The influence of the accuracy of mass measurements on the number of possible structural compositions and the computation time of computer-aided interpretation of mass spectrometric data has been evaluated. Experimental measurements have been performed for two model objects in the range of small and medium masses using high, ultrahigh, and extreme high resolution electrospray ionization mass spectrometers. The number of possible solutions have been examined and prospects of using machine learning in combination with mass spectrometry for predicting new data on reaction mechanisms and searching for hidden relationships in the chemical space have been demonstrated. It has been shown that there are two types of relationships between the molecular formula and the mass determination error depending on the ion mass: a nonlinear curve is observed for small molecules and a linear relationship is observed for large molecules.

中文翻译：

---

用于开发机器学习和人工智能算法的化合物的综合质谱图

已经评估了质量测量的准确性对可能的结构组成的数量和计算机辅助解释质谱数据的计算时间的影响。已使用高分辨率、超高和极高分辨率电喷雾电离质谱仪对中小质量范围内的两个模型对象进行了实验测量。已经研究了许多可能的解决方案，并证明了将机器学习与质谱结合使用来预测有关反应机制的新数据和搜索化学空间中隐藏关系的前景。已经表明，分子式与取决于离子质量的质量测定误差之间存在两种类型的关系：