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Annual Review of Pharmacology and Toxicology

Volume 60, 2020

Artificial Intelligence in Drug Treatment

Abstract

The most common applications of artificial intelligence (AI) in drug treatment have to do with matching patients to their optimal drug or combination of drugs, predicting drug-target or drug-drug interactions, and optimizing treatment protocols. This review outlines some of the recently developed AI methods aiding the drug treatment and administration process. Selection of the best drug(s) for a patient typically requires the integration of patient data, such as genetics or proteomics, with drug data, like compound chemical descriptors, to score the therapeutic efficacy of drugs. The prediction of drug interactions often relies on similarity metrics, assuming that drugs with similar structures or targets will have comparable behavior or may interfere with each other. Optimizing the dosage schedule for administration of drugs is performed using mathematical models to interpret pharmacokinetic and pharmacodynamic data. The recently developed and powerful models for each of these tasks are addressed, explained, and analyzed here.

Keyword(s): artificial intelligence, combination therapy, deep learning, drug combination, machine learning, personalized medicine
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/content/journals/10.1146/annurev-pharmtox-010919-023746
2020-01-06
2024-04-18
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/content/journals/10.1146/annurev-pharmtox-010919-023746
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Artificial Intelligence in Drug Treatment
Annual Review of Pharmacology and Toxicology 60, 353 (2020); https://doi.org/10.1146/annurev-pharmtox-010919-023746
/content/journals/10.1146/annurev-pharmtox-010919-023746
/content/journals/10.1146/annurev-pharmtox-010919-023746
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