利用强化学习进行新药设计

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hampus Gummesson Svensson, Christian Tyrchan, Ola Engkvist, Morteza Haghir Chehreghani
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引用次数: 0

摘要

过去几年,基于深度学习生成具有特定性质的新型药物分子的方法受到了广泛关注。最近的研究表明,利用强化学习生成基于字符串的新型分子具有良好的性能。在本文中,我们开发了一个将强化学习用于新药设计的统一框架,系统地研究了各种政策内和政策外强化学习算法和重放缓冲器,以学习基于 RNN 的政策,生成预测对多巴胺受体 DRD2 有活性的新分子。我们的研究结果表明,当结构多样性至关重要时,至少使用得分最高和得分最低的分子来更新策略是有利的。在一次迭代中使用所有生成的分子似乎能提高策略算法的性能稳定性。此外,在重放高分、中分和低分分子时,非政策算法显示出提高结构多样性和生成的活性分子数量的潜力,但可能要以延长探索阶段为代价。我们的工作提供了一个开源框架,使研究人员能够研究用于新药设计的各种强化学习方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Utilizing reinforcement learning for de novo drug design

Utilizing reinforcement learning for de novo drug design

Deep learning-based approaches for generating novel drug molecules with specific properties have gained a lot of interest in the last few years. Recent studies have demonstrated promising performance for string-based generation of novel molecules utilizing reinforcement learning. In this paper, we develop a unified framework for using reinforcement learning for de novo drug design, wherein we systematically study various on- and off-policy reinforcement learning algorithms and replay buffers to learn an RNN-based policy to generate novel molecules predicted to be active against the dopamine receptor DRD2. Our findings suggest that it is advantageous to use at least both top-scoring and low-scoring molecules for updating the policy when structural diversity is essential. Using all generated molecules at an iteration seems to enhance performance stability for on-policy algorithms. In addition, when replaying high, intermediate, and low-scoring molecules, off-policy algorithms display the potential of improving the structural diversity and number of active molecules generated, but possibly at the cost of a longer exploration phase. Our work provides an open-source framework enabling researchers to investigate various reinforcement learning methods for de novo drug design.

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来源期刊
Machine Learning
Machine Learning 工程技术-计算机:人工智能
CiteScore
11.00
自引率
2.70%
发文量
162
审稿时长
3 months
期刊介绍: Machine Learning serves as a global platform dedicated to computational approaches in learning. The journal reports substantial findings on diverse learning methods applied to various problems, offering support through empirical studies, theoretical analysis, or connections to psychological phenomena. It demonstrates the application of learning methods to solve significant problems and aims to enhance the conduct of machine learning research with a focus on verifiable and replicable evidence in published papers.
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