2023_04_25_rdkit_fingerprints_nonfolded.ipynb

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        "<a href=\"https://colab.research.google.com/github/chupvl/gcolab/blob/main/2023_04_25_rdkit_fingerprints_nonfolded.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# How many actual bits in unfolded Morgan fingerprint?"
      ],
      "metadata": {
        "id": "w5g1yFPGXkf5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Vladimir Chupakhin, 2023-04-27*\n",
        "\n",
        "The easiest possible descriptors used in ML for chemistry are fragments, and they are usually used in a folded form. The default folded size is 1024, with some recommendations to use higher dimensionality (>=4096) for computational chemogenomics tasks.\n",
        "\n",
        "Folding of descriptors is done for several reasons: to save space, get a uniform length, with a price of bit collision, and sometimes insensitivity to small changes.\n",
        "\n",
        "Let's check how many actual bits are in drugs as an example of FDA-approved chemical space vs random selection of 300K compounds from Enamine REAL virtual space of 3B compounds commonly used in virtual screening."
      ],
      "metadata": {
        "id": "mQfMv5tLYQq2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Libs"
      ],
      "metadata": {
        "id": "6aFEJtPIXzg9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rdkit -q"
      ],
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        "id": "AoXJbN2PX72A"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import rdkit\n",
        "from rdkit import Chem\n",
        "from rdkit.Chem import rdFingerprintGenerator\n",
        "from rdkit import DataStructs\n",
        "from rdkit.Chem.Draw import IPythonConsole\n",
        "print(rdkit.__version__)\n",
        "%pylab inline\n",
        "\n",
        "import itertools\n",
        "import pandas as pd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6aapUC-X1gu",
        "outputId": "dbad3699-5825-43bf-e26e-faa03bd7eb68"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2022.09.5\n",
            "Populating the interactive namespace from numpy and matplotlib\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def getUniqueBitsForMList(lst_mol, radius):\n",
        "    \"\"\"\n",
        "    Input: a list of RDKit molecule objects and a radius\n",
        "    Output: a set of unique bits generated using the Morgan fingerprint generator.\n",
        "\n",
        "    Parameters:\n",
        "    lst_mol (list): A list of RDKit molecule objects\n",
        "    radius (int): The radius to use for the Morgan fingerprint generator\n",
        "\n",
        "    Returns:\n",
        "    set: A set of unique bits generated using the Morgan fingerprint generator\n",
        "\n",
        "    \"\"\"\n",
        "    try:\n",
        "      mfpgen = rdFingerprintGenerator.GetMorganGenerator(radius=radius)\n",
        "      unique_bits_list = [list(mfpgen.GetSparseFingerprint(m).GetOnBits()) for m in lst_mol]\n",
        "      # unique_bits_set = set([item for sublist in unique_bits_list for item in sublist])\n",
        "      unique_bits_set = set(itertools.chain(*unique_bits_list))\n",
        "      return unique_bits_set\n",
        "    except:\n",
        "      return set()"
      ],
      "metadata": {
        "id": "wu8IoodlbezB"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking for ChEMBL drugs"
      ],
      "metadata": {
        "id": "TE2YUBCqYBuw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KudMAo1zZDWk",
        "outputId": "e2c5d457-da9f-438b-9e09-8ac8cd9c88e0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_drugs = pd.read_csv('/content/gdrive/MyDrive/gcollab/data/chembl/chembl_drugs_lite_20230221.csv')\n",
        "lst_drugs = list(set(df_drugs['canonical_smiles_std'].to_list()))\n",
        "mols_drugs = [ Chem.MolFromSmiles(s) for s in lst_drugs ]\n",
        "len(lst_drugs)"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "jVvrbatIf8G_",
        "outputId": "1f56f628-7cdf-4ce4-ef6a-67eb037d6f64"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4875"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nUB_drugs = {}\n",
        "for r in [2, 3, 4]:\n",
        "  UB = getUniqueBitsForMList(mols_drugs, r)\n",
        "  nUB_drugs[r] = len(UB)\n",
        "  print(f'{len(UB)} unique bits for {len(mols_drugs)} drugs for radius {r} of Morgan fingerprint')"
      ],
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "29201 unique bits for 4875 drugs for radius 2 of Morgan fingerprint\n",
            "75912 unique bits for 4875 drugs for radius 3 of Morgan fingerprint\n",
            "117811 unique bits for 4875 drugs for radius 4 of Morgan fingerprint\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking for 300K random compounds from Enamine lead-like"
      ],
      "metadata": {
        "id": "7bwgWPOxhRZT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_ell = pd.read_csv('/content/gdrive/MyDrive/gcollab/data/enamine_real/Enamine_random.txt.gz', header=None, compression='gzip', sep='\\t')\n",
        "df_ell.columns = ['smiles', 'id', 'sm']\n",
        "df_ell = df_ell[['smiles', 'id']]\n",
        "df_ell.head()"
      ],
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      "execution_count": 7,
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          "data": {
