期刊
CHEMICAL REVIEWS
卷 119, 期 18, 页码 10520-10594出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.chemrev.8b00728
关键词
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资金
- National Natural Science Foundation of China [81573349, 81773633, 81803434, 21772130]
- National Science and Technology Major Project [2018ZX09711002-014-002, 2018ZX09711003-003-006]
- National Postdoctoral Program for Innovative Talents of China [BX20180205]
- Post-Doctoral Research and Development Foundation of Sichuan University [2018SCU12040]
- 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University [ZYGD18001]
- Post-Doctor Research Project, West China Hospital, Sichuan University [2018HXBH040]
- European Union [675555]
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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