论文标题
使用图形神经网络进行质谱预测
Using Graph Neural Networks for Mass Spectrometry Prediction
论文作者
论文摘要
在许多生物学和生物医学应用中，使用质谱（MS）检测和量化细胞代谢产物已经显示出很大的希望。代谢组学的最大挑战是注释，其中测得的光谱是分配的化学身份。尽管有进步，目前的方法为测量光谱提供了有限的注释。在这里，我们使用图形神经网络（GNN）探索来预测光谱。我们模型的输入是分子图。该模型在NIST 17 LC-MS数据集上进行了训练和测试。我们将结果与Neims进行了比较，Neims是一种使用分子指纹作为输入的神经网络模型。我们的结果表明，基于GNN的模型比NEIM提供更高的性能。重要的是，我们表明排名在很大程度上取决于候选设置的大小以及候选者与目标分子的相似性，从而突出了对该域的一致，特征良好的评估协议的需求。
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.
