The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
Artificial Intelligence for Drug Toxicity and Safety
Open accessAbstract
Interventional pharmacology is one of medicine’s most potent weapons against disease. These drugs, however, can result in damaging side effects and must be closely monitored. Pharmacovigilance is the field of science that monitors, detects, and prevents adverse drug effects. Safety efforts begin during the development process, using in vivo and in vitro studies, continue through clinical trials, and extend to post-marketing surveillance of ADRs in real-world populations. Future toxicity and safety challenges, including increased polypharmacy and patient diversity, stress the limits of these traditional tools. Massive amounts of newly available data present an opportunity for using artificial intelligence and machine learning to improve drug safety science. Here, we explore recent advances as applied to pre-clinical drug safety and post-marketing surveillance with a specific focus on machine and deep learning approaches.
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Cited by
Artificial intelligence in drug discovery and development
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Artificial intelligence applied to clinical trials: opportunities and challenges
Askin, S., Burkhalter, D., Calado, G., & El Dakrouni, S. (2023) · Health and Technology · doi:10.1007/s12553-023-00738-2 · Cited by 285
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Explainable artificial intelligence for drug discovery and development: a comprehensive survey
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Metadata
- Authors
- Basile, A. O., Yahi, A., & Tatonetti, N. P.
- Venue
- Trends in Pharmacological Sciences
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 40
- Issue
- 9
- Pages
- 624-635
- ISSN
- 0165-6147 (ISSN-L)
- OpenAlex
- W2966109107
- Cited by
- 363
- References
- 95
- FWCI
- 9.94
- Type
- journal article
- Language
- en
- Open access
- green
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- Basile, A. O., Yahi, A., & Tatonetti, N. P. (2019). Artificial Intelligence for Drug Toxicity and Safety. Trends in Pharmacological Sciences. https://doi.org/10.1016/j.tips.2019.07.005
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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