Divergent Evolution Paths for Multi-Objective Molecular Optimization

Multi-objective molecular optimization is fundamentally constrained not only by the vastness of chemical space, but by the intrinsic conflict among objectives: modifications that improve one property often degrade others. Existing approaches typically optimize molecules within a single evolving search context, which entangles conflicting objectives and discards the path-specific experience needed to reach distinct Pareto regions. We propose ATOM (Agents on a Tree for multi-Objective Molecular optimization), a trajectory-centric framework that treats alternative optimization histories, rather than individual molecular candidates alone, as the units of Pareto exploration. Instead of enforcing global consensus, ATOM organizes specialized agents along different branches of a tree-structured search, where each agent evolves molecules toward a particular objective while accumulating trajectory-specific structural experience. To support structured knowledge sharing, ATOM incorporates three complementary coordination mechanisms operating at lateral, hierarchical, and global scales, enabling progressive cross-path optimization. Experiments on molecular optimization benchmarks involving predicted bioactivity, synthetic accessibility, and CYP3A4 non-inhibition show that ATOM attains the best mean aggregate hypervolume and competitive Pareto coverage, with gains more pronounced under severe inter-objective conflict. These results suggest that divergent evolution provides an effective paradigm for complex multi-objective molecular design. Code is available at https://anonymous.4open.science/r/ATOM-41CE.