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Article

Multi-Task Neural Networks and Molecular Fingerprints to Enhance Compound Identification from LC-MS/MS Data

Department of Earth and Environmental Sciences, University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
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Author to whom correspondence should be addressed.
Academic Editor: Lukasz Komsta
Molecules 2022, 27(18), 5827; https://doi.org/10.3390/molecules27185827
Received: 11 August 2022 / Accepted: 5 September 2022 / Published: 8 September 2022
(This article belongs to the Special Issue Chemometrics in Analytical Chemistry)
Mass spectrometry (MS) is widely used for the identification of chemical compounds by matching the experimentally acquired mass spectrum against a database of reference spectra. However, this approach suffers from a limited coverage of the existing databases causing a failure in the identification of a compound not present in the database. Among the computational approaches for mining metabolite structures based on MS data, one option is to predict molecular fingerprints from the mass spectra by means of chemometric strategies and then use them to screen compound libraries. This can be carried out by calibrating multi-task artificial neural networks from large datasets of mass spectra, used as inputs, and molecular fingerprints as outputs. In this study, we prepared a large LC-MS/MS dataset from an on-line open repository. These data were used to train and evaluate deep-learning-based approaches to predict molecular fingerprints and retrieve the structure of unknown compounds from their LC-MS/MS spectra. Effects of data sparseness and the impact of different strategies of data curing and dimensionality reduction on the output accuracy have been evaluated. Moreover, extensive diagnostics have been carried out to evaluate modelling advantages and drawbacks as a function of the explored chemical space. View Full-Text
Keywords: LC-MS/MS; chemometrics; fingerprints; similarity matching; classification; neural networks; multi-task LC-MS/MS; chemometrics; fingerprints; similarity matching; classification; neural networks; multi-task
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MDPI and ACS Style

Consonni, V.; Gosetti, F.; Termopoli, V.; Todeschini, R.; Valsecchi, C.; Ballabio, D. Multi-Task Neural Networks and Molecular Fingerprints to Enhance Compound Identification from LC-MS/MS Data. Molecules 2022, 27, 5827. https://doi.org/10.3390/molecules27185827

AMA Style

Consonni V, Gosetti F, Termopoli V, Todeschini R, Valsecchi C, Ballabio D. Multi-Task Neural Networks and Molecular Fingerprints to Enhance Compound Identification from LC-MS/MS Data. Molecules. 2022; 27(18):5827. https://doi.org/10.3390/molecules27185827

Chicago/Turabian Style

Consonni, Viviana, Fabio Gosetti, Veronica Termopoli, Roberto Todeschini, Cecile Valsecchi, and Davide Ballabio. 2022. "Multi-Task Neural Networks and Molecular Fingerprints to Enhance Compound Identification from LC-MS/MS Data" Molecules 27, no. 18: 5827. https://doi.org/10.3390/molecules27185827

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