MFG: Multi-turn Feedback Guided Molecular Optimization

arXiv:2610.18851v1 Announce Type: cross 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 ext{GeLLM}^{3}ext{O}, a state-of-the-art LLM model for multi-objective molecular optimization, evaluate ext{GeLLM}^{3}ext{O-MFG} on ten multi-objective molecular optimization tasks. The results demonstrate that ext{GeLLM}^{3}ext{O-MFG} achieves the highest average similarity-constrained success rate (81.50%), outperforming the base model ext{GeLLM}^{3}ext{O} (70.81%).

Source: arXiv cs.LG | 2026-10-14