CovMOLAgent: Execution-Grounded Policy Learning for Multi-Objective Covalent Molecule Optimization

Large language models (LLMs) have shown considerable promise in chemistry, yet existing paradigms mainly focus on the design and single-objective optimization of chemical molecules. Multi-objective optimization of a given covalent molecule presents two challenges. First, tool availability does not equate to strategic tool use: agents struggle to recover from failed edits or decide when to stop, resulting in brittle search trajectories. Second, multi-objective search tends to improve molecular properties at the cost of destroying the key warhead that determines the covalent mechanism, while heterogeneous property scales make coordinated improvement difficult. To address these challenges, we propose Covalent Multi-Objective Lead Optimization Agent (CovMOLAgent), an agentic framework for multi-objective covalent lead optimization. Specifically, we first introduce Covalent Executable Trajectory Reconstruction and Alignment (CETRA), an execution-grounded trajectory reconstruction strategy. CETRA constructs failure-aware search trajectories based on deterministic tool executions, covering behaviors such as invalid trials and negative explorations, thereby grounding decisions in executable evidence rather than hypothesized molecular states. We further develop Warhead-Constrained Policy Optimization (WCPO), a source-relative multi-objective optimization mechanism. It incorporates direction-normalized multi-property rewards and a source-warhead penalty, among other components, to reconcile heterogeneous property scales and favor retention of source-warhead labels. Comprehensive evaluations on a held-out benchmark show that CovMOLAgent achieves the strongest aggregate and joint multi-property performance, with leading results on several individual-property metrics, while retaining all recognized source-warhead labels in 90.9% of benchmark instances.