Agents on a Tree: Pathwise Coordination for Multi-Objective Molecular Optimization
Researchers have introduced ATOM, a novel multi-agent framework that addresses the complex challenge of multi-objective molecular optimization. By utilizing a tree-structured search and pathwise coordination, ATOM outperforms traditional methods in discovering optimal chemical structures.
Computer Science > Artificial Intelligence
Title:Agents on a Tree: Pathwise Coordination for Multi-Objective Molecular Optimization
View PDF HTML (experimental)Abstract: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.
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Source: arXiv cs.AI Recent
