Poly(ADP-ribose) polymerase 1 (PARP-1) is vital for DNA repair. A computational workflow integrating AI-driven virtual screening (TransFoxMol, KarmaDock, AutoDock Vina) and molecular dynamics identified novel PARP-1 inhibitors with promising efficacy and minimized side effects. Our approach offers systematic insights for advancing precision oncology drug discovery and clinical research

Abstract

Despite the promising anticancer properties of PARP-1 inhibitors, their clinical use is hindered by side effects. It is crucial to explore new structural variants of these inhibitors to increase efficacy and minimize side effects, enhancing their clinical viability and therapeutic scope. In this study, we developed a virtual screening workflow that synergistically integrates the capabilities of TransFoxMol, KarmaDock, and AutoDock Vina. This workflow not only streamlines the identification of potential inhibitors but also ensures a systematic approach to prioritizing candidates. Through structural clustering, we identified ten promising PARP-1 inhibitors. Additionally, molecular dynamics simulations and MM/PBSA were employed to elucidate the binding modes of compounds 1, 3, 6, and 9 with PARP-1, providing valuable insights into their interaction mechanisms and supporting future drug development efforts. This workflow serves as a versatile tool for early-stage drug discovery, offering a strategic foundation for the rational design of new PARP-1 inhibitors.