Structure-based discovery of PARP15 inhibitors with enhanced binding energetics and stability via quantum refinement for targeted breast cancer therapy.

Poly(ADP-ribose) polymerase 15 (PARP15) has emerged as a promising yet underexplored target for BRCA-mutated breast cancer. A key strength of this study is the integration of quantum-level density functional theory (DFT) refinement with long-timescale molecular dynamics (MD) validation, an approach rarely applied together for PARP15 inhibitor discovery. Here, an integrated computational workflow combining structure-based virtual screening, DFT optimization, extended MD simulations (500 ns), MM/GBSA binding free energy estimation, and principal component and free energy landscape analyses was employed to identify potential inhibitors of the PARP15 catalytic domain from the Diverse-lib chemical library. Following DFT-based refinement and re-docking, three top-ranked candidates were subjected to long-timescale MD simulations to evaluate dynamic stability. Among them, compound 85267964(4-[2-[4-(3-chloro-4-fluorophenyl)sulfonylpiperazin-1-yl]-2-oxoethyl]-2H-phthalazin-1-one) exhibited comparatively favorable computational performance, characterized by stable RMSD profiles, persistent hydrogen bonding, reduced conformational fluctuations, and the most favorable MM/GBSA binding free energy (ΔG_total = -91.72 ± 9.16 kcal/mol). PCA and free energy landscape analyses further supported the presence of stable low-energy conformational states, and superimposition of MD-derived minima confirmed pose integrity. Overall, this study demonstrates the value of combining quantum chemical refinement with dynamic validation for PARP15 inhibitor prioritization and identifies 85267964 as a computationally prioritized candidate warranting further experimental investigation.