Visualization

This tutorial will highligh the major viz related features of Datamol.

In [1]:

import datamol as dm

import datamol as dm

First let's get a dataset.

In [2]:

data = dm.read_csv(
    "https://raw.githubusercontent.com/rdkit/rdkit/master/Data/NCI/first_200.tpsa.csv",
    comment="#",
    header=None,
)
data.columns = ["smiles", "tpsa"]

# Create a mol column
with dm.without_rdkit_log():
    data["mol"] = data["smiles"].apply(dm.to_mol)

# Patch the dataframe to render the molecules in it
dm.render_mol_df(data)

data.iloc[0]["mol"]

data = dm.read_csv( "https://raw.githubusercontent.com/rdkit/rdkit/master/Data/NCI/first_200.tpsa.csv", comment="#", header=None, ) data.columns = ["smiles", "tpsa"] # Create a mol column with dm.without_rdkit_log(): data["mol"] = data["smiles"].apply(dm.to_mol) # Patch the dataframe to render the molecules in it dm.render_mol_df(data) data.iloc[0]["mol"]

Out[2]:

Now let's cluster the molecules and only keep the first cluster.

In [3]:

cluster_indices, cluster_mols = dm.cluster_mols(data["mol"].dropna().tolist(), cutoff=0.7)
mols = cluster_mols[1]

cluster_indices, cluster_mols = dm.cluster_mols(data["mol"].dropna().tolist(), cutoff=0.7) mols = cluster_mols[1]

Display the molecules of the cluster while aligning then using MCS. This can be done using a simple boolean flag in dm.to_image().

In [4]:

dm.to_image(mols, mol_size=(300, 200), align=True, use_svg=False)

dm.to_image(mols, mol_size=(300, 200), align=True, use_svg=False)

Out[4]:

In [ ]: