MFG: Multi-turn Feedback Guided Molecular Optimization· MFG：多轮反馈引导的分子优化
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 fe
多轮反馈引导下利用强化学习优化分子结构的新方法。
- 核心方法
- 提出了一种多轮反馈引导的分子优化框架 MFG，通过强化学习训练大型语言模型（LLM）逐步优化分子，利用来自前几轮的反馈信息。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有的分子优化方法主要集中在单轮优化上，未能充分利用多轮反馈信息进行连续的分子改进。
- 关键实验
- 通过多个分子优化任务验证了 MFG 的有效性，展示出比单轮优化方法更好的性能。
- 主要贡献
- 首次提出将多轮反馈信息融入分子优化过程，提高了分子优化的效率和效果。
- 意义与局限
- 该方法对于药物发现和新材料设计具有重要意义，但目前仅在模拟环境中测试，实际应用还需要进一步验证。