Abstract

Over the past decade, deep learning has achieved remarkable success in various artificial intelligence research areas. Evolved from the previous research on artificial neural networks, this technology has shown superior performance to other machine learning algorithms in areas such as image and voice recognition, natural language processing, among others. The first wave of applications of deep learning in pharmaceutical research has emerged in recent years, and its utility has gone beyond bioactivity predictions and has shown promise in addressing diverse problems in drug discovery. Examples will be discussed covering bioactivity prediction, de novo molecular design, synthesis prediction and biological image analysis.

Keywords

Deep learningDrug discoveryArtificial intelligenceComputer scienceArtificial neural networkMachine learningData scienceDeep neural networksBioinformaticsBiology

MeSH Terms

Datasets as TopicDiagnostic ImagingDrug DiscoveryMachine LearningNeural NetworksComputer

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Publication Info

Year
2018
Type
review
Volume
23
Issue
6
Pages
1241-1250
Citations
1619
Access
Closed

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Cite This

Hongming Chen, Ola Engkvist, Yinhai Wang et al. (2018). The rise of deep learning in drug discovery. Drug Discovery Today , 23 (6) , 1241-1250. https://doi.org/10.1016/j.drudis.2018.01.039

Identifiers

DOI
10.1016/j.drudis.2018.01.039
PMID
29366762

Data Quality

Data completeness: 90%