深度学习在分子生成中的应用进展  

Application of Deep Learning in Molecule Generation

作　　者：陈琳杰 周瑞宁 吕皓 徐佳颖 何正大 陈亚东 CHEN Linjie;ZHOU Ruining;LYU Hao;XU Jiaying;HE Zhengda;CHEN Yadong(School of Science,China Pharmaceutical University,Nanjing 211198,China)

机构地区：[1]中国药科大学理学院,江苏南京211198

出　　处：《药学进展》2023年第12期950-960,共11页Progress in Pharmaceutical Sciences

基　　金：国家自然科学基金(No.61806092)。

摘　　要：分子设计中的药物设计是为了产生具有理想生物活性和物理化学性质的分子,随着计算机科学与高性能计算的快速发展,深度学习技术在药物设计领域的应用日益受到重视。生成式深度学习模型在自然语言、图像、音乐、视频等领域的表现卓越,为分子生成提供了新的思路。越来越多的研究者开始尝试使用深度学习技术完成分子生成任务。综述总结了深度学习算法在分子生成中的研究进展,重点介绍了常用的几种分子生成神经网络架构的原理、应用、分子表征形式及其技术细节。Drug design in molecular design aims to produce molecules with desirable biological activity and physicochemical properties.With the rapid development of computer science and high performance computing,the application of deep learning technologies in the field of drug design is gaining increasing recognition.Generative deep learning models have demonstrated remarkable performance in such fields as natural language,image,music,and video,providing new ideas for molecule generation.More and more researchers have started to use deep learning technologies to complete molecule generation tasks.This article summarizes the research progress of deep learning algorithms in molecule generation,focusing on the principles,applications,molecular representation forms,and technical details of several commonly used neural network architectures for molecule generation.

关 键 词：人工智能 深度学习 药物设计 分子生成 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]