计算机科学
人工智能
人工神经网络
机器学习
药物发现
图形
数据科学
生物信息学
理论计算机科学
生物
作者
Jiacheng Xiong,Zhaoping Xiong,Kaixian Chen,Hualiang Jiang,Mingyue Zheng
标识
DOI:10.1016/j.drudis.2021.02.011
摘要
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.
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