Toward Medicinal Chemistry-Aware Molecular Generation through LLM Guidance

Molecular optimization and evaluation in AI-driven drug discovery relies primarily on established molecular metrics and heuristics such as QED, Lipinski's Rule-of-Five, structure filters, and synthetic accessibility scores. While effective at assessing specific aspects of molecular quality, these metrics cannot fully capture the holistic reasoning that medicinal chemists apply when prioritizing candidate molecules. In this work, we propose a new evaluation paradigm in which a large language model acts as a medicinal chemistry judge, assigning each molecule an overall quality score while explaining the medicinal chemistry considerations underlying its assessment. To systematically evaluate this approach, we introduce MedChemJudge, the first benchmark specifically designed for LLM-based medicinal chemistry assessment, comprising over 500 expert-annotated molecules across three complementary evaluation settings. Using this benchmark, we identify an effective judge configuration achieving a C-index of 0.84 in reproducing expert quality rankings. The analysis shows that LLM-derived scores can distinguish between structurally similar molecules even when conventional molecular metrics are nearly identical. Finally, incorporating the proposed score into RL–based molecular generative model improves the medicinal chemistry quality of generated molecules across 5 targets while maintaining strong docking performance and high diversity. Together, these results highlight the potential of LLM-based evaluation as a complementary assessment layer for AI-assisted drug design and support molecular optimization that more closely reflects medicinal chemistry decision-making.