greglandrum/replicating RGD substructure search.ipynb

## replicating RGD substructure search.ipynb
{
  "cells": [
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-07-28T15:16:34.347898Z",
          "end_time": "2022-07-28T15:16:34.353433Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from rdkit import Chem\nfrom rdkit.Chem import Draw\nfrom rdkit.Chem.Draw import IPythonConsole\nIPythonConsole.drawOptions.addAtomIndices = True\nimport rdkit\nprint(rdkit.__version__)",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": "2022.03.4\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-07-28T15:16:38.640656Z",
          "end_time": "2022-07-28T15:16:38.646613Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "qry = Chem.MolFromMolBlock('''\n     RDKit          2D\n\n  0  0  0  0  0  0  0  0  0  0999 V3000\nM  V30 BEGIN CTAB\nM  V30 COUNTS 8 8 0 0 0\nM  V30 BEGIN ATOM\nM  V30 1 C -3.362821 -2.771429 0.000000 0\nM  V30 2 C -2.125642 -2.057143 0.000000 0\nM  V30 3 C -2.125642 -0.628571 0.000000 0\nM  V30 4 C -3.362821 0.085714 0.000000 0\nM  V30 5 C -4.600000 -0.628571 0.000000 0\nM  V30 6 C -4.600000 -2.057143 0.000000 0\nM  V30 7 R# -5.837179 0.085714 0.000000 101 RGROUPS=(1 1)\nM  V30 8 R# -5.837179 -2.771429 0.000000 102 RGROUPS=(1 2)\nM  V30 END ATOM\nM  V30 BEGIN BOND\nM  V30 1 1 1 2\nM  V30 2 2 2 3\nM  V30 3 1 3 4\nM  V30 4 2 4 5\nM  V30 5 1 5 6\nM  V30 6 2 6 1\nM  V30 7 1 5 7\nM  V30 8 1 6 8\nM  V30 END BOND\nM  V30 END CTAB\nM  END\n$$$$''')\nqry",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": "<rdkit.Chem.rdchem.Mol at 0x7fbb1c76d820>",
            "image/png": 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uhBI8Y2lERISurm5KSoqVlZWtrW1RURHddQEJQIyCXunx48fXrl17/Pgxi8WiuxYpsLW1zczMxONho6KiHjx4QHdFQAIQo6D3YbPZmzZtOn36NL5g+nXAjxK8efNm586dzs7OdJcDJPD1/C0EfceBAwfmzZsnmJjua6Krq+vn50d3FUAycDQKepn09PSwsDAvLy+R5YcOHRo9evSlS5doqQr0ZXA0CnqZzZs3Dx061N3dHSFUV1e3Z8+eixcvqqioVFVVFRYW1tfX010g6HMgRkEv4+Pj09raij/fuHFjyZIlMIgS0AtiFPQylpaWgs/KysozZ87E05cAQBeIUdCLZWZmfvPNN3RXAfo6iFHQiw0YMIDuEgCAO/UAAEANxCgAAFACMQoAAJRAjAIAACUQowAAQAnEKAAAUAIxCgAAlECMAgAAJRCjAABACcQoAABQAjEKAACUQIwCAAAlEKMAAEAJxCgAAFACMQoAAJRAjAIAACUQowAAQAnEKAAAUAIxCgAAlECMAgAAJRCjAABACcQoAABQAjEKAACUQIwCAAAlEKMAAEAJxCgAAFACMQoAAJRAjAIAACUQowAAQAnEKAAAUKJIdwEASIe9vb2+vr6VlRXdhYA+B2IUfCUsLS0tLS3prkIKGhsbv/nmG7qrABKAk3oAZAWLxTpw4ICurm5xcTHdtQAJQIwCQD8+n3/p0qWxY8cePHiwvr7+/v37dFcEJAAn9QDQLCkpyc3N7cWLFwih6dOnnzx50sLCgu6igATgaBQA2pSVlTk5OZmbm7948UJTUzM4OJjJZEKG9jpwNAoADVpaWgICAg4fPtzY2MhgMFxdXffu3Qt3lnopiFHQC5AkmZqaihBKT0/n8XgKCgp0V0RJZGSkm5tbYWEhQsjGxiYgIGDUqFHib85ms7Ozs7utOiA5EgDZlpycjEcyDR06FCE0bty4u3fv0l1UF6WkpHz33Xf4n96UKVMeP34saQ8RERFjxoxBCP300081NTXtG3z69EkalQIJQIwC2VVeXu7s7CwvL48Q0tDQ2LRpk56eHs6g5cuXFxQU0F2gBKqqqlxdXfFx9JAhQ/z9/blcrkQ9ZGRkWFtb468/YcKEBw8eCK/lcrkhISFTpkxBCHE4HKnWDjoBMQpkUVtbm7+/f//+/RFCSkpKrq6u9fX1JEmy2Wx/f398DbFfv36C5bIMf5cBAwYIvktdXZ1EPdTU1Li6uioqKiKEBg0a5O/v3z4oeTze6dOnc3JyIEZ7HsQokDmC81aEkLW1dWZmpkiDsrIyFxcXfJSqqakZGBjI4/FoKbVTsbGxEyZMEHyXN2/eSLQ5h8MJDAwcNmwYQkhRUdHFxeXjx48dtOfxeBCjPQ9iFMiQrKysRYsW4dAxNDS8c+dOB42TkpK+/fZb3NjU1PTp06c9Vqc4srOzlyxZgsszMDCIjIyUtIe4uLiJEyfiHubOnZuent7pJhCjtIAYBTJB5LzV19eXzWYL1qamprr814ULFwTL+Xx+eHi4jo4OQkhOTs7BwaG4uJiO8v9HbW2tu7t7v379EEIDBw4U+S7iyM3NdXBwwAGqr68fHh4u5oYQo7SAGAXkkSNHfIXU1tb25N7FOW8NCgpaunRpbGxsbGzsu3fvRNayWCxPT08VFRWEkKqqqqenZ0tLS0+V/z94PF5wcPDw4cMRQvLy8o6OjpWVlRL10NTUJPguampqkn4XiFFaQIwCMigoKDAwMDAw8Pjx4wwGoydjVOS8NS0t7bPNDhw4cOjQoY67KikpcXR0xF1pa2sHBwd3Q70diY+PnzRpEi5g9uzZr1+/lmhzPp8fHBysoaGBj6wdHR3Ly8sl6uH169cxMTEIoXv37uXm5kq0LaACYhT8P39//3Xr1vXMvoTPW8eMGdPxeeuGDRtsbW2dnJy2bt1aVlbWQUuKWdY11BM8MTFR8Azo9OnTnz9/3oUyzpw5I7j0cfPmzS70ALoGYhT8hcvl6uvrv337trt31IXz1tevX0dGRr59+9bd3d3Y2Ljj+/LUz6zFR/16wvv37x0dHeXk5BBCI0eODA4O5vP53VQt6CYQo+AvV69eXbJkSbfugsfjXblyBU9QLy8v7+zsLOl5K4/H69evX1FRUactqd/n6Rj1u1vNzc2+vr7q6uoIIQaD4e7u3tDQIMUKQY+BGAV/mTFjxsOHD7uv/9TUVBsbG4Ig5s2bJ+l5a3x8fGtrK0mS6enp6urqLBZLzA2pjzr6LCaTaW5ujrs1Nzd/+fKlpD1EREQInsiysbHpXU9kAREQo4AkSTIhIcHExKSbTifLyso2btyoqalJEMTUqVNv3rwp0Y74fP6aNWt0dHQWL148cuTIP/74Q9ICKI6Bb+/EiRNdHvlP/bF6IGsgRgFJkuTSpUtDQ0Ol3m1LS8vp06fHjh1LEMSoUaMOHTrU2NjYta4aGhrS09PFPw4VQf2JTJHejh49Kul3of5YPZBNEKOAJEkyLi6ura1Nun3GxMSYm5sTBEEQhJOTkywMjKcryKQb4kDWQIzSoKmpie4SuldGRsayZctwgM6fP//Fixd0V/Q/evi0OjY2dvz48VK8pABkDcRoz+HxeCdOnDA2NiYIwsDAYP/+/VI/AKRdTU2Nh4eHlpYWQRDjx48PCgqS2fPWiIgIwWTJ3XSTR+QGV1RUlNR3AWQBxGjPOXbsmLGxcXR0dEVFRUREhI6OzokTJ+guSmra2tqCgoIMDQ0JgtDR0fHw8JD9KezwkCM87Z50hxx193ArIFPkSJJEoEeUl5dXVVUJnn1cv359WVnZtWvXLl68KNxMWVl5/Pjx5ubmIq/KqK6u/vPPPwsKCurq6giCmDt37syZMzveI5PJfPjw4aRJkxYvXiyyqrW1NTo6Ojk5uaamZsSIEbNmzZo9ezYeBC7A5/Pj4uKYTGZ5ebmqqqqJicnSpUs/+76ghISEffv24ckuraysvLy8DA0Nxfut0K+srGz37t34DpumpqaPj49gPHwX8Pn80NDQHTt2fPz4UV5e/scffzx27Bh+FgB8tejO8b5r27ZtCxcu/PTpE/E5ZmZmTCZT0PjixYu6uroibX766afPXhbg8XgxMTF4kCYepynSoKCgYObMmSK9OTg4CN96zs/PnzVrlkibSZMmpaamCneVn5/v6OiI186cOTM2Nlaqv6SeI5XHMR8+fGhiYoI76bFHUQHtIEbpweVyzczM9u3bJ4jRx48fP3ny5NGjR2FhYd9//z1BEGPHjn3//j1JklevXsU/BgUF5efnl5aWhoSE4FFEhw8fFuk5OTnZ1NSUIAhtbe05c+a0j1E2m40z1MbGJjExsaKiIiYmBm+ydu1a3KahoWHy5MkEQbi4uCQlJVVUVDCZTFtbW4IgJk+e3NzcTJJkfX39oUOHdHR0CIIwNDQ8ffp0b7/US2VyENonRgE0ghilx/Hjxw0MDCorKwUxKjwinc1mz549myCIX3/9lSTJX3/9dfTo0UlJScI9XLp0iSAIIyMjkXs4lZWV69atu379el1dXWJiYvsYvXjxIkEQpqamwtcBc3JycCCmpKSQJPnq1Stzc/OtW7cKb1hbW2tkZEQQxI0bN0iSXLFiBUEQWlpa7u7u1dXVUvvV0E3SR/6F29M7TR+gC8QoDUJCQnR1dfEryT4boyRJ+vn5EQSxevVq/GP7MVIfPnzAG5aWln5pR5+NUXxQee7cOZHG69evJwhi3759+EcOh9M+DpycnAiCwHPWvXjxwsHBof0bPr4OeXl5ggmovnR0KZuTRoOeJ9+zV2L7Oi6Xe/DgwQMHDgQGBs6bN6+Dlvj+Ep4NHiGkpqYm0kCwhCTJtra2srKyioqKTgvgcDhpaWkIITw/iDA8lPLVq1f4R0VFRXyEJQzPo0GSJEJoxowZ4eHh48aN63SnvRGeuy8uLs7ExKS0tPQf//gHfo2HoMGrV68sLS1XrFhRUlKCX2EiiFTQ10CM9pzq6uoffvghMjLy5s2bCxYs6KAll8vF8+8aGRl9qU1ubi5CSE1NTUNDg8lkTp8+ff78+Z3WUFJSwuFwEEIEQYiswksKCgo62Dw7OxshpK+v3+mOvg5z585NTk7Gk/PHx8dPmTLFyckpIyNj/fr15ubmz58/x4/VM5lMwVuhQB+kSHcBfcjatWuZTCaDwVi5cqVg4f379/GH4uJiOTk5Npudn59/6dKljIwMBoPh5OT0pd7CwsIQQvPnz1dSUhK/hoaGBoSQgoLCwIEDRVYNHToUIdTY2Mjn8/FLN0W8fv06MzNTWVm54+Porwx+r4m9vb2np+f58+dDQkJu3rzJYrFUVFS2b9++Z8+e9icKoK+BGO05R44caWlpEVmorKyMP4gczhAEcfbs2fbHjFhSUtL169eVlJTc3NzwtllZWZ/NPhH4XT1KSkrtG+Ox4iRJ8ni89ms5HI6HhwdCyMnJCb83qU8ZPHjwqVOnNmzYsG3bNisrq7S0tKNHjwoeggJ9HMRozxk7dmz7hVVVVfjD6tWrEUKFhYXPnj3T19ePi4v70mHm+/fvN2zYwOVyPT098Sh3BQWF/v37i1MDPnRqbW1lsVgih1HV1dUIIWVl5fb7JUnSw8MjJSVl3Lhxu3btEmdHX6UJEybgiy0ACIMYlRV+fn5ycnJNTU1WVlZ5eXkxMTGCx7GFvX//3t7evry8/J///Of69esl3Yumpib+UF1d/dkYHTlypMgmOENDQkJGjx595coVBoMh6U4B+LrBLSbZoq6uvnfvXoSQp6dnc3OzyNrk5GRbW9uSkpK1a9fu2bOnC/0PGDBAS0sLIZSRkSGyCt+GNjY2Fl7Y0tKyYcOGS5cu6enphYeH98HTeQA6BTEqc5YvX25ubv7hw4dz584JLw8PD7e3t6+pqTl8+LCXl1eX+//+++9xb8ILORzOrVu3EELCQwhKS0uXLl0aGRlpZWV1584dwZEsAEAYxKjMkZOT8/b2VlBQOHPmTElJCUKotbV13759W7duVVNTCwsLc3Z2FtkkJyfHzc0NH8Z2au3atUpKSrGxscHBwXgJj8fbu3dvWVmZjo6OjY0NXnj//v0FCxZkZGSsXr06NDR00KBB0vuKvVVtba2Xl9fq1auPHj3a/m4h6LNghieaVVVV4cksysrKhGcV2rlzZ2ho6KJFiy5cuGBtbZ2ZmYkQGjly5JAhQ0R62Lhx4+DBg1euXDlkyBB8qu7v73/v3j2EEIvFys/PV1FRMTAwQAhpa2sHBQUhhH7//Xd8TWDcuHGjRo3KyMgoLS1VUVEJDw83NTVFCF2+fBnfSmIwGO3vjOno6Pz73//urt+IDFuwYIGWltaaNWtOnDgxaNCgCxcu0F0RkAlwi4lm8vLy+N0SInbt2hUfH//8+fPExMSamhrcpqmpqampSaRlY2OjiYnJ+vXr8SNGCKGampri4mL8GW+IfxTMvLdmzRptbe1Tp04lJye/e/dOWVl5wYIFu3btEsxuV1lZKahK0JWA4NmqPiU3NzcxMfH27dsqKiqGhoajRo367bffPvtnB/oaOBrt03g8XlNTE2SBOCIjI318fJ4/f45/1NXVvXXr1pQpU+itCsgCuDbapykoKECGiqmlpUV4kgEGg8FisWisB8gOiFEAxEIQxIcPH/BnPp9fUVGBh44BADEKgFjMzMxqa2uZTCZC6Pbt29ra2np6enQXBWRCX7xXAEAXKCsrBwQELFu2zMDAIC8vLzQ0lO6KgKyAW0wASIDL5ZaWlurq6oozEQzoIyBGAQCAEvgfFQAAKIEYBQAASiBGAQCAEohRAACgBGIUAAAo+T9pJmzqkImxrAAAAMd6VFh0cmRraXRQS0wgcmRraXQgMjAyMi4wMy40AAB4nHu/b+09BiDgZYAARiDmgOIGRjaHDCDNzIyfoQFisMBoBhkNkCGMIEISZIxjSX6uX2lOjoYMg7ZEqg4TUCjIEKaKCauqNIgqI24GxgwmRqYEJuYMJmaWBBbWDCZWhgQWNgZWdgYRRiDFwszEKJ4FdTcYcJRZhB+QiDc8AOK0cnEcsF7BDGezv1LYD2KD1FzqWm8LYhsbT4aLg9gw9TqndsHVgNgwM8UA3s4yOIBjTfkAAAEWelRYdE1PTCByZGtpdCAyMDIyLjAzLjQAAHicfVLNasMwDL7nKQS7dIcYSf7NoYf8lDJGkpGkfYfd9/5UXkgdQ4hkgSx9/ixLLiDK1H3//sFbuCsKADxZVVXBUyNi0UN0oLndvwZol7rZIu34GJYZgihGzZH1MvZbhKCFUivtODBBycp7MlwBKvyXdJYjkhWxdYajh1ag+gCpcyQqIRfsAdJkt0s+4swB0EagUW4Nn1G6HHlSpofpA0qrgvbkq4PbCQn6ep6vBNN9Gh8/8/VCQJ8bQcgJDppHyCsD7xn4zXAbumwa63yacejSfIwYpyEYMZ06baKldka1qWmyAZc6Y8QoPZ8k69NjSLJhX9m+jrjffpj4xQsY/IXc9UusNQAAANB6VFh0U01JTEVTIHJka2l0IDIwMjIuMDMuNAAAeJxtj7sKwkAQRX/FUiUZdmZfYazsNYitWMSYLkuCmELIxzsLJhvR7p7DvbNsjXW9vuCWUeF1I5FiJIk1rsZ1ToBknclyBY4K6zHb5Rq0ZMoUqGiMKAPuq2Kh0B6LnwqB+uDUEOXF6MXdhZqfn4abrHp24fToelYQY9k9GyDGBJopgWENoWv3wseqL4dwax4gfxV9H0J4Hapb08I58rxxbBJYtv8OkOjlgcjzxrNLoNgnwPEN0x9kqxijO+EAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-07-28T15:16:39.026378Z",
          "end_time": "2022-07-28T15:16:39.052664Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "m1 = Chem.MolFromSmiles('Cc1ccccc1c1ccccc1')\nm1",
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 22,
          "data": {
            "text/plain": "<rdkit.Chem.rdchem.Mol at 0x7fbb1c76db20>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-07-28T15:16:39.442380Z",
          "end_time": "2022-07-28T15:16:39.450668Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "m2 = Chem.MolFromSmiles('Cc1ncccc1c1ccccc1')\nm2",
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 23,
          "data": {
            "text/plain": "<rdkit.Chem.rdchem.Mol at 0x7fbb1bc21340>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "## First find the right substructure match"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-07-28T15:05:19.668704Z",
