A combined structure-based pharmacophore modeling and 3D-QSAR study on a series of N-heterocyclic scaffolds to screen novel antagonists as human DHFR inhibitors

Abstract

Dihydrofolate reductase (DHFR) is an essential enzyme that participates in folate metabolism and purine and thymidylate synthesis in cell proliferation. It converts dihydrofolate (DHF) to tetrahydrofolate (THF) in the presence of nicotinamide adenine dinucleotide phosphate (NADPH). This enzyme is found within all organisms adjusting the cellular level of THF. Negligible DHFR activity leads to a deficiency of THF and cell death. This trait is used to inhibit the cancer cells. DHFR has been extensively studied as the therapeutic target for cancer treatment. Accordingly, there is an urgent need for the identification and development of novel effective inhibitors with higher selectivity, lower toxicity, and better potency than currently available drugs. Hence, we aimed to identify new compounds by utilizing the alignment-independent three-dimensional quantitative structure-activity relationship (3D-QSAR) and structure-based pharmacophore modeling. Using the results obtained from these approaches, several antagonists have been retrieved from the virtual screening by applying some filters such as Lipinski’s rule of five. Selected compounds were then docked into the binding site of the receptor for identification of ligand-receptor interactions, binding affinity prediction, and refinement based on GoldScore fitness values. Eventually, pharmacokinetic/drug-likeness features and toxicity profiles of novel compounds were predicted and evaluated. Four hits with PubChem CIDs of 136138676, 94182663, 60219817, and 133300845 were finally proposed as new candidates with potential inhibitory activity against human DHFR.