Machine learning in chemoinformatics and drug discovery
Open accessAbstract
Chemoinformatics is an established discipline focusing on extracting, processing and extrapolating meaningful data from chemical structures. With the rapid explosion of chemical 'big' data from HTS and combinatorial synthesis, machine learning has become an indispensable tool for drug designers to mine chemical information from large compound databases to design drugs with important biological properties. To process the chemical data, we first reviewed multiple processing layers in the chemoinformatics pipeline followed by the introduction of commonly used machine learning models in drug discovery and QSAR analysis. Here, we present basic principles and recent case studies to demonstrate the utility of machine learning techniques in chemoinformatics analyses; and we discuss limitations and future directions to guide further development in this evolving field.
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Non-linear SAR modeling is beyond the capacity of classical linear regression QSAR methods and generally requires the use of machine learning.
“This is beyond the capacity of classical linear regression QSAR methods and generally requires the use of machine learning (ML) as discussed in the next section.26”
From QSAR without borders (2020) · cites this paper as [26]
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- Authors
- Lo, Y.-C., Rensi, S. E., Torng, W., & Altman, R. B.
- Affiliations
- Stanford University (US)
- Corresponding author
- Altman, Russ B.
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 23
- Issue
- 8
- Pages
- 1538-1546
- ISSN
- 1359-6446 (ISSN-L)
- PMC
- PMC6078794
- OpenAlex
- W2801991413
- Cited by
- 960
- References
- 6
- Citations by year
- 2026: 70 · 2025: 99 · 2024: 107 · 2023: 123 · 2022: 166 · 2021: 169 · 2020: 139 · 2019: 73 · 2018: 13
- FWCI
- 7.71
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Drug Discovery Today (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIH (GM102365)
- FDA (U01FD004979)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › History and advancements in chemistry
- Keywords
- machine learning, cheminformatics, drug discovery, QSAR, chemical data processing, chemical databases, high-throughput screening, combinatorial synthesis, molecular structure data
- MeSH terms
- Machine Learning, Animals, Diffusion of Innovation, Pharmaceutical Preparations, Humans, Pattern Recognition, Automated, Molecular Structure, Quantitative Structure-Activity Relationship, Informatics, Drug Discovery, High-Throughput Screening Assays
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