Machine learning techniques can be applied to MALDI-TOF mass spectral data of drug-treated cells to obtain classification models which assign the mechanism of action of drugs. Here, we present an example application of this concept to the screening of antibacterial drugs that act at the major bacterial target sites such as the ribosome, penicillin-binding proteins, and topoisomerases in a pharmacologically relevant phenotypic setting. We show that antibacterial effects can be identified and classified in a label-free, high-throughput manner using wild-type Escherichia coli and Staphylococcus aureus cells at variable levels of target engagement. This phenotypic approach, which combines mass spectrometry and machine learning, therefore denoted as PhenoMS-ML, may prove useful for the identification and development of novel antibacterial compounds and other pharmacological agents.

中文翻译：

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质谱中的机器学习：MALDI-TOF MS方法进行表型抗菌素筛选。

机器学习技术可以应用于药物处理过的细胞的MALDI-TOF质谱数据，以获得分配药物作用机理的分类模型。在这里，我们提出这个概念的示例应用，以筛选在药理学上相关的表型环境中作用于主要细菌靶位的抗菌药物，例如核糖体，青霉素结合蛋白和拓扑异构酶。我们表明，可以使用野生型大肠杆菌和金黄色葡萄球菌以无标签，高通量的方式鉴定和分类抗菌作用单元处于不同水平的目标参与度。这种将质谱和机器学习相结合的表型方法，因此被称为PhenoMS-ML，可能被证明可用于鉴定和开发新型抗菌化合物和其他药物。