greglandrum/Copying stereoinfo.ipynb

## Copying stereoinfo.ipynb
{
  "cells": [
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "from rdkit import Chem\nfrom rdkit.Chem import rdDistGeom\nfrom rdkit.Chem.Draw import IPythonConsole\nIPythonConsole.ipython_3d = False\nIPythonConsole.drawOptions.addAtomIndices = True\nimport rdkit\nprint(rdkit.__version__)",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": "2021.03.1\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "mb = '''\n     RDKit          3D\n\n 11 11  0  0  0  0  0  0  0  0999 V2000\n   -0.2301    2.1359    0.6071 C   0  0  0  0  0  0  0  0  0  0  0  0\n    0.7333    1.1836    0.3744 C   0  0  0  0  0  0  0  0  0  0  0  0\n    0.3032   -0.0717   -0.0237 C   0  0  0  0  0  0  0  0  0  0  0  0\n    1.2370   -1.0667   -0.2707 O   0  0  0  0  0  0  0  0  0  0  0  0\n    2.6289   -0.8412   -0.1287 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -1.0189   -0.3927   -0.1907 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -1.4128   -1.7594   -0.6201 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -0.9366   -2.9561    0.6112 Cl  0  0  0  0  0  0  0  0  0  0  0  0\n   -0.8535   -2.0916   -1.8412 F   0  0  0  0  0  0  0  0  0  0  0  0\n   -1.9582    0.5870    0.0515 C   0  0  0  0  0  0  0  0  0  0  0  0\n   -1.5803    1.8580    0.4515 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  1  0\n  3  6  2  0\n  6  7  1  0\n  7  8  1  0\n  7  9  1  0\n  6 10  1  0\n 10 11  2  0\n 11  1  1  0\nM  END\n'''\nmb_m = Chem.MolFromMolBlock(mb)\nprint(Chem.MolToSmiles(mb_m))\nmb_m",
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": "COc1ccccc1C(F)Cl\n",
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "execution_count": 27,
          "data": {
            "text/plain": "<rdkit.Chem.rdchem.Mol at 0x7f01cde4d3a0>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "smi_m = Chem.MolFromSmiles('COc1ccccc1[C@H](F)Cl')\nsmi_m",
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 28,
          "data": {
            "text/plain": "<rdkit.Chem.rdchem.Mol at 0x7f01cddfbd60>",
