UMO-Agent: Uncertainty-Guided Molecular Optimization Agent for Drug Discovery

Molecular optimization against a protein target is an important step in drug discovery, seeking to improve an initial ligand's binding affinity while maintaining drug-likeness, synthetic accessibility, and structural feasibility. We focus on fragment-based optimization, which enables controlled molecular refinement through modular chemical edits. However, the problem poses several technical challenges, including competing objectives, reward hacking, uncertain evaluation feedback, and combinatorial search. Earlier techniques primarily employ heuristic or evolutionary search, which requires careful operator and objective design, or reinforcement learning, which introduces additional policy-training costs and difficulties. Recent LLM-based agents support molecular optimization through tool use and iterative feedback, motivating mechanisms that limit the propagation of unreliable evaluations through molecular edits and accumulated experience. To fill this gap, we introduce UMO-Agent, a training-free LLM-based agent that integrates predictive uncertainty into the control of iterative fragment-based optimization. The agent combines Monte Carlo dropout estimates from a neural affinity predictor with docking consistency, interaction evidence, and molecular-property assessments. Its central design separates candidate screening from acceptance and links the resulting reliability assessment to memory updates and subsequent fragment search. We evaluate UMO-Agent on the CrossDocked2020 benchmark, and the results show that it achieves an average joint Success Rate of 60% across three independent runs, compared with approximately 35% reported for the agent-based baseline CIDD under its original evaluation protocol.