深度学习辅助药物发现的研究进展  

Recent Advances in Deep Learning Aided Drug Discovery

作　　者：戴青青 余俊霖 李国菠 DAI Qingqing;YU Junlin;LI Guobo(Department of Medicinal Chemistry,West China School of Pharmacy,Sichuan University,Chengdu 610041,China)

机构地区：[1]四川大学华西药学院药物化学系,四川成都610041

出　　处：《药学进展》2022年第1期60-70,共11页Progress in Pharmaceutical Sciences

基　　金：国家自然科学基金(No.82122065);四川省国际合作项目(No.22GJHZ0253)。

摘　　要：深度学习技术近年来取得了重大突破,被应用于医学、药学等多个领域。聚焦深度学习在创新药物发现中的发展和应用,对深度学习被用于蛋白结构预测、药物靶标预测、药物-靶标相互作用预测、药物合成路线设计、从头药物分子设计以及药物吸收、分布、代谢、排泄和毒性(ADMET)预测等代表性案例进行详细综述,同时总结了现有方法面临的问题和可能的解决思路,以期为深度学习辅助药物发现相关方法的发展和应用提供借鉴与思考。With its significant breakthroughs in recent years, deep learning technology has been used in medical, pharmaceutical and many other areas. This review focuses on the development and application of deep learning for innovative drug discovery, summarizes typical cases of deep learning for the prediction of protein structure, drug target and drug-target interaction, the design of drug synthesis route, de novo drug design, and the prediction of drug absorption, distribution, metabolism, excretion and toxicity(ADMET), and discusses the current problems and possible solutions, in the hope of providing some reference for the development and application of deep learning for drug discovery.

关 键 词：人工智能 深度学习 药物设计 药物发现 从头设计 

分 类 号：R914.2[医药卫生—药物化学]