Sulstice
commented
1 month ago Read this:
https://plotly.com/python/distplot/ https://sharifsuliman.medium.com/understanding-drug-likeness-filters-with-rdkit-and-exploring-the-withdrawn-database-ebd6b8b2921e https://www.rdkit.org/docs/index.html
Code to Process Rings in Drugs:
from rdkit import Chem
from rdkit.Chem import Descriptors
import progressbar
if __name__ == '__main__':
smiles_list = []
molecules = [ Chem.MolFromSmiles(smiles) for smiles in smiles_list ]
results = {
"Lipinski Rule of 5": 0,
"Ghose Filter": 0,
"Veber Filter": 0,
"Rule of 3 Filter": 0,
"REOS Filter": 0,
"Drug-like Filter": 0,
"Passes All Filters": 0,
}
print ("Molecule Database Length: " + str(len(molecules)))
for i in progressbar.progressbar(range(len(molecules))):
molecule = molecules[i]
if molecule:
lipinski = False
rule_of_3 = False
ghose_filter = False
veber_filter = False
reos_filter = False
drug_like_filter = False
molecular_weight = Descriptors.ExactMolWt(molecule)
logp = Descriptors.MolLogP(molecule)
h_bond_donor = Descriptors.NumHDonors(molecule)
h_bond_acceptors = Descriptors.NumHAcceptors(molecule)
rotatable_bonds = Descriptors.NumRotatableBonds(molecule)
number_of_atoms = Chem.rdchem.Mol.GetNumAtoms(molecule)
molar_refractivity = Chem.Crippen.MolMR(molecule)
topological_surface_area_mapping = Chem.QED.properties(molecule).PSA
formal_charge = Chem.rdmolops.GetFormalCharge(molecule)
heavy_atoms = Chem.rdchem.Mol.GetNumHeavyAtoms(molecule)
num_of_rings = Chem.rdMolDescriptors.CalcNumRings(molecule)
# Lipinski
if molecular_weight <= 500 and logp <= 5 and h_bond_donor <= 5 and h_bond_acceptors <= 5 and rotatable_bonds <= 5:
lipinski = True
results["Lipinski Rule of 5"] += 1
# Ghose Filter
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 Filter
if rotatable_bonds <= 10 and topological_surface_area_mapping <= 140:
veber_filter = True
results["Veber Filter"] += 1
# Rule of 3
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
# REOS Filter
if molecular_weight >= 200 and molecular_weight <= 500 and logp >= int(0 - 5) and logp <= 5 and h_bond_donor >= 0 and h_bond_donor <= 5 and h_bond_acceptors >= 0 and h_bond_acceptors <= 10 and formal_charge >= int(0-2) and formal_charge <= 2 and rotatable_bonds >= 0 and rotatable_bonds <= 8 and heavy_atoms >= 15 and heavy_atoms <= 50:
reos_filter = True
results["REOS Filter"] += 1
#Drug Like Filter
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
if lipinski and ghose_filter and veber_filter and rule_of_3 and reos_filter and drug_like_filter:
results["Passes All Filters"] += 1
print (results)
Lyq322
Hi,
In this study, we are taking the rings in drugs list and applying generative AI to rings in drugs to predict future most likely ring systems.
Read these two papers:
1.) https://arxiv.org/abs/1704.07555 2.) https://pubs.acs.org/doi/10.1021/jm4017625
Ask questions and ask some thoughts on you can apply these two together.