Probabilistic Evaluation of Induced-Fit Effects in Fragment-Based Drug Optimization

Fragment-based lead optimization often involves relatively small chemical modifications, yet these changes can induce substantial rearrangements in ligand and receptor conformations and alter the preferred bound state among competing conformational minima. Accurately capturing and evaluating these ligand-dependent structural responses therefore remains a central challenge in structure-guided optimization. Here, we present a generation-and-evaluation framework for the probabilistic evaluation of a given bound structure. It first generates an ensemble of conformations around the input using flow matching and then evaluates their conditional log-probability scores. The generator learns to softly restrain scaffold positions near the initial ligand-fragment positions, focusing on sampling around the known binding context while allowing ligand and pocket conformations to adapt. Benchmarking on lead optimization tasks demonstrates that our generator provides more robust sampling than existing methods, producing valid and diverse conformations while accurately reproducing key interactions and induced-fit effects observed in experimental structures. The evaluator shows a meaningful correlation with the activity trends of congeneric ligand series without explicit affinity supervision of the generative model. Overall, these results suggest that the proposed strategy can serve as a competitive evaluation module in structure-based drug optimization workflows, replacing or complementing widely used docking and binding-affinity estimation methods.