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

Algorithm

Lipinski

Rules

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

Implementation

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

Veber

Rules

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

Implementation

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

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

For a more detailed look, please visit this blog for an application on the WITHDRAWN database: