MDL extended parser (Ketcher flavour)

mdl_extended_parser.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "## Ketcher MDL flavor \n",
    "\n",
    "This snippet allow parsing MDL outputed by Ketcher with support for some of its custom atom/group labeling. At the moment, only `Acyclic` and `Cyclic` labels familly are supported "
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "source": [
    "import yaml\n",
    "\n",
    "Ketcher_Generics = \"\"\"{\n",
    "  atom: {\n",
    "    any: {\n",
    "      labels: ['A', 'AH']\n",
    "    },\n",
    "    'no-carbon': {\n",
    "      labels: ['Q', 'QH']\n",
    "    },\n",
    "    metal: {\n",
    "      labels: ['M', 'MH']\n",
    "    },\n",
    "    halogen: {\n",
    "      labels: ['X', 'XH']\n",
    "    }\n",
    "  },\n",
    "  group: {\n",
    "    labels: ['G', 'GH', 'G*', 'GH*'],\n",
    "    acyclic: {\n",
    "      labels: ['ACY', 'ACH'],\n",
    "      carbo: {\n",
    "        labels: ['ABC', 'ABH'],\n",
    "        alkynyl: {\n",
    "          labels: ['AYL', 'AYH']\n",
    "        },\n",
    "        alkyl: {\n",
    "          labels: ['ALK', 'ALH']\n",
    "        },\n",
    "        alkenyl: {\n",
    "          labels: ['AEL', 'AEH']\n",
    "        }\n",
    "      },\n",
    "      hetero: {\n",
    "        labels: ['AHC', 'AHH'],\n",
    "        alkoxy: {\n",
    "          labels: ['AOX', 'AOH']\n",
    "        }\n",
    "      }\n",
    "    },\n",
    "    cyclic: {\n",
    "      labels: ['CYC', 'CYH'],\n",
    "      'no-carbon': {\n",
    "        labels: ['CXX', 'CXH']\n",
    "      },\n",
    "      carbo: {\n",
    "        labels: ['CBC', 'CBH'],\n",
    "        aryl: {\n",
    "          labels: ['ARY', 'ARH']\n",
    "        },\n",
    "        cycloalkyl: {\n",
    "          labels: ['CAL', 'CAH']\n",
    "        },\n",
    "        cycloalkenyl: {\n",
    "          labels: ['CEL', 'CEH']\n",
    "        }\n",
    "      },\n",
    "      hetero: {\n",
    "        labels: ['CHC', 'CHH'],\n",
    "        aryl: {\n",
    "          labels: ['HAR', 'HAH']\n",
    "        }\n",
    "      }\n",
    "    }\n",
    "  },\n",
    "  special: {\n",
    "    labels: ['H+', 'D', 'T', 'R', 'Pol']\n",
    "  }\n",
    "}\"\"\""
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Setting up some example mol string"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "source": [
    "example_1 = \"\"\"\n",
    "  Ketcher 10 42113 32D 1   1.00000     0.00000     0\n",
    "\n",
    " 18 20  0     1  0            999 V2000\n",
    "    0.0000    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "   -0.5878    0.8090    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "   -0.8090   -0.5878    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "   -1.3968    0.2213    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    0.8090   -0.5878    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    0.5000   -1.5388    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "   -0.5000   -1.5388    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "   -0.4314    1.7967    0.0000 R#  0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    3.4543   -0.6389    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    2.5032   -0.9479    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    1.7601   -0.2788    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    1.9680    0.6994    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    2.9191    1.0084    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    3.6622    0.3393    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    2.2953   -1.9261    0.0000 L   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    1.2248    1.3685    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    3.1269    1.9866    0.0000 ACH 0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    4.1614   -1.3460    0.0000 CYC 0  0  0  0  0  0  0  0  0  0  0  0\n",
