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通过帕雷托MCTS实现目标意识分子生成以实现多个目标

Yaodong Yang¹, Guangyong Chen², Jinpeng Li¹

¹Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.

Communications biology

|September 2, 2024

概括

一个新的算法ParetoDrug同时优化多种药物特性,以实现针对性药物发现. 这种方法增强了有效的小分子配体的生成,具有强大的结合亲和力和药物相似性.

科学领域:

计算化学和化学信息学
人工智能在药物发现中的作用
分子建模和模拟分子模型

背景情况:

针对目标的药物发现加速了具有高结合亲和力的小分子配体的设计.
目前的深度生成模型专注于蛋白质-联体结合,但忽视了其他关键药物特性,如药物相似性.
在使用深度学习实现目标意识分子生成的多目标优化中存在差距.

研究的目的:

介绍ParetoDrug,一个帕雷托蒙特卡洛树搜索 (MCTS) 算法,用于多目标目标感知分子生成.
为了使多个分子性质的同步优化,包括结合亲和和药物相似性.
通过结合药物相似性以及结合亲和力来解决现有方法的局限性.

主要方法:

开发了ParetoDrug,一个用于化学太空探索的Pareto MCTS生成算法.
利用预训练的原子对原子自回归生成模型来在MCTS中引导分子生成.
引入了帕雷托PUCT计划,以在原子选择过程中平衡勘探和开发.

主要成果:

帕雷托药物有效地导航复杂的化学空间,以发现新的化合物.
该算法成功识别了具有令人满意的结合亲和力和药物样性质的分子.
通过基准实验和案例研究,在各种多目标目标意识药物发现任务中表现出高效率.

结论:

帕雷托药物弥合了多目标目标意识分子生成的差距.
提出的方法可以同时优化结合亲和力和药物相似性.
帕雷托药物代表了基于深度学习的药物发现的重大进步,用于生成有效的候选药物.

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