{"ID":2921986,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-02T05:59:20.873954421Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.00555","arxiv_id":"2606.00555","title":"Probe Before You Edit: Probing-Guided Molecular Optimization for LLM Agents in Structure-Based Drug Design","abstract":"Structure-based drug design increasingly employs LLM agents to iteratively refine ligands against a target pocket, yet a viable ligand must satisfy two often-conflicting objectives -- binding affinity and druggability -- which single optimization steps rarely improve together. To quantify this difficulty, we introduce two diagnostic metrics: the first measures how often a single edit improves both objectives, and the second measures how often a gain on one objective comes with a loss on the other. Applying these diagnostics to current LLM-agent pipelines exposes a consistent failure mode: the agent performs molecular editing without knowing how the pocket-ligand complex responds to local modifications, thus rarely achieving joint improvement. Inspired by medicinal chemists, who probe the pocket-ligand complex with controlled analog edits before choosing an optimization direction, we propose \\textbf{PROBE}, an optimization framework built around edit-response probing. PROBE first decomposes the ligand into editable sites and builds a pocket-specific \\textbf{site map} that flags where joint gains are plausible, where the two objectives are likely in tension, and where liability substructures should be changed; it then performs controlled probe edits whose responses are distilled into an \\textbf{EditManual}. Guided by the site map and EditManual, PROBE runs an iterative multi-agent loop in which an affinity agent, a druggability agent, and a co-optimization agent jointly produce edits. On the CrossDocked2020 benchmark, PROBE achieves state-of-the-art performance and substantially mitigates the failure modes exposed by our diagnostics metrics.","short_abstract":"Structure-based drug design increasingly employs LLM agents to iteratively refine ligands against a target pocket, yet a viable ligand must satisfy two often-conflicting objectives -- binding affinity and druggability -- which single optimization steps rarely improve together. To quantify this difficulty, we introduce...","url_abs":"https://arxiv.org/abs/2606.00555","url_pdf":"https://arxiv.org/pdf/2606.00555v1","authors":"[\"Zaifei Yang\",\"Weiyu Chen\",\"Yaqing Wang\",\"James Kwok\"]","published":"2026-05-30T06:06:51Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"q-bio.BM\"]","methods":"[\"Large Language Model\",\"Generative Adversarial Network\"]","has_code":false}