Probe Before You Edit: Probing-Guided Molecular Optimization for LLM Agents in Structure-Based Drug Design
On the map: LLM agents and tool use → Medical and scientific domain agents
Abstract (arXiv)
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 PROBE, an optimization framework built around edit-response probing. PROBE first decomposes the ligand into editable sites and builds a pocket-specific 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 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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 14 of its 49 references that are in the map. First posted on arXiv 30 May 2026.
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14 of 49 references.
References · 14
- 2022Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pocketscited by 183
- 20233D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Predictioncited by 177
- 2022A 3D Generative Model for Structure-Based Drug Designcited by 149
- 2024DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Designcited by 88
- 2024MolCRAFT: Structure-Based Drug Design in Continuous Parameter Spacecited by 48
- 2022Structure-based drug design with geometric deep learningcited by 40
- 2023Learning Subpocket Prototypes for Generalizable Structure-based Drug Designcited by 25
- 2025LIDDIA: Language-based Intelligent Drug Discovery Agentcited by 24
- 2024TAGMol: Target-Aware Gradient-guided Molecule Generationcited by 12
- 2024Decomposed Direct Preference Optimization for Structure-Based Drug Designcited by 7
- 2024Empower Structure-Based Molecule Optimization with Gradient Guided Bayesian Flow Networkscited by 6
- 2025Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedulecited by 6
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- 2024AgentDrug: Utilizing Large Language Models in An Agentic Workflow for Zero-Shot Molecular Editing
- 2026From Benchmark to Bench: Can Agents Survive Real-World Drug Discovery?
- 2026Molecular Lead Optimization via Agentic Tool Planning
- 2026LLM-Guided Dynamic Action Spaces for Synthesizable Molecular Optimization
- 2026ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery
- 2026M3OS: A Monte Carlo Graph Search-Orchestrated Multi-Agent LLM System for Evidence-Traced Molecular Optimization