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Cited by 760Computational Drug Discovery MethodsMachine Learning in Materials ScienceProtein Degradation and Inhibitors

The DOI matches its Crossref record; 59 of the 60 references it cites were found in a registry or an index.

From machine learning to deep learning: progress in machine intelligence for rational drug discovery

Zhang, Lu, Tan, Jianjun, Han, Dan, Zhu, Hao·2017·Drug Discovery Today
DOI 10.1016/j.drudis.2017.08.010
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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.

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Research works printed but not linked: 3

  1. Hinton, 2006, 8, 1527
  2. Jain, 2015, In silico de novo design of novel NNRTIs: a bio-molecular modelling approach, RSC Adv., 5, 14814, 10.1039/C4RA15478A · DOI
  3. Unterthiner, 2014, Deep learning as an opportunity in virtual screening, Proc. Deep Learning Workshop NIPS

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Metadata

Authors
Zhang, L., Tan, J., Han, D., & Zhu, H.
Affiliations
  • Beijing University of Technology (CN)
  • Rutgers, The State University of New Jersey (US)
Corresponding author
  • Tan, Jianjun
  • Zhu, Hao
ORCID
Venue
Drug Discovery Today
Publisher
Elsevier BV
Published
2017-11
Volume
22
Issue
11
Pages
1680-1685
ISSN
1359-6446 (ISSN-L)
DOI
10.1016/j.drudis.2017.08.010
OpenAlex
W2753588101
Cited by
760
References
60
Citations by year
2026: 42 · 2025: 90 · 2024: 105 · 2023: 134 · 2022: 112 · 2021: 96 · 2020: 79 · 2019: 61 · 2018: 39 · 2017: 2
FWCI
4.98
Type
journal article
Language
en
Open access
closed
Licence
Locations
  • Drug Discovery Today (publishedVersion)
  • PubMed (publishedVersion)
Funders
Chinese Natural Science Foundation Project (21173014)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
Keywords
rational drug discovery, deep learning, machine learning, QSAR, big data, history of machine learning, computer-aided drug design
MeSH terms
Machine Learning, Artificial Intelligence, Humans, Models, Theoretical, Drug Design, Quantitative Structure-Activity Relationship, Drug Discovery

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