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

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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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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: Zhang, Lu
Tan, Jianjun
Han, Dan
Zhu, Hao
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: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
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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From machine learning to deep learning: progress in machine intelligence for rational drug discovery

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