          "end_time": "2022-07-28T15:05:19.681444Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "m1.GetSubstructMatches(qry)",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "((2, 3, 4, 5, 6, 1, 7, 0),)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-07-28T15:05:54.531733Z",
          "end_time": "2022-07-28T15:05:54.541369Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "m2.GetSubstructMatches(qry)",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 11,
          "data": {
            "text/plain": "()"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "In order to reproduce what the RGD code is doing, we have to allow matches to Hs, which means adding Hs to the molecules.\n\nBut then we get too many substructure matches:"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-07-28T15:06:36.138931Z",
          "end_time": "2022-07-28T15:06:36.141319Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "m1h = Chem.AddHs(m1)\nm2h = Chem.AddHs(m2)\nprint(m1h.GetSubstructMatches(qry))\nprint(m2h.GetSubstructMatches(qry))",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": "((1, 2, 3, 4, 5, 6, 19, 7), (1, 6, 5, 4, 3, 2, 17, 16), (2, 1, 6, 5, 4, 3, 18, 17), (2, 3, 4, 5, 6, 1, 7, 0), (3, 2, 1, 6, 5, 4, 19, 18), (3, 4, 5, 6, 1, 2, 0, 16), (7, 8, 9, 10, 11, 12, 23, 24), (7, 12, 11, 10, 9, 8, 21, 20), (8, 7, 12, 11, 10, 9, 22, 21), (8, 9, 10, 11, 12, 7, 24, 6), (9, 8, 7, 12, 11, 10, 23, 22), (9, 10, 11, 12, 7, 8, 6, 20))\n((7, 8, 9, 10, 11, 12, 22, 23), (7, 12, 11, 10, 9, 8, 20, 19), (8, 7, 12, 11, 10, 9, 21, 20), (8, 9, 10, 11, 12, 7, 23, 6), (9, 8, 7, 12, 11, 10, 22, 21), (9, 10, 11, 12, 7, 8, 6, 19))\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-07-28T15:25:18.629705Z",
          "end_time": "2022-07-28T15:25:18.637725Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "from rdkit.Chem import rdDepictor\nfrom rdkit import Geometry\n\ndef get_rgd_match_mol(mol,qry):\n    mh = Chem.AddHs(mol)\n    non_hs = [x.GetIdx() for x in mh.GetAtoms() if x.GetAtomicNum()>1]\n    non_isohs = [x.GetIdx() for x in mh.GetAtoms() if x.GetAtomicNum()>1 or (x.GetAtomicNum()==1 and x.GetIsotope())]\n    matches = mh.GetSubstructMatches(qry)\n    # We want to prefer the matches which have the most heavy atoms:\n    mcounts = []\n    for match in matches:\n        n_nonh = len([1 for i in match if i in non_hs])\n        n_nonisoh = len([1 