Design of pyrimidine-based scaffolds as potential anticancer agents for human DHFR: three-dimensional quantitative structure–activity relationship by docking derived grid-independent descriptors

Dihydrofolate reductase (DHFR) is one of the effective and attractive chemotherapeutic targets because of its importance in catalyzing the reduction of dihydrofolate to tetrahydrofolate, which has important function in the action of folate-dependent enzymes. In this study, a new hybrid docking alignment-independent three-dimensional quantitative structure–activity relationship (3D-QSAR) model was prepared and applied in order to predict biological activities in a series of pyrimidine-based scaffold inhibitors against the human DHFR target. Partial least squares (PLS) regression was implemented to correlate genetic algorithm selected-grind descriptors with biological activity. Several internal/external approaches were used to validate the model. Considering the good agreement of all calculated parameters with the acceptance criteria and the meaningful and interpretable results of the QSAR model (i.e., PLS coefficient), we have retrieved new hits using the Pharmit server. The most active compound in the data set was docked and imported to Pharmit server in order to screen the PubChem library through building a structure-based pharmacophore model. We applied some shape filters for protein and ligand as well as Lipinski’s rule of five, in order to filter the results generated by a virtual screening which was performed by pharmacophore search. Based on the virtual screening and molecular docking results, eventually, 44 compounds were retrieved. ADMET filtration was applied to validate the hit compounds. Based on the results obtained from the in silico prediction of ADMET properties, two hits were finally retrieved from the candidate compounds.

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