Recent Progress of Deep Learning in Drug Discovery

被引:11
|
作者
Wang, Feng [1 ]
Diao, XiaoMin [1 ]
Chang, Shan [2 ]
Xu, Lei [2 ]
机构
[1] Changzhou Univ, Coll Informat Sci & Engn, Huaide Coll, Taizhou 214500, Peoples R China
[2] Jiangsu Univ Technol, Inst Bioinformat & Med Engn, Changzhou 213001, Peoples R China
基金
美国国家科学基金会;
关键词
Artificial intelligence; neural networks; deep learning; drug discovery; de novo design; property prediction; biomedical imaging; synthetic planning; CONVOLUTIONAL NEURAL-NETWORK; AQUEOUS SOLUBILITY; PREDICTION; DESIGN; CLASSIFICATION; REPRESENTATION; TOOL;
D O I
10.2174/1381612827666210129123231
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Deep learning, an emerging field of artificial intelligence based on neural networks in machine learning, has been applied in various fields and is highly valued. Herein, we mainly review several mainstream architectures in deep learning, including deep neural networks, convolutional neural networks and recurrent neural networks in the field of drug discovery. The applications of these architectures in molecular de novo design, property prediction, biomedical imaging and synthetic planning have also been explored. Apart from that, we further discuss the future direction of the deep learning approaches and the main challenges we need to address.
引用
收藏
页码:2088 / 2096
页数:9
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