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. The predicted molecular fingerprints can then be used to help rank candidate metabolite IDs obtained based on predicted formula or measured precursor m/z of the unknown metabolite. This approach is particularly useful to help annotate metabolites whose corresponding MS/MS spectra cannot be matched with those in spectral libraries. We previously reported application of a convolutional neural network (CNN) for molecular fingerprint prediction using MS/MS spectra obtained from the MoNA repository and NIST 20. In this paper, we investigate high-dimensional representation of the spectral data and molecular fingerprints to improve accuracy in molecular fingerprint prediction.

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. The predicted molecular fingerprints can then be used to help rank putative metabolite IDs obtained by using either the precursor mass or the formula of the unknown metabolite. This method is particularly useful to help annotate metabolites whose corresponding MS/MS spectra are missing or cannot be matched with those in accessible spectral libraries. We investigated a convolutional neural network (CNN) for molecular fingerprint prediction based on data acquired by MS/MS. We used more than 680,000 MS/MS spectra obtained from the MoNA repository and NIST 20, representing about 36,000 compounds for training and testing our CNN model. The trained CNN model is implemented as a python package, MetFID. The package is available on GitHub for users to enter their MS/MS spectra and corresponding putative metabolite IDs to obtain ranked lists of metabolites. Better performance is achieved by MetFID in ranking putative metabolite IDs using the CASMI 2016 benchmark dataset compared to two other machine learning-based tools (CSI:FingerID and ChemDistiller).