An approach of molecular-fingerprint prediction implementing a GAT.

In the domain of metabolomics, the accurate identification of compounds is paramount. However, this process is hindered by the vast number of metabolites, which poses a significant challenge. In this study, a novel approach to compound identification is proposed, namely a molecular-fingerprint prediction method based on the graph attention network (GAT) model. The method involves the processing of fragmentation-tree data derived from tandem mass spectrometry (MS/MS) data computation and the subsequent processing of fragmentation-tree graph data with a technique inspired by natural language processing. The model is then trained using a 3-layer GAT model and a 2-layer linear layer. The results demonstrate the method's efficacy in molecular-fingerprint prediction, with the prediction of molecular fingerprints from MS/MS spectra exhibiting a high degree of accuracy. Firstly, this model achieves excellent performance in receiver operating characteristic (ROC) and precision-recall curves. The factors that have the most influence on the resultant performance are identified as edge features using different training parameters. Then, better performance is achieved for accuracy and