MolDiscovery: learning mass spectrometry fragmentation of small molecules

Liu Cao, Mustafa Guler, Azat Tagirdzhanov, Yi-Yuan Lee, Alexey Gurevich and Hosein Mohimani ()
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Liu Cao: Carnegie Mellon University
Mustafa Guler: Carnegie Mellon University
Azat Tagirdzhanov: St. Petersburg State University
Yi-Yuan Lee: Carnegie Mellon University
Alexey Gurevich: St. Petersburg State University
Hosein Mohimani: Carnegie Mellon University

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.

Date: 2021
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DOI: 10.1038/s41467-021-23986-0

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