Big Data and Artificial Intelligence Modeling for Drug Discovery

被引:201
作者
Zhu, Hao [1 ,2 ]
机构
[1] Rutgers State Univ, Dept Chem, Camden, NJ 08102 USA
[2] Rutgers State Univ, Ctr Computat & Integrat Biol, Camden, NJ 08102 USA
来源
ANNUAL REVIEW OF PHARMACOLOGY AND TOXICOLOGY, VOL 60 | 2020年 / 60卷
关键词
artificial intelligence; big data; deep learning; machine learning; rational drug design; computer-aided drug discovery; THROUGHPUT SCREENING DATA; DEEP NEURAL-NETWORKS; COMBINATORIAL CHEMISTRY; BIOLOGICAL-ACTIVITIES; CARBON NANOTUBES; QSAR MODELS; DESIGN; TOXICITY; VALIDATION; SYSTEMS;
D O I
10.1146/annurev-pharmtox-010919-023324
中图分类号
R9 [药学];
学科分类号
1007 ;
摘要
Due to the massive data sets available for drug candidates, modern drug discovery has advanced to the big data era. Central to this shift is the development of artificial intelligence approaches to implementing innovative modeling based on the dynamic, heterogeneous, and large nature of drug data sets. As a result, recently developed artificial intelligence approaches such as deep learning and relevant modeling studies provide new solutions to efficacy and safety evaluations of drug candidates based on big data modeling and analysis. The resulting models provided deep insights into the continuum from chemical structure to in vitro, in vivo, and clinical outcomes. The relevant novel data mining, curation, and management techniques provided critical support to recent modeling studies. In summary, the new advancement of artificial intelligence in the big data era has paved the road to future rational drug development and optimization, which will have a significant impact on drug discovery procedures and, eventually, public health.
引用
收藏
页码:573 / 589
页数:17
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