Computational design of PARP-1 inhibitors: QSAR, molecular docking, virtual screening, ADMET, and molecular dynamics simulations for targeted drug development.

Abstract

Poly (ADP-ribose) polymerase-1 (PARP-1) inhibitors have shown promise in treating various cancers with homologous recombination repair deficiencies, particularly in breast and ovarian cancers harbouring BRCA1/2 mutations. This study aimed to identify and optimize novel PARP-1 inhibitors using the phthalazinone scaffold, known for forming strong and selective interactions with the active site of PARP-1. Through a combination of Quantitative Structure-Activity Relationship (QSAR) modelling, molecular docking simulations, and virtual screening, we discovered compounds with significant anticancer potential. Both the Multiple Linear Regression (MLR) and Support Vector Machines (SVM) models, utilizing four selected molecular descriptors, demonstrated high predictive efficiency for inhibitory activity (MLR: r²  = 0.944, Q²_cv (cross-validated correlation coefficient) = 0.921, root mean square error (RMSE) = 0.249; SVM: r²  = 0.947, Q²_cv = 0.887, RMSE = 0.245). Molecular docking studies revealed that several new compounds exhibited strong interactions with key amino acids GLY 227A, MET 229A, PHE 230A, and TYR 246A within the PARP-1 active site, similar to those observed in reference inhibitors Olaparib and AZD2461. Then, the top-ranked compound's (3a) ligand-protein complex underwent a 200 ns molecular dynamics (MD) simulation, confirming stable binding and revealing a robust set of intermolecular interactions maintained under physiological conditions.