripped = DeleteSubstructs(lig02_mol, MCS_SMARTS)\n",
    "lig12_stripped = DeleteSubstructs(lig12_mol, MCS_SMARTS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lig02: ['']\n",
      "lig12: ['[H]Cl', '[H]Cl']\n"
     ]
    }
   ],
   "source": [
    "# print SMILES of each stripped molecule:\n",
    "print(\"lig02: \" + str([Chem.MolToSmiles(m) for m in lig02_stripped]))\n",
    "print(\"lig12: \" + str([Chem.MolToSmiles(m) for m in lig12_stripped]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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",
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