    "  1  2  1  0  0  0  0\n",
    "  1  3  1  0  0  0  0\n",
    "  3  4  1  0  0  0  0\n",
    "  4  2  1  0  0  0  0\n",
    "  1  5  2  0  0  0  0\n",
    "  5  6  1  0  0  0  0\n",
    "  6  7  2  0  0  0  0\n",
    "  7  3  1  0  0  0  0\n",
    "  2  8  1  0  0  0  0\n",
    "  9 10  1  0  0  0  0\n",
    " 10 11  2  0  0  0  0\n",
    " 11 12  1  0  0  0  0\n",
    " 12 13  2  0  0  0  0\n",
    " 13 14  1  0  0  0  0\n",
    " 14  9  2  0  0  0  0\n",
    " 11  5  1  0  0  0  0\n",
    " 10 15  1  0  0  0  0\n",
    " 12 16  1  0  0  0  0\n",
    " 13 17  1  0  0  0  0\n",
    "  9 18  1  0     0  0\n",
    "M  RGP  1   8   1\n",
    "M  APO  1   4   1\n",
    "M  ALS  15  4 F F   Cl  I   Br \n",
    "M  END\n",
    "\"\"\"\n",
    "\n",
    "example_2 = \"\"\"\n",
    "  Ketcher 10 42115322D 1   1.00000     0.00000     0\n",
    "\n",
    " 17 19  0     1  0            999 V2000\n",
    "    0.0000    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "   -0.5878    0.8090    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "   -0.8090   -0.5878    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "   -1.3968    0.2213    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    0.8090   -0.5878    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    0.5000   -1.5388    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "   -0.5000   -1.5388    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "   -0.4314    1.7967    0.0000 R#  0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    3.4543   -0.6389    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    2.5032   -0.9479    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    1.7601   -0.2788    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    1.9680    0.6994    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    2.8941    1.0084    0.0000 C   0  0  0  2  0  0  0  0  0  0  0  0\n",
    "    3.6622    0.3393    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    1.2248    1.3685    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    4.1614   -1.3460    0.0000 XH  0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    4.6622    0.3393    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "  1  2  1  0  0  0  0\n",
    "  1  3  1  0  0  0  0\n",
    "  3  4  1  0  0  0  0\n",
    "  4  2  1  0  0  0  0\n",
    "  1  5  2  0  0  0  0\n",
    "  5  6  1  0  0  0  0\n",
    "  6  7  2  0  0  0  0\n",
    "  7  3  1  0  0  0  0\n",
    "  2  8  1  0  0  0  0\n",
    "  9 10  1  0  0  0  0\n",
    " 10 11  2  0  0  0  0\n",
    " 11 12  1  0  0  0  0\n",
    " 12 13  2  0  0  0  0\n",
    " 13 14  1  0  0  0  0\n",
    " 14  9  2  0  0  0  0\n",
    " 11  5  1  0  0  0  0\n",
    " 12 15  1  0  0  0  0\n",
    "  9 16  1  0     0  0\n",
    " 14 17  1  0     0  0\n",
    "A   17\n",
    "N,O,S,P\n",
    "M  CHG  1  15   1\n",
    "M  RGP  1   8   1\n",
    "M  APO  1   4   1\n",
    "M  RBC  1  13   2\n",
    "M  SUB  1  13  -1\n",
    "M  END\n",
    "\"\"\"\n",
    "\n",
    "example_3 = \"\"\"\n",
    "Ketcher 10 42114352D 1   1.00000     0.00000     0\n",
    "\n",
    " 16 18  0     1  0            999 V2000\n",
    "    0.0000    0.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "   -0.5878    0.8090    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "   -0.8090   -0.5878    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "   -1.3968    0.2213    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    0.8090   -0.5878    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    0.5000   -1.5388    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "   -0.5000   -1.5388    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "   -0.4314    1.7967    0.0000 R#  0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    3.4543   -0.6389    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    2.5032   -0.9479    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    1.7601   -0.2788    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    1.9680    0.6994    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    2.8941    1.0084    0.0000 C   0  0  0  2  0  0  0  0  0  0  0  0\n",
