Computational and Structure-Based Insights into Poly (ADP-Ribose) Polymerase (PARP) Inhibitors for Cancer Therapy

Abstract

Poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi) have become a cornerstone of precision oncology by exploiting synthetic lethality in tumors with homologous recombination deficiency, particularly those harboring BRCA1/2 mutations. Despite substantial clinical success, the long-term effectiveness of PARPi is limited by acquired drug resistance, hematological toxicity, and challenges in achieving isoform selectivity. This narrative review aims to critically synthesize current knowledge on the structural mechanisms of PARP inhibition, pharmacological diversity among clinically relevant PARPi, emerging resistance mechanisms, and the contribution of computational approaches to next-generation inhibitor development. Current evidence indicates that PARPi exert their antitumor effects through both catalytic inhibition of PARylation and PARP trapping, with clinically approved agents, including Olaparib, Rucaparib, Niraparib, and Talazoparib, exhibiting distinct trapping potency, selectivity, toxicity, and therapeutic applications. Emerging resistance mechanisms, including homologous recombination restoration, replication fork stabilization, altered PARP1 trapping, and drug efflux, have shifted drug development toward mechanism-informed therapeutic design. In this context, structure-based computational approaches—including molecular docking, molecular dynamics simulations, pharmacophore modeling, quantitative structure–activity relationship (QSAR), and artificial intelligence-assisted drug design—have become integral to identifying novel scaffolds, improving PARP1 selectivity, optimizing PARP-trapping properties, and prioritizing compounds for experimental validation. Collectively, these advances indicate that integrating structural biology, computational drug design, and resistance biology is essential for developing safer, more selective, and resistance-resilient PARP inhibitors, thereby supporting the next generation of precision cancer therapeutics.