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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

Chinese

多目标分子优化是药物发现的一个关键过程,旨在修改某一分子以同时提高多个属性,同时保持相似的整体结构。 这种过程通常需要通过计算或实验反馈进行迭代。 最近的大型语言模型(LLM)基于的方法已经开始探索这种迭代方法;然而,这些方法没有被明确训练以利用此类反馈,而现有的方法主要集中在单转分子优化上。 为了解决这一差距,我们引入了多转反馈引导(MFG)分子优化,一种增强学习框架,该框架训练一个LLM以逐步优化使用以前的反馈的分子。 MFG引入了一个反馈矢量,捕捉了性能的改进和源分子的相似性,并展示了反馈的优化,该功能促进了对

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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