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.

中文翻译：

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药物治疗中的人工智能。

人工智能（AI）在药物治疗中的最普遍应用与使患者匹配其最佳药物或药物组合，预测药物靶标或药物-药物相互作用以及优化治疗方案有关。这篇综述概述了一些最近开发的帮助药物治疗和给药过程的AI方法。为患者选择最佳药物通常需要将患者数据（如遗传学或蛋白质组学）与药物数据（如化合物化学描述符）进行整合，以对药物的治疗效果进行评分。假设具有相似结构或目标的药物具有可比的行为或可能相互干扰，则药物相互作用的预测通常依赖于相似性度量。使用数学模型来解释药物代谢动力学和药效学数据，从而优化药物的给药方案。本文针对每个任务的最新开发的功能强大的模型进行了介绍，解释和分析。