RLMolLM:强化学习增强型语言模型框架用于反向分子设计.
Xiaobo Lin1, Debsindhu Bhowmik2, Logan T Kearney1
1Carbon and Composites Group, Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, United States.
Journal of chemical information and modeling
|November 6, 2025
概括
RLMolLM是一种新的强化学习框架,通过优化药物相似性和ADMET等多种属性来增强分子设计. 它克服了当前语言模型的局限性,提高了药物发现的有效性和支架保存.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
背景情况:
- 反向分子设计面临着巨大的化学空间和复杂的性能要求的挑战.
- 现有的语言模型在分子有效性,多属性优化和结构约束方面扎.
研究的目的:
- 介绍RLMolLM,一个强化学习框架,将近距离政策优化 (PPO) 与遗传算法集成在一起.
- 解决分子生成的局限性,包括有效性,多属性优化和支架保护.
主要方法:
- RLMolLM框架将近距离政策优化 (PPO) 与遗传算法相结合.
- 优化药物相似性 (QED),合成可访问性 (SA) 和ADMET属性的定量估计.
- 允许制约脚手架的生成,保留特定的基层结构.
主要成果:
- 在GDB13,Moses和Zinc数据集中获得了优异的QED分数,提高了多达31%.
- 证明了ADMET的实质性改善,包括4.5倍降低hERG毒性和增强Caco-2的透性.
- 在结构约束下显著改善分子有效性和属性优化,同时保留脚手架.
结论:
- RLMolLM为制药和材料分子设计提供了一种多功能解决方案.
- 该框架有效地整合了强化学习和遗传算法,用于多属性优化和脚手架保护.
- 通过克服当前生成模型的关键局限性,推进逆分子设计.
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