Diagnostic ion–molecule reactions employed in tandem mass spectrometry experiments can frequently be used to differentiate between isomeric compounds unlike the popular collision-activated dissociation methodology. Selected neutral reagents, such as 2-methoxypropene (MOP), are introduced into an ion trap mass spectrometer where they react with protonated analytes to yield product ions that are diagnostic for the functional groups present in the analytes. However, the understanding and interpretation of the mass spectra obtained can be challenging and time-consuming. Here, we introduce the first bootstrapped decision tree model trained on 36 known ion–molecule reactions with MOP. It uses the graph-based connectivity of analytes' functional groups as input to predict whether the protonated analyte will undergo a diagnostic reaction with MOP. A Cohen kappa statistic of 0.70 was achieved with a blind test set, suggesting substantial inter-model reliability on limited training data. Prospective diagnostic product predictions were experimentally tested for 13 previously unpublished analytes. We introduce chemical reactivity flowcharts to facilitate chemical interpretation of the decisions made by the machine learning method that will be useful to understand and interpret the mass spectra for chemical reactivity.

中文翻译：

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基于图的机器学习可解释和预测串联质谱中的诊断性异构体选择性离子分子反应

与流行的碰撞活化解离方法不同，串联质谱实验中使用的诊断性离子分子反应通常可用于区分异构化合物。将选定的中性试剂（例如2-甲氧基丙烯（MOP））引入离子阱质谱仪，在其中与质子化分析物发生反应，生成可诊断分析物中存在的官能团的产物离子。然而，对获得的质谱的理解和解释可能是具有挑战性和耗时的。在这里，我们介绍了第一个自举决策树模型，该模型在MOP的36种已知离子-分子反应中训练。它使用基于图的分析物官能团的连通性作为输入，以预测质子化分析物是否会与MOP进行诊断反应。盲测集获得的Cohen kappa统计量为0.70，这表明在有限的训练数据上模型间的可靠性很高。对13种先前未发布的分析物进行了实验性的前瞻性诊断产品预测。我们引入化学反应性流程图，以促进对机器学习方法所做出决定的化学解释，这对于理解和解释化学反应性质谱非常有用。