    "    3.6622    0.3393    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    1.2248    1.3685    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    4.1614   -1.3460    0.0000 L   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "  1  2  1  0  0  0  0\n",
    "  1  3  1  0  0  0  0\n",
    "  3  4  1  0  0  0  0\n",
    "  4  2  1  0  0  0  0\n",
    "  1  5  2  0  0  0  0\n",
    "  5  6  1  0  0  0  0\n",
    "  6  7  2  0  0  0  0\n",
    "  7  3  1  0  0  0  0\n",
    "  2  8  1  0  0  0  0\n",
    "  9 10  1  0  0  0  0\n",
    " 10 11  2  0  0  0  0\n",
    " 11 12  1  0  0  0  0\n",
    " 12 13  2  0  0  0  0\n",
    " 13 14  1  0  0  0  0\n",
    " 14  9  2  0  0  0  0\n",
    " 11  5  1  0  0  0  0\n",
    " 12 15  1  0  0  0  0\n",
    "  9 16  1  0     0  0\n",
    "M  CHG  1  15   1\n",
    "M  RGP  1   8   1\n",
    "M  APO  1   4   1\n",
    "M  RBC  1  13   2\n",
    "M  SUB  1  13  -1\n",
    "M  ALS  16  3 F F   Cl  Br  \n",
    "M  END\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "example_4=\"\"\"\n",
    "  Ketcher 10 52121162D 1   1.00000     0.00000     0\n",
    "\n",
    "  6  5  0     0  0            999 V2000\n",
    "    2.9000    3.6000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    2.9000    4.6000    0.0000 L   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    2.0340    3.1000    0.0000 ACH 0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    3.9000    3.6000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    4.9000    3.6000    0.0000 XH  0  0  0  0  0  0  0  0  0  0  0  0\n",
    "    3.9000    2.6000    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\n",
    "  1  2  1  0     0  0\n",
    "  1  3  1  0     0  0\n",
    "  1  4  1  0     0  0\n",
    "  4  5  1  0     0  0\n",
    "  4  6  2  0     0  0\n",
    "M  ALS   2  2 F O   N  \n",
    "M  END\n",
    "\"\"\""
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## MDL Parser in rdkit"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "source": [
    "from typing import Optional\n",
    "import datamol as dm\n",
    "from typing import Union\n",
    "from typing import TextIO\n",
    "from rdkit import Chem\n",
    "import io\n",
    "import os\n",
    "import fsspec\n",
    "import gzip\n",
    "import re\n",
    "\n",
    "def _process_lines(lines):\n",
    "    \"\"\"Fix common issue in MDL file from epam's ketcher\"\"\"\n",
    "    fixed_lines  = []\n",
    "    additional_lines = []\n",
    "    als_pattern  = re.compile(\"^M\\s+ALS.*\")\n",
    "    end_pattern  = re.compile(\"^M\\s+END\\s*$\")\n",
    "    last_headers = -1\n",
    "    header_pattern = re.compile(\"\\s*V[2,3]000$\")\n",
    "    for i, line in enumerate(lines):\n",
    "        if header_pattern.search(line) is not None:\n",
    "            last_headers = i\n",
    "        if als_pattern.match(line.strip()):\n",
    "            # number of space at the end of the line often wrong\n",
    "            line = line.rstrip()\n",
    "            n_char = len(line.split()[-1])\n",
    "            n_spaces = max(0, 4 - n_char)\n",
    "            line = line + \" \" * n_spaces + \"\\n\"\n",
    "        # the following is lazy\n",
    "        elif ' ACY ' in line:\n",
    "            line = line.replace('ACY', '* ')\n",
    "            atom_idx = i - last_headers\n",
    "            query = \"[!R]\"\n",
    "            additional_lines.append(f\"V{atom_idx:>5} {query}\\n\")\n",
    "        elif ' ACH ' in line:\n",
    "            line = line.replace('ACH', '* ')\n",
    "            atom_idx = i - last_headers\n",
    "            query = \"[!R,#1]\"\n",
