Functional API¶

The functional API of medchem provides an easy and uniform way to access most the medchem alerts, filters and rules proposed in the other medchem module.

In [3]:

import datamol as dm
import pandas as pd

import medchem as mc

import datamol as dm import pandas as pd import medchem as mc

Generic filters¶

All the filters can be applied on a list of SMILES or molecule objects. It's also possible to run the filtering in parallel in processes or threads.

The below examples contains molecules that are shown for illustration purposes.

macrocycle_filter¶

Find molecules that do not infringe the strict maximum cycle size.

In [4]:

smiles_list = [
    "Nc1noc2cc(-c3noc(C(F)(F)F)n3)ccc12",
    "Nc1noc2cc(-c3noc(n3)C(Cl)(Cl)Cl)c(Cl)c(B3C=C3)c12",
    "C[C@H](Nc1ncc(-c2noc(C(F)(F)F)n2)cc1Cl)c1ccncc1",
    "NC1=NOC2=CC(C3=NOC(=N3)C(F)(F)F)=C3CCCCCCCC3=C12",
    "Nc1noc2c(cc(cc12)C1NCO1)C1CCCCCCCCC1",
]
mols = [dm.to_mol(s) for s in smiles_list]

# Apply the filter
out = mc.functional.macrocycle_filter(mols, max_cycle_size=7, return_idx=False)

legends = [f"Pass the filter={o}" for o in out]
dm.to_image(mols, legends=legends, n_cols=5, mol_size=(250, 150))

smiles_list = [ "Nc1noc2cc(-c3noc(C(F)(F)F)n3)ccc12", "Nc1noc2cc(-c3noc(n3)C(Cl)(Cl)Cl)c(Cl)c(B3C=C3)c12", "C[C@H](Nc1ncc(-c2noc(C(F)(F)F)n2)cc1Cl)c1ccncc1", "NC1=NOC2=CC(C3=NOC(=N3)C(F)(F)F)=C3CCCCCCCC3=C12", "Nc1noc2c(cc(cc12)C1NCO1)C1CCCCCCCCC1", ] mols = [dm.to_mol(s) for s in smiles_list] # Apply the filter out = mc.functional.macrocycle_filter(mols, max_cycle_size=7, return_idx=False) legends = [f"Pass the filter={o}" for o in out] dm.to_image(mols, legends=legends, n_cols=5, mol_size=(250, 150))

Out[4]:

atom_list_filter¶

Find molecules without any atom from a set of unwanted atomic symbols and with all atoms in the set of wanted atom list.

In [5]:

smiles_list = [
    "Nc1noc2cc(-c3noc(C(F)(F)F)n3)ccc12",
    "Nc1noc2cc(-c3noc(n3)C(Cl)(Cl)Cl)c(Cl)c(B3C=C3)c12",
    "C[C@H](Nc1ncc(-c2noc(C(F)(F)F)n2)cc1Cl)c1ccncc1",
    "NC1=NOC2=CC(C3=NOC(=N3)C(F)(F)F)=C3CCCCCCCC3=C12",
    "Nc1noc2c(cc(cc12)C1NCO1)C1CCCCCCCCC1",
]
mols = [dm.to_mol(s) for s in smiles_list]

# Apply the filter
out = mc.functional.atom_list_filter(mols, unwanted_atom_list=["B"], return_idx=False)

legends = [f"Pass the filter={o}" for o in out]
dm.to_image(mols, legends=legends, n_cols=5, mol_size=(250, 150))

smiles_list = [ "Nc1noc2cc(-c3noc(C(F)(F)F)n3)ccc12", "Nc1noc2cc(-c3noc(n3)C(Cl)(Cl)Cl)c(Cl)c(B3C=C3)c12", "C[C@H](Nc1ncc(-c2noc(C(F)(F)F)n2)cc1Cl)c1ccncc1", "NC1=NOC2=CC(C3=NOC(=N3)C(F)(F)F)=C3CCCCCCCC3=C12", "Nc1noc2c(cc(cc12)C1NCO1)C1CCCCCCCCC1", ] mols = [dm.to_mol(s) for s in smiles_list] # Apply the filter out = mc.functional.atom_list_filter(mols, unwanted_atom_list=["B"], return_idx=False) legends = [f"Pass the filter={o}" for o in out] dm.to_image(mols, legends=legends, n_cols=5, mol_size=(250, 150))

Out[5]:

ring_infraction_filter¶

Find molecules that have a ring infraction filter. This filter focuses on checking for rings that are too small to have an heteroatom.

In [6]:

smiles_list = [
    "Nc1noc2cc(-c3noc(C(F)(F)F)n3)ccc12",
    "Nc1noc2cc(-c3noc(n3)C(Cl)(Cl)Cl)c(Cl)c(B3C=C3)c12",
    "C[C@H](Nc1ncc(-c2noc(C(F)(F)F)n2)cc1Cl)c1ccncc1",
    "NC1=NOC2=CC(C3=NOC(=N3)C(F)(F)F)=C3CCCCCCCC3=C12",
    "Nc1noc2c(cc(cc12)C1NCO1)C1CCCCCCCCC1",
]
mols = [dm.to_mol(s) for s in smiles_list]

# Apply the filter
out = mc.functional.ring_infraction_filter(mols, hetcycle_min_size=4, return_idx=False)

legends = [f"Pass the filter={o}" for o in out]
dm.to_image(mols, legends=legends, n_cols=5, mol_size=(250, 150))

smiles_list = [ "Nc1noc2cc(-c3noc(C(F)(F)F)n3)ccc12", "Nc1noc2cc(-c3noc(n3)C(Cl)(Cl)Cl)c(Cl)c(B3C=C3)c12", "C[C@H](Nc1ncc(-c2noc(C(F)(F)F)n2)cc1Cl)c1ccncc1", "NC1=NOC2=CC(C3=NOC(=N3)C(F)(F)F)=C3CCCCCCCC3=C12", "Nc1noc2c(cc(cc12)C1NCO1)C1CCCCCCCCC1", ] mols = [dm.to_mol(s) for s in smiles_list] # Apply the filter out = mc.functional.ring_infraction_filter(mols, hetcycle_min_size=4, return_idx=False) legends = [f"Pass the filter={o}" for o in out] dm.to_image(mols, legends=legends, n_cols=5, mol_size=(250, 150))

Out[6]: