Drug Design Filters

Most common are drug filters in reducing a dataset of large SMILES down. These filters can be cumbersome to find, compare, and code. So we extended GlobalChemExtensions to cover that. We use RDKit to fetch the parameters of each molecule.

Imports

from global_chem_extensions import GlobalChemExtensions

cheminformatics = GlobalChemExtensions().cheminformatics()

Filter a Drug by one of the Filters

Code

gc.build_global_chem_network()
smiles_list = list(gc.get_node_smiles('emerging_perfluoroalkyls').values())

filtered_smiles = cheminformatics.filter_smiles_by_criteria(
    smiles_list,
    lipinski_rule_of_5=True,
    ghose=False,
    veber=False,
    rule_of_3=False,
    reos=False,
    drug_like=False,
    pass_all_filters=False
)

print (filtered_smiles)

Output

[
   'O=C(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F', 
   'O=C(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F',
   'O=S(=O)(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F',
   'O=S(=O)(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F',
   'FC(F)(F)C1(F)OC1(F)F', 
   'O=C(O)C(F)(F)C(F)(F)C(F)(F)F',
   'O=C(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F', 
   'O=S(=O)(O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F'
]

Algorithm

Lipinski

Rules

Lipinski:
    Moleculer Weight <= 500
    LogP <= 5
    H-Bond Donor Count <= 5
    H-Bond Acceptor Count <= 10

Implementation

if molecular_weight <= 500 and logp <= 5 and h_bond_donor <= 5 and h_bond_acceptors <= 10and rotatable_bonds <= 5:
    lipinski = True
    results["Lipinski Rule of 5"] += 1

Ghose

Rules

Ghose:
    Molecular weight between 160 and 480
    LogP between -0.4 and +5.6
    Atom count between 20 and 70
    Molar refractivity between 40 and 130

Implementation

if molecular_weight >= 160 and molecular_weight <= 480 and logp >= -0.4 and logp <= 5.6 and number_of_atoms >= 20 and number_of_atoms <= 70 and molar_refractivity >= 40 and molar_refractivity <= 130:
    ghose_filter = True
    results["Ghose Filter"] += 1

Veber

Rules

Veber:
    Rotatable bonds <= 10
    Topological polar surface area <= 140

Implementation

if rotatable_bonds <= 10 and topological_surface_area_mapping <= 140:
    veber_filter = True
    results["Veber Filter"] += 1

Rule of 3

Rules

Rule of 3:
    Molecular weight <= 300
    LogP <= 3
    H-bond donor <= 3
    H-bond acceptor count <= 3
    Rotatable bond count <= 3

Implementation

if molecular_weight <= 300 and logp <= 3 and h_bond_donor <= 3 and h_bond_acceptors <= 3 and rotatable_bonds <= 3:
    rule_of_3 = True
    results["Rule of 3 Filter"] += 1

Drug-Like (QED)

Rules

mass < 400
ring count > 0
rotatable bond count < 5
h-bond donor count <= 5
h-bond acceptor count <= 10
logP < 5

Implementation

if molecular_weight < 400 and num_of_rings > 0 and rotatable_bonds < 5 and h_bond_donor <= 5 and h_bond_acceptors <= 10 and logp < 5:
    drug_like_filter = True
    results["Drug-like Filter"] += 1

For a more detailed look, please visit this blog for an application on the WITHDRAWN database: