Metabolomics using nontargeted tandem mass spectrometry can detect thousands of molecules in a biological sample. However, structural molecule annotation is limited to structures present in libraries or databases, restricting analysis and interpretation of experimental data. Here we describe CANOPUS (class assignment and ontology prediction using mass spectrometry), a computational tool for systematic compound class annotation. CANOPUS uses a deep neural network to predict 2,497 compound classes from fragmentation spectra, including all biologically relevant classes. CANOPUS explicitly targets compounds for which neither spectral nor structural reference data are available and predicts classes lacking tandem mass spectrometry training data. In evaluation using reference data, CANOPUS reached very high prediction performance (average accuracy of 99.7% in cross-validation) and outperformed four baseline methods. We demonstrate the broad utility of CANOPUS by investigating the effect of microbial colonization in the mouse digestive system, through analysis of the chemodiversity of different Euphorbia plants and regarding the discovery of a marine natural product, revealing biological insights at the compound class level.

使用非靶向串联质谱的代谢组学可以检测生物样品中的数千个分子。然而，结构分子注释仅限于文库或数据库中存在的结构，限制了实验数据的分析和解释。在这里，我们描述 CANOPUS（使用质谱的类分配和本体预测），一种用于系统化合物类注释的计算工具。CANOPUS 使用深度神经网络从碎片谱中预测 2,497 种化合物类别，包括所有生物学相关类别。CANOPUS 明确针对既没有光谱也没有结构参考数据的化合物，并预测缺乏串联质谱训练数据的类别。在使用参考数据的评估中，CANOPUS 达到了非常高的预测性能（交叉验证的平均准确度为 99.7%），并且优于四种基线方法。我们通过研究小鼠消化系统中微生物定植的影响、分析不同大戟属植物的化学多样性以及关于海洋天然产物的发现，展示了 CANOPUS 的广泛实用性，揭示了化合物类别水平的生物学见解。