An integrative machine learning approach for prediction of toxicity-related drug safety
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Abstract
Abstract Recent trends in drug development have been marked by diminishing returns of escalating costs and falling rate of new drug approval. Unacceptable drug toxicity is a substantial cause of drug failure during clinical trials as well as the leading cause of drug withdraws after release to market. Computational methods capable of predicting these failures can reduce waste of resources and time devoted to the investigation of compounds that ultimately fail. We propose an original machine learning method that leverages identity of drug targets and off-targets, functional impact score computed from Gene Ontology annotations, and biological network data to predict drug toxicity. We demonstrate that our method (TargeTox) can distinguish potentially idiosyncratically toxic drugs from safe drugs and is also suitable for speculative evaluation of different target sets to support the design of optimal low-toxicity combinations. Summary blurb: Prediction of toxicity-related drug clinical trial failures, withdrawals from market and idiosyncratic toxicity risk by combining biological network analysis with machine learning.
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The efficacy and toxicity of new drug compounds can be predicted using AI approaches with greater accuracy and efficiency than traditional methods.
“For instance, the efficacy and toxicity of new drug compounds can be predicted using these approaches, with greater accuracy and efficiency than when using traditional methods [12,13].”
From The role of AI in drug discovery: challenges, opportunities, and strategies (2023) · cites this paper as [13]
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- Authors
- Lysenko, A., Sharma, A., Boroevich, K. A., & Tsunoda, T.
- Affiliations
- RIKEN Center for Integrative Medical Sciences (JP)
- Tokyo Medical and Dental University (JP)
- Corresponding author
- Lysenko, Artem
- Tsunoda, Tatsuhiko
- Venue
- Life Science Alliance
- Publisher
- Life Science Alliance, LLC
- Published
- 2018-11-28
- Volume
- 1
- Issue
- 6
- Pages
- e201800098
- ISSN
- 2575-1077 (ISSN-L)
- PMC
- PMC6262234
- OpenAlex
- W2898397916
- Cited by
- 88
- References
- 72
- Citations by year
- 2026: 5 · 2025: 15 · 2024: 21 · 2023: 20 · 2022: 9 · 2021: 5 · 2020: 8 · 2019: 4 · 2017: 1
- FWCI
- 3.18
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Life Science Alliance (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, public-domain)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Griffith Research Online (submittedVersion, cc-by)
- Funders
- Core Research for Evolutional Science and Technology
- Japan Science and Technology Agency (JPMJCR1412)
- Japan Society for the Promotion of Science KAKENHI (18K18156, 17H06307, 17H06299)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Toxicology › Pharmacovigilance and Adverse Drug Reactions
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- drug toxicity prediction, drug safety, idiosyncratic toxicity, therapeutic target, off-target effects, biological networks, drug combination toxicity, drug development failure
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