Algorithm uses mass spectrometry data to predict identity of molecules

molecule

An algorithm designed by researchers from Carnegie Mellon University’s Computational Biology Department and St. Petersburg State University in Russia could help scientists identify unknown molecules. The algorithm, called MolDiscovery, uses mass spectrometry data from molecules to predict the identity of unknown substances, telling scientists early in their research whether they have stumbled on something new or merely rediscovered something already known.This development could save time and money in the search for new naturally occurring products that could be used in medicine.

Scientists waste a lot of time isolating molecules that are already known, essentially rediscovering penicillin. Detecting whether a molecule is known or not early on can save time and millions of dollars, and will hopefully enable pharmaceutical companies and researchers to better search for novel natural products that could result in the development of new drugs.

Mohimani, whose research in the Metabolomics and Metagenomics Lab focuses on the search for new, naturally occurring drugs, said after a scientist detects a molecule that holds promise as a potential drug in a marine or soil sample, for example, it can take a year or longer to identify the molecule with no guarantee that the substance is new. MolDiscovery uses mass spectrometry measurements and a predictive machine learning model to identify molecules quickly and accurately.

Mass spectrometry measurements are the fingerprints of molecules, but unlike fingerprints there’s no enormous database to match them against. Even though hundreds of thousands of naturally occurring molecules have been discovered, scientists do not have access to their mass spectrometry data. MolDiscovery predicts the identity of a molecule from the mass spectrometry data without relying on a mass spectra database to match it against.

Liu Cao et al, MolDiscovery: learning mass spectrometry fragmentation of small molecules, Nature Communications (2021). DOI: 10.1038/s41467-021-23986-0

 

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