Code for generating a visualisation of Drugbank. The drugbank data has to be downloaded separately from https://www.drugbank.ca.

drugbank.py

import numpy as np
import pandas as pd
import tmap as tm
import scipy.stats as ss
from faerun import Faerun
from rdkit.Chem import AllChem, Descriptors, Descriptors3D
from mhfp.encoder import MHFPEncoder
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from lipinski import lipinski_pass


def main():
    """ The main function """
    df = pd.read_csv("drugbank.csv").dropna(subset=["SMILES"]).reset_index(drop=True)
    enc = MHFPEncoder()
    lf = tm.LSHForest(2048, 128)

    fps = []
    labels = []
    groups = []
    tpsa = []
    logp = []
    mw = []
    h_acceptors = []
    h_donors = []
    ring_count = []
    is_lipinski = []
    has_coc = []
    has_sa = []
    has_tz = []

    substruct_coc = AllChem.MolFromSmiles("COC")
    substruct_sa = AllChem.MolFromSmiles("NS(=O)=O")
    substruct_tz = AllChem.MolFromSmiles("N1N=NN=C1")

    total = len(df)
    for i, row in df.iterrows():
        if i % 1000 == 0 and i > 0:
            print(f"{round(100 * (i / total))}% done ...")

        smiles = row[6]
        mol = AllChem.MolFromSmiles(smiles)

        if mol and mol.GetNumAtoms() > 5 and smiles.count(".") < 2:
            fps.append(tm.VectorUint(enc.encode_mol(mol, min_radius=0)))
            labels.append(
                f'{smiles}__<a href="https://www.drugbank.ca/drugs/{row[0]}" target="_blank">{row[0]}</a>__{row[1]}'.replace(
                    "'", ""
                )
            )
            groups.append(row[3].split(";")[0])
            tpsa.append(Descriptors.TPSA(mol))
            logp.append(Descriptors.MolLogP(mol))
            mw.append(Descriptors.MolWt(mol))
            h_acceptors.append(Descriptors.NumHAcceptors(mol))
            h_donors.append(Descriptors.NumHDonors(mol))
            ring_count.append(Descriptors.RingCount(mol))
            is_lipinski.append(lipinski_pass(mol))
            has_coc.append(mol.HasSubstructMatch(substruct_coc))
            has_sa.append(mol.HasSubstructMatch(substruct_sa))
            has_tz.append(mol.HasSubstructMatch(substruct_tz))

    # Create the labels and the integer encoded array for the groups,
    # as they're categorical
    labels_groups, groups = Faerun.create_categories(groups)
    tpsa_ranked = ss.rankdata(np.array(tpsa) / max(tpsa)) / len(tpsa)
    logp_ranked = ss.rankdata(np.array(logp) / max(logp)) / len(logp)
    mw_ranked = ss.rankdata(np.array(mw) / max(mw)) / len(mw)
    h_acceptors_ranked = ss.rankdata(np.array(h_acceptors) / max(h_acceptors)) / len(
        h_acceptors
    )
    h_donors_ranked = ss.rankdata(np.array(h_donors) / max(h_donors)) / len(h_donors)
    ring_count_ranked = ss.rankdata(np.array(ring_count) / max(ring_count)) / len(
        ring_count
    )

    lf.batch_add(fps)
    lf.index()
    cfg = tm.LayoutConfiguration()
    cfg.k = 100
    # cfg.sl_extra_scaling_steps = 1
    cfg.sl_repeats = 2
    cfg.mmm_repeats = 2
    cfg.node_size = 2
    x, y, s, t, _ = tm.layout_from_lsh_forest(lf, config=cfg)

    # Define a colormap highlighting approved vs non-approved
    custom_cmap = ListedColormap(
        ["#2ecc71", "#9b59b6", "#ecf0f1", "#e74c3c", "#e67e22", "#f1c40f", "#95a5a6"],
        name="custom",
    )

    bin_cmap = ListedColormap(["#e74c3c", "#2ecc71"], name="bin_cmap")

    f = Faerun(
        clear_color="#222222",
        coords=False,
        view="front",
        impress='made with <a href="http://tmap.gdb.tools" target="_blank">tmap</a><br />and <a href="https://github.com/reymond-group/faerun-python" target="_blank">faerun</a>',
    )

    f.add_scatter(
        "Drugbank",
        {
            "x": x,
            "y": y,
            "c": [
                groups,
                is_lipinski,
                has_coc,
                has_sa,
                has_tz,
                tpsa_ranked,
                logp_ranked,
                mw_ranked,
                h_acceptors_ranked,
                h_donors_ranked,
                ring_count_ranked,
            ],
            "labels": labels,
        },
        shader="smoothCircle",
        colormap=[
            custom_cmap,
            bin_cmap,
            bin_cmap,
            bin_cmap,
            bin_cmap,
            "viridis",
            "viridis",
            "viridis",
            "viridis",
            "viridis",
            "viridis",
        ],
        point_scale=2.5,
        categorical=[True, True, True, True, True, False, False, False, False, False],
        has_legend=True,
        legend_labels=[
            labels_groups,
            [(0, "No"), (1, "Yes")],
            [(0, "No"), (1, "Yes")],
            [(0, "No"), (1, "Yes")],
            [(0, "No"), (1, "Yes")],
        ],
        selected_labels=["SMILES", "Drugbank ID", "Name"],
        series_title=[
            "Group",
            "Lipinski",
            "Ethers",
            "Sulfonamides",
            "Tetrazoles",
            "TPSA",
            "logP",
            "Mol Weight",
            "H Acceptors",
            "H Donors",
            "Ring Count",
        ],
        max_legend_label=[
            None,
            None,
            None,
            None,
            None,
            str(round(max(tpsa))),
            str(round(max(logp))),
            str(round(max(mw))),
            str(round(max(h_acceptors))),
            str(round(max(h_donors))),
            str(round(max(ring_count))),
        ],
        min_legend_label=[
            None,
            None,
            None,
            None,
            None,
            str(round(min(tpsa))),
            str(round(min(logp))),
            str(round(min(mw))),
            str(round(min(h_acceptors))),
            str(round(min(h_donors))),
            str(round(min(ring_count))),
        ],
        title_index=2,
        legend_title="",
    )

    f.add_tree("drugbanktree", {"from": s, "to": t}, point_helper="Drugbank")

    f.plot("drugbank", template="smiles")


if __name__ == "__main__":
    main()