Small Molecules Magnified by Machine Learning

Researchers at Aalto University and the University of Luxembourg report they have developed a new machine learning model that will help identify small molecules, with applications in medicine, drug discovery, and environmental chemistry. Their findings, "Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data," were published in the journal Nature Machine Intelligence. "Structural annotation of small molecules in biological samples remains a key bottleneck in untargeted metabolomics, despite rapid progress in predictive methods and tools during the past decade," wrote the researchers. "Liquid chromatography–tandem mass spectrometry, one of the most widely used analysis platforms, can detect thousands of molecules in a sample, the vast majority of which remain unidentified even with best-of-class methods. "Even the best methods can't identify more than 40% of the molecules in samples without making some additional assumptions about the candidate molecules," explained Juho Rousu, PhD, professor of computer science at Aalto University.