Indexed by：
Abstract：
Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of ＇big＇ data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era.
Keyword：
Reprint Author's Address：
Source ：
DRUG DISCOVERY TODAY
ISSN： 1359-6446
Year： 2017
Issue： 11
Volume： 22
Page： 1680-1685
7 . 4 0 0
JCR@2022
ESI Discipline： PHARMACOLOGY & TOXICOLOGY;
ESI HC Threshold：160
CAS Journal Grade：1
Cited Count：
WoS CC Cited Count： 420
SCOPUS Cited Count： 549
ESI Highly Cited Papers on the List： 39 Unfold All
WanFang Cited Count：
Chinese Cited Count：
30 Days PV： 11
Affiliated Colleges：
