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From machine learning to deep learning: progress in machine intelligence for rational drug discovery
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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“There are various tasks in the drug discovery process where machine learning can be used to streamline the tasks. This includes drug compound property and activity prediction, de novo design of drug compounds, drug–receptor interactions, and drug reaction prediction [16].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [16]
In the citing paper
“QSAR modeling methods, such as linear discriminant analysis (LDA), support vector machines (SVMs), random forest (RF), and decision trees, have been used to identify prospective drug candidates and have evolved into AI-based QSAR approaches can use that to speed up QSAR analysis”
From Blockchain and Artificial Intelligence Technology in E-Health (2021) · cites this paper as Zhang 2017 & Jain 2015
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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
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2017-11
- Volume
- 22
- Issue
- 11
- Pages
- 1680-1685
- ISSN
- 1359-6446 (ISSN-L)
- 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
- 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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