Molecular Lead Optimization via Agentic Tool Planning
TRACE is a trajectory-aware LLM-reasoning agent for molecular lead optimization that treats tool selection as a sequential decision-making problem, enabling forward-looking structural refinement under constraints, achieving higher success rates and property improvements on ADMET tasks.
Article intelligence
Key points
- TRACE formulates tool selection as sequential decision making over action trajectories.
- It enables trajectory-aware decisions to improve ADMET properties while preserving molecular similarity.
- Experiments show superior optimization success, property improvements, and validity over baselines.
Why it matters
This matters because TRACE formulates tool selection as sequential decision making over action trajectories.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.28862] Molecular Lead Optimization via Agentic Tool Planning
[Submitted on 21 May 2026]
Title:Molecular Lead Optimization via Agentic Tool Planning
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Abstract:Drug discovery is a lengthy and resource-intensive process composed of multiple stages. Among these stages, lead optimization plays a critical role in transforming early hit compounds into viable drug candidates. This stage requires improving ADMET-related properties through subtle structural refinement while preserving key molecular substructures responsible for binding affinity to disease targets. Recent advances in artificial intelligence have shown promise in accelerating various aspects of drug discovery; however, most existing approaches to lead optimization rely on one-step molecular optimization, which fail to account for the long-term consequences of sequential design decisions. To address this limitation, we propose TRACE, a trajectory-aware, LLM-reasoning agent for molecular lead optimization that formulates tool selection as a sequential decision-making problem over action trajectories. Given a lead molecule and an optimization objective, TRACE makes trajectory-aware decisions over molecular optimization tools, enabling forward-looking refinement under structural constraints. Experiments on multiple ADMET optimization tasks show that our agent achieves higher optimization success, larger property improvements, and higher validity, while preserving molecular similarity compared to baseline models.
Comments: 12 pages
Subjects:
Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
Cite as: arXiv:2605.28862 [cs.LG]
(or arXiv:2605.28862v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.28862
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Lingxiao Li [view email] [v1] Thu, 21 May 2026 19:12:19 UTC (7,825 KB)
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