Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
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The to date largest comparative study of nine state-of-the-art drug target prediction methods finds that deep learning outperforms all other competitors. The results are based on a benchmark of 1300 assays and half a million compounds.
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A nested cluster-cross validation strategy has been used to show that DNNs outperform other machine learning methods.
“A nested cluster-cross validation strategy has also been used to show that DNNs outperform these other machine learning methods18.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [18]
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- Authors
- Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., & Hochreiter, S.
- Affiliations
- Johannes Kepler University of Linz (AT)
- Open Analytics NV
- Janssen (Belgium) (BE)
- Bayer (Germany) (DE)
- ORCID
- Venue
- Chemical Science
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2018
- Volume
- 9
- Issue
- 24
- Pages
- 5441-5451
- ISSN
- 2041-6520 (ISSN-L)
- 2041-6539
- PMC
- PMC6011237
- OpenAlex
- W2806547269
- Cited by
- 584
- References
- 68
- Citations by year
- 2026: 57 · 2025: 68 · 2024: 83 · 2023: 83 · 2022: 63 · 2021: 94 · 2020: 75 · 2019: 48 · 2018: 13
- FWCI
- 43.38
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Chemical Science (publishedVersion, cc-by-nc)
- Europe PMC (PubMed Central) (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- Horizon 2020 Framework Programme (ExCAPE/671555)
- Austrian Science Fund (P 28660-N31)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- drug target prediction, ChEMBL, method comparison, deep learning, machine learning, drug discovery
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Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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