Agents on a Tree: Pathwise Coordination for Multi-Objective Molecular Optimization
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, ena...
| Authors | Jia Zhang, Tengfei Ma, Tianle Li et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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