Human microbes get closely involved in an extensive variety of complex human diseases and become new drug targets. In silico methods for identifying potential microbe–drug associations provide an effective complement to conventional experimental methods, which can not only benefit screening candidate compounds for drug development but also facilitate novel knowledge discovery for understanding microbe–drug interaction mechanisms. On the other hand, the recent increased availability of accumulated biomedical data for microbes and drugs provides a great opportunity for a machine learning approach to predict microbe–drug associations. We are thus highly motivated to integrate these data sources to improve prediction accuracy. In addition, it is extremely challenging to predict interactions for new drugs or new microbes, which have no existing microbe–drug associations.

中文翻译：

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集成图注意力网络进行人类微生物-药物关联预测

人类微生物紧密参与各种复杂的人类疾病，并成为新的药物靶标。电脑识别潜在的微生物-药物关联的方法为常规实验方法提供了有效的补充，不仅可以有益于筛选药物开发的候选化合物，还可以促进新知识的发现，以了解微生物-药物相互作用的机理。另一方面，近来微生物和药物积累的生物医学数据的可用性增加，为机器学习方法预测微生物与药物的关联提供了巨大的机会。因此，我们非常有动力整合这些数据源以提高预测准确性。此外，预测没有新的微生物-药物关联的新药或新微生物之间的相互作用极具挑战性。