基于深度学习的全新药物分子设计:原理、工具与实践  

Deep learning-based de novo drug design:principles,tools and practice

作　　者：沈涛 王冬梅 吴松 蒋建东 夏杰 SHEN Tao;WANG Dong-mei;WU Song;JIANG Jian-dong;XIA Jie(State Key Laboratory of Bioactive Substance and Function of Natural Medicines,Institute of Materia Medica,Chinese Academy of Medical Sciences&Peking Union Medical College,Beijing 100050,China)

机构地区：[1]中国医学科学院、北京协和医学院药物研究所,天然药物活性物质与功能国家重点实验室,北京100050

出　　处：《药学学报》2023年第9期2610-2622,共13页Acta Pharmaceutica Sinica

基　　金：中国医学科学院医学与健康科技创新工程(2021-I2M-1-069);国家重大新药创制科技重大专项(2018ZX09711001-012-003)。

摘　　要：利用深度学习方法设计具有全新结构的药物分子可以突破传统计算机辅助药物设计的技术瓶颈,已经成为药物设计新技术研究的前沿,在药物研发实践中展现了巨大潜力。本文从深度学习驱动的全新分子设计的基本原理出发,简要介绍了深度分子生成技术和计算工具,分析了若干代表性成功案例,最后对该技术的未来发展方向和应用前景进行了展望。本综述将为新技术研究提供思路,为应用该技术开展新药研发实践提供借鉴。Design of structurally-novel drug molecules with deep learning can overcome the technical bottleneck of classical computer-aided drug design.It has become the frontier of new technique research on drug design,and has shown great potential in drug research and development practice.This review starts from the basic principles of deep learning-driven de novo drug design,goes on with the brief introduction to deep molecular generation techniques as well as computational tools and the analysis on representative successful cases,and eventually provides our perspective for future direction and application prospect about this technique.This review will provide ideas on new technique research and references for new drug research and development practice to which this technique is applied.

关 键 词：新药研发 分子信息学 全新药物设计 人工智能 深度学习 

分 类 号：R914[医药卫生—药物化学]