Probe Before You Edit: Probing-Guided Molecular Optimization for LLM Agents in Structure-Based Drug Design

2026-05-30

AI Summary

This work addresses the challenge in structure-based drug design where existing large language model (LLM) agents struggle to simultaneously optimize ligand binding affinity and drug-likeness within a single editing step. To overcome this limitation, the authors propose the PROBE framework, which introduces a novel “edit-response probing” mechanism. By conducting controlled probe experiments on ligand–pocket complexes, PROBE constructs pocket-specific site maps and an EditManual that guide a multi-agent system—comprising affinity-, drug-likeness-, and co-optimization-focused agents—to collaboratively explore the feasible edit space. This approach effectively identifies regions amenable to joint optimization of both objectives, thereby breaking through the single-step editing bottleneck. Evaluated on the CrossDocked2020 benchmark, PROBE achieves state-of-the-art performance and substantially mitigates the optimization failure commonly observed in current methods.

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.

Problem

• molecular optimization
• binding affinity
• druggability
• structure-based drug design
• LLM agents

Innovation

• probe-guided optimization
• molecular editing
• structure-based drug design
• multi-agent LLM
• druggability-affinity trade-off