Detecting and quantifying products of cellular metabolism using Mass Spectrometry (MS) has already shown great promise in many biological and biomedical applications. The biggest challenge in metabolomics is annotation, where measured spectra are assigned chemical identities. Despite advances, current methods provide limited annotation for measured spectra. Here, we explore using graph neural networks (GNNs) to predict the spectra. The input to our model is a molecular graph. The model is trained and tested on the NIST 17 LC-MS dataset. We compare our results to NEIMS, a neural network model that utilizes molecular fingerprints as inputs. Our results show that GNN-based models offer higher performance than NEIMS. Importantly, we show that ranking results heavily depend on the candidate set size and on the similarity of the candidates to the target molecule, thus highlighting the need for consistent, well-characterized evaluation protocols for this domain.

中文翻译：

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使用图神经网络进行质谱预测

在许多生物和生物医学应用中，使用质谱（MS）检测和定量细胞代谢产物已显示出巨大的希望。代谢组学中最大的挑战是注释，在其中为测得的光谱分配化学身份。尽管取得了进步，但当前的方法为测量的光谱提供的注释有限。在这里，我们探索使用图神经网络（GNN）预测光谱。我们模型的输入是分子图。该模型在NIST 17 LC-MS数据集上经过训练和测试。我们将结果与NEIMS（一种利用分子指纹作为输入的神经网络模型）进行比较。我们的结果表明，基于GNN的模型比NEIMS具有更高的性能。重要的，