            "image/png": 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UPSuxWPz++++fO3fOxsYmPT3d3NxcboeE+hwso6olPT3dy8tr0KBB1OOVpOO8ApfL9fHxycvL8/f3T01Npd6+1xOtra13796lrqvm5ubevXu3paVFdgd9ff1Ro0a5urqWlpampaWZmpreunXLzs6uh/2ivg3LqMqxtbUtLS29du3ahAkTSGd5GZFIFBQUxGQyHRwcbt26ZWyskJXoampqqIEqdSng3r171L8IMzMzNpudlpY2ceJERfSL+hJcaFblzJs3b+vWrXFxcUpeRpcvX85kMs3MzJKSkhRUQwFg4MCBAwcOpCb2A0BDQ0NOTs6NGzeo+dQdHR0K6hf1JTgaVTlFRUUODg79+/d//Pix0t422bFjx1dffaWtrX3lypXx4+lYiY7P5zOZTH19/cmTJzMYjBEjRpSVlXl6eqanp9PQO+rVlP3qGJK7kSNHurm5sdls6QtElc358+dXr17NYDAOHz5MTw0FgPXr17e1tbFYrKNHjwIA1S+LxVKqtaKRcsIyqoqUeSHnrKysefPmiUSirVu3UjnpMWjQoHv37lET+OGvMgoA+LQ0eiUso6ooIiJCXV09KSmJw+GQzvKMmpqa9957r7W1ddGiRWvXrqWza7FYbGlpef/+/adPn4JMGU1PT798+TKdSVDvQ2hJFESYv78/AMTExJAO8jcul0s9FODn59fe3k5z79OnT+fz+Q8fPly8eLFEIhEKhfr6+tS/ES8vL5rDoN4FR6Mq6uULOXO53PT09E5zKhVKJBJFRETk5uaOHDkyMTGR/ntfGzZsWLZs2bZt26KiogBAXV19zJgx1KZbt25duXKF5jyoF8E79SqKzWZbWVkJhcJHjx5ZWlp22nrx4sVp06YBgJWVlXT9JEdHR0dHRwU9V758+fI9e/aYmpqmp6fb2toqoovuWrdu3ffff0/92c/P748//iCbBykv0sNhRExQUBAA7Nq16/lNTCbTzc3t+aeGTE1NJ02a9OWXX8bExOTm5vL5fLkk2bVrFwBoaWldvXpVLl8oF0lJSbLHrlTZkFLB0ajqOnnyZEhIiIeHB4vF6nKHTit9ZGRk1NbWyu5ArfQhHat6eHgMGDCguzFSUlJmzZolEol+++23Dz744A0PRgHq6+sHDBgg/Qfi7+9/9epVspGQcsIyqrra29stLS05HE5JSclrnkfX1NRIq2pmZmZRUZFYLJbd4UUrfbxIfn6+j48Ph8OJjo7euHFjj45HAahJ+NIflf8JWkQG4dEwImrhwoUAEB0d/WYf53K5N2/e3L9//9KlSz08PHR1dTv97TI0NCwrK3vRx2tqagYPHgwAoaGhYrH4TQ9CgebPny89FjUtrYmTJpFOhJQRjkZV2qVLl6ZMmTJs2LC0tDQbG5ue3z6SrvRBLfZRU1PD5XI1NLpYuqGtrc3f3//27ds+Pj6XL19WzsdS9+/f/89lywzGjjWZOtU4IEDC4/3k5jb+uTtySMVhGVVpIpHIx8ensrLy8ePHBgYGdnZ20vPx0aNH6+np9fD7m5qaulxVRCwWz5kzJzEx0dramsViKe1qntnZ2bNiYy3CwqQt4wwM9uO6eehZWEYRBAcHp6enV1dXyzaqq6vb2dlRbzSiXm1kZWUlrx6/+OKLnTt3mpiYpKenK/NqnkKRyCsrS/zs5d3D9vauf83MRwiwjKqmzMzM3NzcqVOnDhw4UNrY1NSUn58vXXkzMzOzvb1d9lP9+/eXjlWdnJycnZ3f7Ez80KFDS5Ys0dTUvHjxYkBAQE8PRsEWFhUVtLbKtngYGu5TjpmtSElgGVU5jY2NBw4cmDBhQlRU1EueFhcIBCUlJdKq+r///Y962Fyq08uN3dzczMzMXtl7ampqYGCgSCQ6evToggUL5HA8CravuvrXZ1/oBACH7O3dcECK/oJlVBW1trampKTcuHFj586dr/8p6vaRdKza5Wwn6RxSd3f35x95Kiws9PLy4nA4UVFR33zzjXwORsH+5HC+LC3t1Dje0HAPDkjRX7CMqiIOh8NkMlks1p49e974S7hcbklJiXQOaXZ2No/Hk93B0NDQxcVFWlXfeustf3//ysrK4ODgEydO9JaXFTcJhZNzc5//R/LryJEuPb4Fh/oGLKMqh81m19XV2draBgYGnj9/Xl7ljHrkiXpVHPX/uro62R3U1NTEYrG3t/fly5e1tbXl0ik9ZufnP3zubSJeRka7R4wgkgcpGyyjKqe9vX379u1PnjyZPn16YGCg4jrq9HLjioqK9vb2a9eu+fr6Kq5TRYiqqLjQ0PB8e8zIkU44