    "            additional_lines.append(f\"V{atom_idx:>5} {query}\\n\")\n",
    "\n",
    "        elif ' CYC ' in line:\n",
    "            line = line.replace('CYC', '* ')\n",
    "            atom_idx = i - last_headers\n",
    "            query = \"[R]\"\n",
    "            additional_lines.append(f\"V{atom_idx:>5} {query}\\n\")\n",
    "        elif ' CYH ' in line:\n",
    "            line = line.replace('CYH', '* ')\n",
    "            atom_idx = i - last_headers\n",
    "            query = \"[R,#1]\"\n",
    "            additional_lines.append(f\"V{atom_idx:>5} {query}\\n\")\n",
    "\n",
    "        if end_pattern.match(line.strip()):\n",
    "            fixed_lines.extend(additional_lines)\n",
    "            additional_lines = []\n",
    "\n",
    "        fixed_lines.append(line)    \n",
    "    return \"\".join(fixed_lines)\n",
    "\n",
    "\n",
    "def read_mol_block(mdl_string: str, sanitize: bool = False, show_attachment: bool = False, replace_query_by_atom_val:bool=False):\n",
    "    \"\"\"Read a single mol block from an input string\n",
    "\n",
    "    Args:\n",
    "        mdl_string: input mol block string\n",
    "        sanitize: whether to sanitize the molecules or not\n",
    "        show_attachment: whether to show attachment point or just add the infor to the atom\n",
    "        replace_query_by_atom_val: Whether to replace atom label by full query or keep both when atom values are set\n",
    "    \"\"\"\n",
    "    block = _process_lines([f\"{x}\\n\" for x in mdl_string.splitlines()])\n",
    "    mol = Chem.MolFromMolBlock(\n",
    "        block,\n",
    "        sanitize=sanitize,\n",
    "        strictParsing=False,\n",
    "    )\n",
    "    return update_query(mol, show_attachment=show_attachment, sanitize=sanitize, replace_query_by_atom_val=replace_query_by_atom_val)\n",
    "\n",
    "\n",
    "def read_mdl(\n",
    "    mdl_data: Union[str, os.PathLike, TextIO],\n",
    "    sanitize: bool = False,\n",
    "    show_attachment: bool = False,\n",
    "    as_df: bool = False,\n",
    "    mol_column: Optional[str] = \"mols\",\n",
    "    smiles_column: Optional[str] = \"smiles\",\n",
    "    smarts_column: Optional[str] = \"smarts\",\n",
    "    include_private: bool = False,\n",
    "    include_computed: bool = False,\n",
    "    replace_query_by_atom_val:bool=False\n",
    "):\n",
    "    \"\"\"Read a MDL file\n",
    "\n",
    "    Args:\n",
    "        mdl_data: Mdl input data. Can be multiple molecules\n",
    "        sanitize: whether to sanitize the molecules or not\n",
    "        show_attachment: whether to show attachment point or just add the info to the atom\n",
    "        as_df: whether to return the dataframe or not\n",
    "        mol_column: Name of the molecule column. If not None, rdkit.Chem.PandaTools is used to add a molecule column.\n",
    "        smiles_column: Name of the smiles column. If not None, smiles will not be written\n",
    "        smarts_column: Name of the smarts column. If not None, smarts will not be written\n",
    "        include_private: Include private properties in the columns.\n",
    "        include_computed: Include computed properties in the columns.\n",
    "        replace_query_by_atom_val: Whether to replace atom label by full query or keep both when atom values are set\n",
    "\n",
    "    \"\"\"\n",
    "\n",
    "    # convert str first to File-like object\n",
    "    if isinstance(mdl_data, (str, os.PathLike)) and not dm.utils.fs.exists(mdl_data):\n",
    "        mdl_data = io.BytesIO(mdl_data.encode(\"utf-8\"))\n",
    "\n",
    "    if isinstance(mdl_data, io.IOBase):\n",
    "        content = _process_lines([x.decode() for x in mdl_data.readlines()])\n",
    "        mdl_data = io.BytesIO(content.encode(\"utf-8\"))\n",
    "        supplier = Chem.ForwardSDMolSupplier(\n",
    "            mdl_data,\n",
