Agents on a Tree: Pathwise Coordination for Multi-Objective Molecular Optimization

Computer Science > Artificial Intelligence [Submitted on 27 Mar 2026] Agents on a Tree: Pathwise Coordination for Multi-Objective Molecular Optimization Jia Zhang, Tengfei Ma, Tianle Li, Daojian Zeng, Xieping Gao, Xiangxiang Zeng Multi-objective molecular optimization requires searching vast chemical spaces under conflicting objectives, where early design decisions strongly constrain downstream outcomes. Existing methods typically rely on a single policy or fixed scalarization, which limits their ability to represent diverse trade-offs and to explore multiple promising design trajectories. We propose ATOM, a multi-agent framework that formulates molecular optimization as a tree-structured search. Each node corresponds to an atomic operation and hosts an agent specialized for a particular objective or decision context. Agents coordinate along different paths of the tree rather than enforcing a global consensus, enabling the method to maintain and compare alternative molecular evolution trajectories. A global memory of past optimization behaviors further supports balanced exploration and exploitation across objectives. This tree-structured interaction enables reasoning over long-horizon dependencies inherent in molecular design. Experiments on challenging multi-objective benchmarks involving activity, synthesizability, and ADMET-related properties show that ATOM consistently achieves improved Pareto coverage and hypervolume over strong baselines. These results demonstrate the effectiveness of pathwise multi-agent coordination for molecular optimization. Code is available at this https URL. Comments: 17 pages, 6 figures Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2606.00008 [cs.AI]   (or arXiv:2606.00008v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2606.00008 Focus to learn more Submission history From: Jia Zhang [view email] [v1] Fri, 27 Mar 2026 14:21:21 UTC (2,175 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)