Deep Learning Based Metabolite Annotation.

Metabolite annotation is a major bottleneck in untargeted metabolomics studies by liquid chromatography coupled with mass spectrometry (LC-MS). This is in part due to the limited publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known compounds. Machine learning and deep learning methods provide the opportunity to predict molecular fingerprints based on MS/MS data.

Convolutional Neural Network-Based Compound Fingerprint Prediction for Metabolite Annotation.

Metabolite annotation has been a challenging issue especially in untargeted metabolomics studies by liquid chromatography coupled with mass spectrometry (LC-MS). This is in part due to the limitations of publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known metabolites. Machine learning provides the opportunity to predict molecular fingerprints based on MS/MS data.