Multi-Objective Molecular Generation via Actor-Critic Regulated by Adaptive Cooperative Evolution

Many complex diseases involve intricate and interdependent biological mechanisms, making multi-target drug design therapeutically important. However, existing molecular generation models often struggle to balance conflicting property requirements, maintain structural exploration, and avoid premature convergence during reinforcement learning. To address these challenges, we propose CMIR-RL, a reinforcement learning-based molecular generation model that integrates cooperative crossover-mutation exploration, structure-based intrinsic rewards, and Pareto-based multi-objective optimization. In CMIR-RL, mutation and crossover networks synergistically guide the agent to balance exploration and exploitation, while Pareto-front-guided reward assignment updates the generation policy under multiple property constraints. Experiments on a three-objective polypharmacology task show that CMIR-RL significantly improves desirability from 0.8868 to 0.9542 over the baseline method, while maintaining competitive validity, uniqueness, and diversity. Additional GuacaMol results further indicate competitive generalization across goal-directed molecular generation tasks. These results show that CMIR-RL is an effective reinforcement learning strategy for early-stage multi-objective molecular design.