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MFG: Multi-turn Feedback Guided Molecular Optimization

Trieu Nguyen, Frazier N. Baker, Ruoxi Gao, Daniel Adu‐Ampratwum, Xia Ning · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.18851 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple properties whileining a similar overall structure. このプロセスはしばしば、計算または実験的なフィードバックによって導かれるイテラティブな変更を必要とする。最近の大言語モデル(LLM)ベースの方法は、このイテラティブなアプローチを探求し始めたが、これらの方法は同時に複数の特性を改善するために明示的に訓練されていないが、既存のアプローチは主に単一回転分子の最適化に焦点を当てている。このギャップを解決するために、我々は、複数の回転フィードバックガイド(MFG)分子

Machine translation (Meta M2M100); the English original follows.

Abstract

Multi-objective molecular optimization is a critical process in drug discovery that aims to modify a given molecule to simultaneously improve multiple properties while maintaining a similar overall structure. This process often requires iterative modifications guided by computational or experimental feedback. Recent large language model (LLM)-based methods have begun to explore this iterative approach; however, these methods are not explicitly trained to leverage such feedback, while existing approaches primarily focus on single-turn molecular optimization. To address this gap, we introduce Multi-turn Feedback Guided (MFG) molecular optimization, a reinforcement learning framework that trains an LLM to progressively optimize molecules using feedback from previous turns. MFG introduces a feedback vector capturing both property improvements and source molecule similarity, alongside a feedback-aligned reward function that promotes progressive improvement across turns. We apply MFG over $\text{GeLLM}^{3}\text{O}$, a state-of-the-art LLM model for multi-objective molecular optimization, evaluate $\text{GeLLM}^{3}\text{O-MFG}$ on ten multi-objective molecular optimization tasks. The results demonstrate that $\text{GeLLM}^{3}\text{O-MFG}$ achieves the highest average similarity-constrained success rate ($81.50%$), outperforming the base model $\text{GeLLM}^{3}\text{O}$ ($70.81%$).

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Topics: Computer science, Leverage (statistics), Process (computing), Reinforcement learning, Iterative and incremental development, Property (philosophy)

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