IEVYRhFtPv7444MHD65du/a7774jnaV7Tj19+kNV1fPtPkZGO3FAinD1e6Q4ubm5oaGhCxYsSE5OBgBq2ZHY2Nhe95t71AuGnDc4nMJn50Ih1YSjUaQotbW1BgYG1HrMSUlJEonE2tq6oqLixo0b3t7epNN1g1gi8c/J4T07LYEy1sDgJyV+fADRA0ejSFEsLCx0dXVv3rzp4eEBAAwGIzQ0FJT1VXovocZgOL5gQJrB5d7hcmnOg5QNllGkQBkZGfHx8atWraJ+pBZMOnHiBJ/PJ5qr2150Xg8Asc+uwYpUEJZRpCiJiYlz5861s7M7duwY1eLo6Oji4tLY2JiWlkY2W3e5vOCZJQbAIlzwSeV1sYIZQnLh6+tLLRcv+zKS8PDwtWvXxsXFzZgxg1y0bnPR02MAyN5GGKGjM83EZIqJieVzr1pBqgZvMSFaVVVVDR8+XFtbu7a2Vr9XPZa+urz8CpttpaU1tF+/289eD11kafnZoEGkgiHisIwiuvn5+V2/fv348eMRERGks3TPw46Ot/r1O1FXd/Dx48P29tJ2Yw2N/l0tTY1UBF4bRXQLDw8HgLi4ONJBum1wv37Uk7PqAMO1taX/YQ1VcVhGEd1CQkK0tLQuXbrU6aF7hHopLKOIbiYmJu+++65QKDx16hTpLG+oWSSaf++e9D8xXhlTbXgyggiIiIjIzMiQZGSQDvKGtNXUwgYMkP7YWxb9QwqCt5gQAUIejzFwoHpzM5SVwfDhpON0z+91dYcfP05zdSUdBCkLPKlHBGjo6qrPnAkSCcTHk86CUE9hGUWEhIcDABw/TjoHQj2FZRQRMnkyWFjAvXuQm0s6CkI9gmUUEaKhAXPnAgD0tgmkIebm511cSKdASgRvMSFybt0Cb28YPBgqKkANf6Oj3gr/7iJyxo8Ha2t4+BCuXycdBaE3h2UUkcNgQFgYAPT2+/VVVVUJCQm1uPCoqsIyioiiVidJSIDn3mDcW7DZ7M8++8zc3DwyMlIoFJKOgwjAMoqIcnAANzdgsyE1lXSUN1RVVWVvbz9x4kRra+v79++TjoMIwDKKSKMmkPa2+/VSLi4u+vr6X375ZU5ODuksiAy8U49Iq6mBwYNBSwtqa8HQkHSaN9Te3h4cHHz69GktXAxf9eDSJIi0gQPB1xf++APOnYP580mn6TaJRLJy5Uo+n//NN99gDVVNOBpFSuDECWCxYMkScHQkHQWhbsMyipRDWRmcPg0VFaCjA56eMHs24JLyqJfAW0xICfz6Kzg4wNmzoK4ONTXw6acwbhzg2viol8DRKCKtoABGj4aoKNiw4f9bamvBwwPc3eH0aaLJEHotOBpFpB06BObmsGrV3y0WFrBuHSQmQk0NuVgIvS4so4i0zExwd4d+/Z5p9PYGsRiyswllQqgbsIwi0pqbwdy8c6OFBQAAhyPb1tHRER0dPWXKFLqSIfRasIwi0gwNu7ibRC3zYWws26alpbV8+XIjIyO6kiH0WrCMItLc3SErC/j8ZxrT00FNDd5+W7aNwWAYP1tYEVIGWEYRaZGRUFcHu3f/3cJmww8/QFAQWFqSi4XQ61LfvHkz6QxItQ0YAObm8NVXcPMmlJTAyZPw+edgYAC//w76+p32/fnnn1NSUhgMxpgxY4iEReh5OG8UkSMW//3ukMJCiIuDBw9AVxe8vCA8vPO9ewAAqKqqEgqFGhoaQ4YMoTUqQi+GZRQRIhbDnDng6gp4PoR6OSyjiJAVK2D3bjAzg7t38Roo6tXwFhMi4eBB2L0btLQgIQFrKOrtcDSKaHfxIsycCSIRxMT0xgVGEeoER6OIXgUFEBoKQiFs2oQ1FPUNOBpFNHr8GDw9oaoK5s2D+HhgMEgHQkgOsIwiurS1gb8/3L4NPj5w+XKX85kQ6o3wpB7RQiyG8HC4fRusreHMGayhqC/BMoposWoVJCaCiQlcuNDFek4I9WZYRpHiHT4MO3aApiacPAn29qTTICRnWEaRYv330iXunj0AAL/8AgEBpOMgJH94iwkpUH5+vre3t4ZAkB0dPWTlStJxEFIIHI0iRXn69GlQUFBzc/OkwMDBX31FOg5CioKjUaQQbW1tAQEBLBZr7Nix165d09XVJZ0IIUXB0SiSP4lEEhkZyWKxhg0bxmQysYaivg3LKJK/NWvWxMfHGxoaJicnDxgwgHQchBQLT+qRnB05ciQyMlJTU/PChQvvvPMO6TgIKRyORpE8/fHHH59++ikA7N69G2soUhFYRpHc3Lt3b/bs2Xw+f82aNZ988gnpOAjRBE/qkXzU19ePHz++tLR0zpw5CQkJamr4GxqpCiyjSD6mTp2amprq4eFx9epVHR0d0nEQoo8G6QCoj/jxxx/b29tPnDiBNRSpGhyNIvkrLi4uKysLCAjQ1tYmnQUhhcMLWEjOcnJytmzZ0t7evnTpUtJZEKIDllEkZ4mJiStWrHj//fdbWlr4fD7pOAgpHJZRJGc8Hk9PTw8AdHV129vbScdBSOGwjCI5GzVqFIvFEggEDQ0NhoaGpOMgpHB4iwnJmUAgiI6ObmhoCA8P9/HxIR0HIYXDMooQQj2CJ/UIIdQjWEYRQqhHsIwihFCP/B84fJFwkwUiywAAAP16VFh0cmRraXRQS0wgcmRraXQgMjAyMS4wMy4xAAB4nHu/b+09BiDgAWJGBgjghuIGRjaGBJA4MweDBpBmZmJzANMsbA4ZIJqZES8DqlbBBEgzMbJwMiiADBOEUNxA6xiZFJiYM5iYWRJYWDOYWNkS2NgzmNg5GDg4WRg5uBjYmRJEGNmY2NlYWZjFYY5j4DY+27D/h7v3ARBHM5Rxb4E/E5jNLcWxv81Ufj+I/TWD4cBluU37QGzlLIUDmTUd9iD2XsEj+/eY8TqA2J9nZe7NjvoOFhe69shu1hoWMDur6qv9D8kqOxA7eZ+Uw/0DU8FmVslZOvibbASrEQMAej40ZGMiU8wAAAEBelRYdE1PTCByZGtpdCAyMDIxLjAzLjEAAHicnZJNasQwDIX3PoUuUCPJ8o/WTbsp7cAs5g4DXfb+VLIzJoUZ6NiI5L04+iJLCeDrvH1cf2Au3kIAIg/Au6GqcGFEDPb6C0XEklylSLUrQHuGCK/wCHGMTrGMRMUVmyp0oJyeoeSdgrEw6Vot7CeSQUlSlykZS/Zc61BZ7Qv5iVqfS+Sdt9TdXHHUoolliWIZIgmHytTWTuQVUG0jl0V4jcJRqE/GOkRFjzN6f4bSlPcZpSZ/+vL9T4r9qtyvbkylaUxJDzd2y9OYKtOYqtOYatOY0klTIOyGhqG5026fwvAJ8Pa1hV+cKJgNzpqYHQAAAOx6VFh0U01JTEVTIHJka2l0IDIwMjEuMDMuMQAAeJxNjr1qQzEMRl+lYwK6Rr+2pbsELpRObffSoYRuLbeUjHn4yiHgetI5sj5peznTeTx6205P74fH4/b1cD0sVBArV1ikUKvcYF2woJA1U1g4y0o3Z0JVG2RVmXw4HqNKQ4m2fneWrkPG1iopaAw2By6MY/4Wbw2FLT+5sBqsWFQFxSHXUGdzWLNHradgVVZYuWhy7qdC1d2G6c7qmSJd84wjfFz279ff/SewjPJ5v3wWDJpAwRM4ZIKETtCwCRZ1Qo02oUWf0MMneNC/Ewivf9biYig2mumNAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# get mapping from smi_m to mb_m\nmatch = mb_m.GetSubstructMatch(smi_m)\n\n# find chiral atoms:\nchi_ats = []\nfor atom in smi_m.GetAtoms():\n    if atom.GetChiralTag() != Chem.ChiralType.CHI_UNSPECIFIED:\n        chi_ats.append(atom)\n\n# copy over chiral info\nfor smi_atom in chi_ats:\n    # order of the neighbors around smi_atom\n    smi_order = [x.GetIdx() for x in smi_atom.GetNeighbors()]\n    \n    mb_atom = mb_m.GetAtomWithIdx(match[smi_atom.GetIdx()])\n    mb_atom.SetChiralTag(smi_atom.GetChiralTag())\n    \n    # check if we need to change that due to neighbor ordering differences\n    # current order of neighbors in the mb_mol\n    mb_order = [x.GetIdx() for x in mb_atom.GetNeighbors()]\n    \n    # with the smiles