Pivotal work in recent years has cast light on the importance of the human microbiome in maintenance of health and physiological response to drugs. It is now clear that gastrointestinal microbiota have the metabolic power to promote, inactivate, or even toxify the efficacy of a drug to a level of clinically relevant significance. At the same time, it appears that drug intake has the propensity to alter gut microbiome composition, potentially affecting health and response to other drugs. Since the precise composition of an individual's microbiome is unique, one's drug-microbiome relationship is similarly unique. Thus, in the age of evermore personalised medicine, the ability to predict individuals' drug-microbiome interactions is highly sought. Machine learning (ML) offers a powerful toolkit capable of characterising and predicting drug-microbiota interactions at the individual patient level. ML techniques have the potential to learn the mechanisms operating drug-microbiome activities and measure patients' risk of such occurrences. This review will outline current knowledge at the drug-microbiota interface, and present ML as a technique for examining and forecasting personalised drug-microbiome interactions. When harnessed effectively, ML could alter how the pharmaceutical industry and healthcare professionals consider the drug-microbiome axis in patient care.

近年来的关键工作揭示了人类微生物组在维持健康和对药物的生理反应方面的重要性。现在很清楚，胃肠道微生物群具有促进、灭活甚至毒化药物功效达到临床相关意义水平的代谢能力。同时，药物摄入似乎有改变肠道微生物组组成的倾向，可能影响健康和对其他药物的反应。由于个体微生物组的精确组成是独一无二的，因此一个人的药物-微生物组关系同样是独一无二的。因此，在个性化医疗时代，预测个体药物-微生物组相互作用的能力受到高度重视。机器学习 (ML) 提供了一个强大的工具包，能够在个体患者层面表征和预测药物-微生物群的相互作用。ML 技术有可能了解操作药物微生物组活动的机制并测量患者发生此类事件的风险。这篇综述将概述药物-微生物群界面的当前知识，并将 ML 作为一种检查和预测个性化药物-微生物群相互作用的技术。如果得到有效利用，ML 可以改变制药行业和医疗保健专业人员在患者护理中考虑药物-微生物组轴的方式。这篇综述将概述药物-微生物群界面的当前知识，并将 ML 作为一种检查和预测个性化药物-微生物群相互作用的技术。如果得到有效利用，ML 可以改变制药行业和医疗保健专业人员在患者护理中考虑药物-微生物组轴的方式。这篇综述将概述药物-微生物群界面的当前知识，并将 ML 作为一种检查和预测个性化药物-微生物群相互作用的技术。如果得到有效利用，ML 可以改变制药行业和医疗保健专业人员在患者护理中考虑药物-微生物组轴的方式。