Metric Alignment for Hit-to-Lead Molecular Optimization with an Iterative Large Language Model Agent

Hit-to-lead optimization must balance binding-related performance with drug-likeness, synthetic accessibility, and early safety considerations. We developed an iterative large language model agent that integrates target and starting-hit context, literature and computational tools, multi-property scoring, and feedback from previously scored analogs. A matched benchmark covered six kinase targets, five comparison strategies, a 40-candidate budget, and six independent campaigns per method. The agent achieved the highest mean aggregate score on four targets under an objective combining docking, quantitative estimate of drug-likeness (QED), synthetic accessibility, and a rule-based property panel. Re-evaluating the same candidate sets using drug-likeness-gated docking and docking-drug-likeness hypervolume produced different target-level winners, showing that metric choice changes the comparative interpretation of optimization campaigns. Candidate distributions showed higher-QED regions alongside improved docking, and ablation identified scored-candidate feedback as the most consistent contributor. In an epidermal growth factor receptor campaign, an erlotinib analog retained the 4-anilinoquinazoline core while improving docking from -6.80 to approximately -8.6 kcal mol⁻¹ and QED from 0.418 to approximately 0.73. These computational results support target-specific agent evaluation and candidate prioritization for subsequent biochemical, cellular, and ADMET characterization.