order:\n    mb_smi_order = [match[x] for x in smi_order]\n    \n    # check if it's a cyclic permutation:\n    tmp1 = ','.join([str(x) for x in mb_smi_order])\n    tmp2 = ','.join([str(x) for x in smi_order + smi_order])\n    \n    \n    if tmp1 not in tmp2:\n        # not cyclic:\n        mb_atom.InvertChirality()\nprint(Chem.MolToSmiles(mb_m))\nprint(Chem.MolToSmiles(smi_m))",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": "COc1ccccc1[C@H](F)Cl\nCOc1ccccc1[C@H](F)Cl\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "smi_m2 = Chem.MolFromSmiles('c1cccc(OC)c1[C@@H](F)Cl')\nsmi_m2",
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 31,
          "data": {
            "text/plain": "<rdkit.Chem.rdchem.Mol at 0x7f01cdae0d00>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "# get mapping from smi_m to mb_m\nmatch = mb_m.GetSubstructMatch(smi_m2)\n\n# find chiral atoms:\nchi_ats = []\nfor atom in smi_m2.GetAtoms():\n    if atom.GetChiralTag() != Chem.ChiralType.CHI_UNSPECIFIED:\n        chi_ats.append(atom)\n\n# copy over chiral info\nfor smi_atom in chi_ats:\n    # order of the neighbors around smi_atom\n    smi_order = [x.GetIdx() for x in smi_atom.GetNeighbors()]\n    \n    mb_atom = mb_m.GetAtomWithIdx(match[smi_atom.GetIdx()])\n    mb_atom.SetChiralTag(smi_atom.GetChiralTag())\n    \n    # check if we need to change that due to neighbor ordering differences\n    # current order of neighbors in the mb_mol\n    mb_order = [x.GetIdx() for x in mb_atom.GetNeighbors()]\n    \n    # with the smiles order:\n    mb_smi_order = [match[x] for x in smi_order]\n    \n    # check if it's a cyclic permutation:\n    tmp1 = ','.join([str(x) for x in mb_smi_order])\n    tmp2 = ','.join([str(x) for x in smi_order + smi_order])\n    \n    \n    if tmp1 not in tmp2:\n        # not cyclic:\n        mb_atom.InvertChirality()\nprint(Chem.MolToSmiles(mb_m))\nprint(Chem.MolToSmiles(smi_m2))",
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": "COc1ccccc1[C@@H](F)Cl\nCOc1ccccc1[C@@H](F)Cl\n",
          "name": "stdout"
        }
      ]
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      "source": "",
      "execution_count": null,
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