MOLT: A Self-Evolving Agentic System for Constrained Multi-Property Lead Optimization

Lead optimization is inherently iterative, yet current agentic molecular optimizers do not systematically improve from the experience they accumulate. Optimization history is often retained as tool calls or trajectories, leaving the chemical interventions associated with their outcomes difficult to reuse. We introduce MOLT, a self-evolving agentic system for constrained multi-property lead optimization. MOLT consolidates evaluated molecular transitions into executable chemical transformations with outcome evidence; a context-conditioned action policy selects among these transformations, and an LLM controller plans across retained optimization branches. Exploration expands the available chemical knowledge, while optimization decisions provide supervision for updating the action policy. Across five constrained lead-optimization tasks, MOLT achieves the highest success rate on every evaluated task, improving over prior lead-optimization agents on the three shared tasks and over generative baselines on both dual-target tasks.