Structure-Based Discovery of Novel PARP1 Inhibitor Candidates Using Pharmacophore Modeling, Virtual Screening, Molecular Docking, and ADMET Profiling

Abstract

Poly(ADP-ribose) polymerase 1 (PARP1) is a recognized target for therapy in oncology, especially in cancers with defective homologous recombination repair. Although PARP inhibitors like olaparib have shown clinical effectiveness, the emergence of acquired resistance and associated side effects persistently drive the quest for novel chemical agents. This research utilized a comprehensive structure-based computational method to discover possible PARP1 inhibitors, with olaparib serving as the reference ligand. A pharmacophore model with five features, including three aromatic elements, one hydrogen-bond donor, and one hydrogen-bond acceptor, was created from the crystal structure of the PARP1–olaparib complex (PDB ID: 7KK4) and applied for virtual screening. Chosen compounds were subsequently assessed through molecular docking via MOE 2024, followed by the prediction of their physicochemical, pharmacokinetic, drug-likeness, medicinal chemistry, and toxicity profiles using SwissADME and ProTox 3.0. Re-docking olaparib to validate the docking protocol resulted in an RMSD of 0.992 Å, confirming the dependability of the chosen docking method. Four compounds demonstrated advantageous binding scores between −8.978 and −9.715 kcal/mol, all surpassing that of olaparib (−8.645 kcal/mol). Compound III exhibited the best docking score (−9.715 kcal/mol) and formed several interactions with crucial PARP1 residues, such as Gly863, Tyr896, Ser904, and Tyr907. Each of the four compounds met Lipinski's rule of five and exhibited a predicted bioavailability score of 0.55; however, variations were noted in solubility, CYP inhibition, medicinal chemistry alerts, and anticipated toxicity. The results indicate that Compound III is the most potent binding candidate among the examined series, while also pointing out weaknesses that need structural improvements. These results offer a computational foundation for additional molecular dynamics, binding free-energy assessments, and experimental verification of the discovered PARP1 inhibitor candidates..