图神经网络在药物从头设计中的应用。
Graph neural networks for automated de novo drug design.
机构信息
Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China.
Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 555 Zuchongzhi Road, Shanghai 201203, China; Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China.
出版信息
Drug Discov Today. 2021 Jun;26(6):1382-1393. doi: 10.1016/j.drudis.2021.02.011. Epub 2021 Feb 17.
The goal of de novo drug design is to create novel chemical entities with desired biological activities and pharmacokinetics (PK) properties. Over recent years, with the development of artificial intelligence (AI) technologies, data-driven methods have rapidly gained in popularity in this field. Among them, graph neural networks (GNNs), a type of neural network directly operating on the graph structure data, have received extensive attention. In this review, we introduce the applications of GNNs in de novo drug design from three aspects: molecule scoring, molecule generation and optimization, and synthesis planning. Furthermore, we also discuss the current challenges and future directions of GNNs in de novo drug design.
从头设计药物的目标是创造具有理想生物活性和药代动力学（PK）性质的新型化学实体。近年来，随着人工智能（AI）技术的发展，基于数据的方法在该领域迅速流行起来。其中，图神经网络（GNN）是一种直接作用于图结构数据的神经网络，受到了广泛关注。在本文综述中，我们从分子打分、分子生成和优化以及合成规划三个方面介绍了 GNN 在从头设计药物中的应用。此外，我们还讨论了 GNN 在从头设计药物中目前面临的挑战和未来的发展方向。
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