Enhancing preclinical drug discovery with artificial intelligence

被引：42

作者：

Vijayan, R. S. K. ^{[1
]}

Kihlberg, Jan ^{[2
]}

Cross, Jason B. ^{[1
]}

Poongavanam, Vasanthanathan ^{[2
]}

机构：

[1] Univ Texas MD Anderson Canc Ctr, Inst Appl Canc Sci, Houston, TX 77030 USA

[2] Uppsala Univ, Dept Chem BMC, Uppsala, Sweden

来源：

DRUG DISCOVERY TODAY | 2022年 / 27卷 / 04期

关键词：

Artificial intelligence; Machine learning; Deep learning; Drug discovery; LIGAND BINDING-AFFINITY; ACCURATE PREDICTION; NEURAL-NETWORKS; DOCKING; COMPUTER; OPTIMIZATION; QUALITY; DESIGN; MODELS; GUIDE;

D O I：

10.1016/j.drudis.2021.11.023

中图分类号：

R9 [药学];

学科分类号：

1007 ;

摘要：

Artificial intelligence (AI) is becoming an integral part of drug discovery. It has the potential to deliver across the drug discovery and development value chain, starting from target identification and reaching through clinical development. In this review, we provide an overview of current AI technologies and a glimpse of how AI is reimagining preclinical drug discovery by highlighting examples where AI has made a real impact. Considering the excitement and hyperbole surrounding AI in drug discovery, we aim to present a realistic view by discussing both opportunities and challenges in adopting AI in drug discovery.

引用

页码：967 / 984

页数：18

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