Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.
J Chem Inf Model. 2022 May 23;62(10):2269-2279. doi: 10.1021/acs.jcim.2c00042. Epub 2022 May 11.
A persistent goal for drug design is to generate novel chemical compounds with desirable properties in a labor-, time-, and cost-efficient manner. Deep generative models provide alternative routes to this goal. Numerous model architectures and optimization strategies have been explored in recent years, most of which have been developed to generate two-dimensional molecular structures. Some generative models aiming at three-dimensional (3D) molecule generation have also been proposed, gaining attention for their unique advantages and potential to directly design drug-like molecules in a target-conditioning manner. This review highlights current developments in 3D molecular generative models combined with deep learning and discusses future directions for drug design.
药物设计的一个持久目标是以节省人力、时间和成本的方式生成具有理想性质的新型化合物。深度生成模型为实现这一目标提供了替代途径。近年来,已经探索了许多模型架构和优化策略,其中大多数都是为了生成二维分子结构而开发的。一些旨在生成三维(3D)分子的生成模型也已被提出,它们具有独特的优势和潜力,可以直接以目标条件的方式设计类药性分子,因此受到关注。本文重点介绍了结合深度学习的 3D 分子生成模型的最新进展,并讨论了药物设计的未来方向。