In a recently published study a team of researchers from the Zelinsky Institute of Organic Chemistry and Skoltech, a research group led by RAS Academician Valentin Ananikov, have developed a unique search engine based on machine-learning which can rapidly analyze large amounts of mass spectrometry data. The algorithm can analyze terabytes of data without requiring new experiments, potentially accelerating the search for new compounds while reducing costs.
Typical mass spectrometry laboratories have terabytes of data resulting from high resolution mass spectrometry experiments. Due to the limitations presented by manual analysis, nearly 95% of this data goes unexplored, leading to the potential loss of important discoveries. Manual analysis of this data could take hundreds of years, however, the new AI-based algorithm developed by the team can conduct this analysis in only a few days.
"Our work is based on an innovative algorithm combining machine learning and analysis of signal distribution in mass spectra, which has significantly reduced false positives when identifying chemical compounds. The new search algorithm has successfully verified historical data on the Mizoroki-Heck reaction and revealed not only already known, but also completely new chemical transformations, including a unique process of cross-combination that has not been previously documented in the scientific literature," said Valentin Ananikov, the scientific supervisor of the study.
The new solution could open new pathways in chemical research by identifying novel catalysts, reactions, and chemistry mechanisms that have yet gone unexplored. By analyzing existing data, the method not only accelerates scientific discovery, but it also reduces experimental costs.