The rise of deep learning in drug discovery

被引:1050
作者
Chen, Hongming [1 ]
Engkvist, Ola [1 ]
Wang, Yinhai [2 ]
Olivecrona, Marcus [1 ]
Blaschke, Thomas [1 ]
机构
[1] AstraZeneca R&D Gothenburg, Innovat Med & Early Dev Biotech Unit, Hit Discovery, Discovery Sci, S-43183 Molndal, Sweden
[2] AstraZeneca, Innovat Med & Early Dev Biotech Unit, Discovery Sci, Quantitat Biol, Unit 310,Cambridge Sci Pk,Milton Rd, Cambridge CB4 0WG, England
基金
欧盟地平线“2020”;
关键词
NEURAL-NETWORKS; GENERATION; PREDICTION; ARCHITECTURES; SEGMENTATION; DATABASE; DOCKING; MODEL;
D O I
10.1016/j.drudis.2018.01.039
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, de novo molecular design, synthesis prediction and biological image analysis.
引用
收藏
页码:1241 / 1250
页数:10
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