DualEvo: Chemical-Space-Aware Online Evolution for Dual-Target Molecular Generation

Dual-target molecular generation seeks molecules that satisfy activity objectives for two biological targets while maintaining useful exploration of chemical space. Existing generative and feedback-learning methods can enrich high-value candidates, but continual generation also requires deciding how evaluated experience is accumulated and which structures are reused for updating. We introduce DualEvo, a chemical-space-controlled online evolution framework that combines fixed dual-target evaluation, cumulative experience archiving, reward-guided filtering, scaffold-balanced selection, and lightweight LoRA updates. Reward ranks candidate value, whereas scaffold balancing controls the structural composition of the high-value update set. Across six tasks, DualEvo attains the highest six-task mean unique predicted-success yield at the final 500-sample readout; relative to Reward-only, per-task means are higher on five of six tasks and successful-scaffold coverage is broader. Larger fixed-checkpoint readouts show more distinct successful molecules and scaffolds despite a lower successful-event frequency. Saved LoRA states can be reused for independent sampling and as fixed proposal models in downstream search.