A multitier computational strategy discovers potential USP7 inhibitors using similarity-based virtual screening, physics-driven molecular simulations, binding free energy estimations, binary QSAR modeling, and steered molecular dynamics analysis. This integrated approach prioritizes and optimizes lead compounds with strong predicted affinity, offering promising candidates for targeted USP7-based cancer therapy.

Ubiquitin-specific protease 7 (USP7) is a key deubiquitinating enzyme involved in tumor suppression, DNA repair, and epigenetic regulation. Given its critical role in cancer progression, USP7 has emerged as an attractive therapeutic target. Using similarity-based ligand screening, structurally related analogs of previously identified and validated hit compounds by our research group are selected and grid-based docking simulations are performed, prioritizing molecules with high binding affinity (docking scores < −8.0 kcal mol⁻¹). The top-ranked candidates are refined through long molecular dynamics (MD) simulations and MM/GBSA free energy calculations to assess their structural stability and interaction patterns with key USP7 residues. Binary QSAR analysis was further used to evaluate the anticancer potential of these compounds, retaining only those compounds with high predicted therapeutic activity values (normalized therapeutic activity value >0.5). Furthermore, to investigate the selectivity of the potent compounds, cross-docking is performed against multiple USP family members, demonstrating strong specificity for USP7 with minimal off-target effects. Finally, steered MD (sMD) simulations provide insights into the mechanical stability of ligand-protein interactions, revealing inhibitors that exhibit high resistance to unbinding forces and enhancing their therapeutic potential. This comprehensive computational workflow provides a rational approach for designing next-generation USP7 inhibitors and lays the foundation for future experimental validation.