for i in match if i in non_isohs])\n        mcounts.append((n_nonisoh,n_nonh,match))\n    res_match = sorted(mcounts,reverse=True)[0][-1]\n    resMol = Chem.RWMol(mh)\n    resMol.BeginBatchEdit()\n    for atom in resMol.GetAtoms():\n        if atom.GetAtomicNum()==1 and atom.GetIdx() not in res_match:\n            resMol.RemoveAtom(atom.GetIdx())\n    resMol.CommitBatchEdit()\n    res_match = resMol.GetSubstructMatch(qry)\n    \n    # set the coordinates\n    conf = qry.GetConformer()\n    cmap = {}\n    for i,aid in enumerate(res_match):\n        cmap[aid] = Geometry.Point2D(conf.GetAtomPosition(i))\n    rdDepictor.Compute2DCoords(resMol,canonOrient=False,coordMap=cmap)\n\n    \n    return resMol,res_match\n",
      "execution_count": 34,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-07-28T15:25:20.923131Z",
          "end_time": "2022-07-28T15:25:20.933262Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "tm1,match = get_rgd_match_mol(m1h,qry)\nprint(match)\ntm1",
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": "(2, 3, 4, 5, 6, 1, 7, 0)\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 35,
          "data": {
            "text/plain": "<rdkit.Chem.rdchem.RWMol at 0x7fbb1bba5cc0>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-07-28T15:25:21.643266Z",
          "end_time": "2022-07-28T15:25:21.651816Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "tm2,match = get_rgd_match_mol(m2h,qry)\nprint(match)\ntm2",
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": "(9, 10, 11, 12, 7, 8, 6, 13)\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 36,
          "data": {
            "text/plain": "<rdkit.Chem.rdchem.RWMol at 0x7fbb1bba5ae0>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "toc": {
      "nav_menu": {},
      "number_sections": true,
      "sideBar": true,
      "skip_h1_title": false,
      "base_numbering": 1,
      "title_cell": "Table of Contents",
      "title_sidebar": "Contents",
      "toc_cell": false,
      "toc_position": {},
      "toc_section_display": true,
      "toc_window_display": false
    },
    "language_info": {
      "name": "python",
      "version": "3.9.1",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "",
      "data": {
        "description": "replicating RGD substructure search.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}