Multi-Pose Structural Evidence for LLM Agent Molecular Optimization in Structure-Based Drug Design

Structure based molecular optimization aims to refine candidate ligands with improved binding scores and molecular quality. Existing methods typically lack explicit modeling of interaction consistency across docking poses and verification of interaction changes after optimization. This paper proposes MEDD, an LLM agent framework for structural evidence guided molecular optimization. It aggregates protein ligand interactions across multiple docking poses into a unified structural evidence representation. This representation is then used to identify stable interactions and pose sensitive regions based on cross pose consistency. The LLM then formulates testable interaction hypotheses and translates them into constrained local molecular modifications. The resulting candidates are docked again to verify whether the hypothesized interaction changes are realized while key interactions are preserved. Experiments on CrossDocked2020 show that MEDD achieves improved docking scores while maintaining QED, SA, and MRR, and achieves a higher key interaction preservation rate and a higher proportion of structurally supported optimization hypotheses. These results support the use of explicit structural evidence for reliable and verifiable LLM guided molecular optimization.