Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.
Open accessAbstract
The machine learning program GOLEM from the field of inductive logic programming was applied to the drug design problem of modeling structure-activity relationships. The training data for the program were 44 trimethoprim analogues and their observed inhibition of Escherichia coli dihydrofolate reductase. A further 11 compounds were used as unseen test data. GOLEM obtained rules that were statistically more accurate on the training data and also better on the test data than a Hansch linear regression model. Importantly machine learning yields understandable rules that characterized the chemistry of favored inhibitors in terms of polarity, flexibility, and hydrogen-bonding character. These rules agree with the stereochemistry of the interaction observed crystallographically.
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King et al. (1992) use ILP to model structure-activity relationships for drug design.
“For instance, King et al. (1992) use ILP to model structure-activity relationships for drug design.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as King et al., 1992
ILP has been widely used for scientific discovery.
“For this reason, ILP has been widely used for scientific discovery.”
From Inductive Logic Programming At 30: A New Introduction (2022) · cites this paper as King et al., 1992
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HANSCH, C., MALONEY, P. P., FUJITA, T., & MUIR, R. M. (1962) · Nature · doi:10.1038/194178b0 · Cited by 1,056 · see citing works
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Dimitrov, T., Kreisbeck, C., Becker, J. S., Aspuru-Guzik, A., & Saikin, S. K. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b01226 · Cited by 103 · see citing works
Inductive Logic Programming At 30: A New Introduction
Cropper, A., & Dumančić, S. (2022) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.13507 · Cited by 83 · see citing works
Turcotte, M., Muggleton, S. H., & Sternberg, M. J. E. (2001) · Machine Learning · doi:10.1023/a:1007672817406 · Cited by 30 · see citing works
Self-citationILP-assisted de novo drug design
Kaalia, R., Srinivasan, A., Kumar, A., & Ghosh, I. (2016) · Machine Learning · doi:10.1007/s10994-016-5556-x · Cited by 13 · see citing works
- Authors
- King, R. D., Muggleton, S., Lewis, R. A., & Sternberg, M. J.
- Affiliations
- University of Strathclyde (GB)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 1992-12
- Volume
- 89
- Issue
- 23
- Pages
- 11322-11326
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC50542
- OpenAlex
- W2051589410
- Cited by
- 219
- References
- 1
- Citations by year
- 2025: 4 · 2023: 8 · 2022: 7 · 2021: 6 · 2020: 2 · 2019: 2 · 2018: 2 · 2017: 2 · 2016: 10 · 2015: 1 · 2014: 1 · 2013: 3 · 2012: 2
- FWCI
- 18.46
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Research Explorer (The University of Manchester) (submittedVersion)
- 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 › Organic Chemistry › Synthesis and Characterization of Heterocyclic Compounds
- Keywords
- dihydrofolate reductase, structure-activity relationship, inductive logic programming, Golem, drug design, enzyme inhibition, Escherichia coli, hydrogen bonding, molecular flexibility, stereochemistry, crystal structure
- MeSH terms
- Artificial Intelligence, Escherichia coli, Folic Acid Antagonists, Structure-Activity Relationship, Trimethoprim, Drug Design, Molecular Structure
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