            "text/plain": [
              "                    smiles           id\n",
              "0          CNC1=NC=CC=C1Br  Z1650167172\n",
              "1      CSCCN1CCC(N(C)C)CC1   Z644156236\n",
              "2   COC(C)(C)CN(C)C(=O)CSC  Z1412073910\n",
              "3     CCC(C)OCC(=O)NC(C)CO  Z1497619376\n",
              "4  CC(CO)CSC(C)C(=O)NC1CC1  Z1268437678"
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
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              "      <td>Z644156236</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>COC(C)(C)CN(C)C(=O)CSC</td>\n",
              "      <td>Z1412073910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>CCC(C)OCC(=O)NC(C)CO</td>\n",
              "      <td>Z1497619376</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>CC(CO)CSC(C)C(=O)NC1CC1</td>\n",
              "      <td>Z1268437678</td>\n",
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      "source": [
        "df_ell.shape"
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              "(392661, 2)"
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    {
      "cell_type": "code",
      "source": [
        "# surprisingly fast enough, but dask would be nice to have\n",
        "df_ell_300k = df_ell.sample(300000)\n",
        "lst_ell_300k = list(set(df_ell_300k['smiles'].to_list()))\n",
        "mols_ell_300k = [ Chem.MolFromSmiles(s) for s in lst_ell_300k ]\n",
        "len(lst_ell_300k)"
      ],
      "metadata": {
        "colab": {
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      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "300000"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nUB_ell = {}\n",
        "for r in [2, 3, 4]:\n",
        "  UB = getUniqueBitsForMList(mols_ell_300k, r)\n",
        "  nUB_ell[r] = len(UB)\n",
        "  print(f'{len(UB)} unique bits for 300K Enamine Lead-like compounds {r} of Morgan fingerprint')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ETajHeeoFLPR",
        "outputId": "1c609806-e70b-4f8a-9d99-4714644e3dbc"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "89070 unique bits for 300K Enamine Lead-like compounds 2 of Morgan fingerprint\n",
            "596124 unique bits for 300K Enamine Lead-like compounds 3 of Morgan fingerprint\n",
            "1899777 unique bits for 300K Enamine Lead-like compounds 4 of Morgan fingerprint\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "nUB_ell_100K = {}\n",
        "for r in [2, 3, 4]:\n",
        "  UB = getUniqueBitsForMList(mols_ell_300k[:100000], r)\n",
        "  nUB_ell_100K[r] = len(UB)\n",
        "  print(f'{len(UB)} unique bits for 100K Enamine Lead-like compounds {r} of Morgan fingerprint')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lzuYZB2iNay-",
        "outputId": "4a81614e-4ff5-4797-dbfb-72083a9a9579"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "65627 unique bits for 100K Enamine Lead-like compounds 2 of Morgan fingerprint\n",
            "361353 unique bits for 100K Enamine Lead-like compounds 3 of Morgan fingerprint\n",
            "961158 unique bits for 100K Enamine Lead-like compounds 4 of Morgan fingerprint\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Stats"
      ],
      "metadata": {
        "id": "4JxEAMnGJLYc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "stats = pd.DataFrame({'Drugs (~5K)': nUB_drugs, \n",
        "                      'Enamine REAL Lead-like (100K)': nUB_ell_100K,\n",
        "                      'Enamine REAL Lead-like (300K)': nUB_ell})\n",
        "stats"
      ],
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      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Drugs (~5K)  Enamine REAL Lead-like (100K)  Enamine REAL Lead-like (300K)\n",
              "2        29201                          65627                          89070\n",
              "3        75912                         361353                         596124\n",
              "4       117811                         961158                        1899777"
            ],
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Drugs (~5K)</th>\n",
              "      <th>Enamine REAL Lead-like (100K)</th>\n",
              "      <th>Enamine REAL Lead-like (300K)</th>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>29201</td>\n",
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              "      <td>89070</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>75912</td>\n",
              "      <td>361353</td>\n",
              "      <td>596124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>117811</td>\n",
              "      <td>961158</td>\n",
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          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, as we can see, on average, for radius 3 there are 16 bits per compound for drug space that's going down 4 and 2 bits for increased chemical space of 100K and 300K diverse compounds from the Enamine REAL library. Enamine virtual space is, of course, repetitive, but it still indicates that with a large chemical space number of unique bits will grow up significantly to more than half of million bits. This raises a good old question about using folded descriptors for ML tasks, especially in a very large chemical space."
      ],
      "metadata": {
        "id": "xBxG3O5OTwOm"
      }
    }
  ]
}