    "            sanitize=sanitize,\n",
    "            strictParsing=False,\n",
    "        )\n",
    "        mols = list(supplier)\n",
    "\n",
    "    # Regular local or remote paths\n",
    "    else:\n",
    "        with fsspec.open(mdl_data) as f:\n",
    "            # Handle gzip file if needed\n",
    "            if str(mdl_data).endswith(\".gz\") or str(mdl_data).endswith(\".gzip\"):\n",
    "                f = gzip.open(f)\n",
    "\n",
    "            content = _process_lines([x.decode() for x in f.readlines()])\n",
    "            f = io.BytesIO(content.encode(\"utf-8\"))\n",
    "            supplier = Chem.ForwardSDMolSupplier(\n",
    "                f,\n",
    "                sanitize=sanitize,\n",
    "                strictParsing=False,\n",
    "            )\n",
    "            mols = list(supplier)\n",
    "    mols = [\n",
    "        update_query(\n",
    "            mol,\n",
    "            show_attachment=show_attachment,\n",
    "            sanitize=sanitize,\n",
    "            remove_isotopes=True,\n",
    "            smarts_prop=smarts_column,\n",
    "            smiles_prop=smiles_column,\n",
    "            replace_query_by_atom_val=replace_query_by_atom_val\n",
    "        )\n",
    "        for mol in mols\n",
    "    ]\n",
    "    mols = [mol for mol in mols if mol is not None]\n",
    "    if as_df:\n",
    "        return dm.to_df(\n",
    "            mols,\n",
    "            smiles_column=None,\n",
    "            mol_column=mol_column,\n",
    "            include_private=include_private,\n",
    "            include_computed=include_computed,\n",
    "        )  # type: ignore\n",
    "\n",
    "    return mols\n",
    "\n",
    "\n",
    "def update_query(\n",
    "    mol,\n",
    "    show_attachment: bool = False,\n",
    "    sanitize: bool = False,\n",
    "    remove_isotopes: bool = False,\n",
    "    smarts_prop:Optional[str]=\"smarts\",\n",
    "    smiles_prop:Optional[str]=None,\n",
    "    replace_query_by_atom_val: bool = False,\n",
    "):\n",
    "\n",
    "    if mol is None:\n",
    "        return mol\n",
    "\n",
    "    query_atoms = []\n",
    "    rw_mol = Chem.RWMol(mol)\n",
    "    all_atoms = reversed(sorted(rw_mol.GetAtoms(), key=lambda x: x.GetIdx()))\n",
    "    for atom in all_atoms:\n",
    "        prop_dict = atom.GetPropsAsDict()\n",
    "        atom_idx = atom.GetIdx()\n",
    "        if \"dummyLabel\" in prop_dict:\n",
    "            atom.SetAtomMapNum(int(prop_dict.get(\"_MolFileRLabel\", 0)))\n",
    "        if \"molAttchpt\" in prop_dict:\n",
    "            attach_num = prop_dict.get(\"molAttchpt\", 0)\n",
    "            if show_attachment:\n",
    "                new_atom = Chem.Atom(\"*\")\n",
    "                new_atom.SetAtomMapNum(attach_num)\n",
    "                new_atom_idx = rw_mol.AddAtom(new_atom)\n",
    "                rw_mol.AddBond(new_atom_idx, atom_idx, dm.SINGLE_BOND)\n",
    "                rw_mol.UpdatePropertyCache(strict=False)\n",
    "            else:\n",
    "                atom.SetAtomMapNum(attach_num)\n",
    "        if \"molFileValue\" in prop_dict:\n",
    "            f_val = prop_dict[\"molFileValue\"]\n",
    "            f_val_query = dm.from_smarts(f_val)\n",
    "            if len(f_val) > 1 and f_val_query is not None and any(x.GetSymbol() == '*' for x in f_val_query.GetAtoms()):\n",
    "                query_atoms.append((atom_idx, dm.from_smarts(f_val)))\n",
    "        if remove_isotopes:\n",
    "            atom.SetIsotope(0)\n",
    "    \n",
    "    mol = rw_mol.GetMol()\n",
    "    for atom_idx, query in query_atoms:\n",
    "        Chem.rdmolops.AddRecursiveQuery(mol, query, atom_idx, preserveExistingQuery=(not replace_query_by_atom_val))\n",
    "    \n",
    "    if sanitize:\n",
    "        mol = dm.sanitize_mol(mol)\n",
    "    smarts_val, smiles_val = None, None\n",
    "    if smarts_prop is not None:\n",
    "        try:\n",
    "            smarts_val = dm.to_smarts(mol)\n",
    "            mol.SetProp(smarts_prop, smarts_val)\n",
    "        except:\n",
    "            pass\n",
    "    if smiles_prop is not None:\n",
    "        try:\n",
    "            smiles_val = dm.to_smiles(mol)\n",
    "            if smiles_val is not None:\n",
    "                mol.SetProp(smiles_prop, smiles_val)\n",
    "        except:\n",
    "            pass\n",
    "    return mol"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Running some tests"
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Let's quickly check if we can recover a query (SMARTS) converted to a Mol block with the reader."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "source": [
    "mol = dm.from_smarts(\"c12c(n(C)c(=O)[nH,$(nC)]c(=O)1)ncn2C\")\n",
    "mol"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x15e1dd4c0>"
      ]
     },
     "metadata": {},
     "execution_count": 211
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "source": [
    "out = dm.to_smarts(read_mol_block(Chem.MolToMolBlock(mol)))\n",
    "out"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'[#6]12[#6]([#7]([#6])[#6](=[#8])[$([n&H1,$(nC)])][#6]1=[#8])[#7][#6][#7]2[#6]'"
      ]
     },
     "metadata": {},
     "execution_count": 212
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "source": [
    "dm.from_smarts(out)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x15e1dd640>"
      ]
     },
     "metadata": {},
     "execution_count": 213
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "We can now verify the validity of the parsing in term of maintaining query information"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "source": [
    "example_4_mols = [\n",
    "\"C(C(=O)[H])(CC)=O\",\n",
    "\"C(C(=O)F)(C1CC1)O\",\n",
    "\"C(C(=O)Cl)(F)C\",\n",
    "\"C(C(=O)F)([H])N\",\n",
    "\"C(C(=O)[H])(CC)N\",\n",
    "\"C(C(=O)F)(C([H])(C1CC1)[H])O\"]\n",
    "\n",
    "def load_mol(x):\n",
    "    return Chem.AddHs(dm.to_mol(x), explicitOnly=True)\n",
    "example_4_ori_mols = [load_mol(x) for x in example_4_mols]\n",
    "\n",
    "example_4_pos = [3,4,5]\n",
    "example_4_neg = [0,1,2]\n",
    "dm.viz.to_image(example_4_ori_mols, legends=['Pos' if i in example_4_pos else 'Neg' for i in range(len(example_4_mols))], n_cols=3)\n"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "execution_count": 222
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "source": [
    "example_4_query_1 = read_mdl(example_4)[0]\n",
    "example_4_query_res_1 = [load_mol(x) for x in example_4_ori_mols]\n",
    "highlights = []\n",
    "for m in example_4_query_res_1:\n",
    "    _ = m.GetSubstructMatch(example_4_query_1) or []\n",
    "    highlights.append(list(_))\n",
    "dm.viz.to_image(example_4_ori_mols, highlight_atom=highlights, n_cols=3)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "execution_count": 223
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "source": [
    "tmp = dm.to_smarts(read_mol_block(example_4))\n",
    "print(tmp)\n",
    "example_4_query2 = dm.from_smarts(tmp)\n",
    "example_4_query_res_2 = [load_mol(x) for x in example_4_ori_mols]\n",
    "highlights = []\n",
    "for m in example_4_query_res_2:\n",
    "    _ = m.GetSubstructMatch(example_4_query2) or []\n",
    "    highlights.append(list(_))\n",
    "dm.viz.to_image(example_4_ori_mols, highlight_atom=highlights, n_cols=3)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[#6](-[#8,#7])(-[$([!R,#1]):0])-[#6](-[#9,#17,#35,#53,#85,#1])=[#8]\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "execution_count": 224
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.9.2",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.2 64-bit (conda)"
  },
  "interpreter": {
   "hash": "0545d51263b4633cc1c20c1cfa9d7a4b0758c4af8cbc80594f7f0b6373fd5641"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}