Discovering organic reactions with a machine-learning-powered deciphering of tera-scale mass spectrometry data

Discovering organic reactions with a machine-learning-powered deciphering of tera-scale mass spectrometry data Full article

Journal

Nature Communications
ISSN: 2041-1723

Output data

Year: 2025, Volume: 16, Number: 1, Article number : 2587, Pages count : DOI: 10.1038/s41467-025-56905-8

Authors

Kozlov Konstantin S. ¹ , Boiko Daniil A. ¹ , Burykina Julia V. ¹ , Ilyushenkova Valentina V. ^1,2 , Kostyukovich Alexander Y. ¹ , Patil Ekaterina D. ^1,2 , Ananikov Valentine P. ¹

Affiliations

1	Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky Prospekt 47, Moscow, Russia
2	Center for Energy Science and Technology, Skolkovo Institute of Science and Technology, Bolshoy Boulevard 30, bld. 1, Moscow, Russia

The accumulation of large datasets by the scientific community has surpassed the capacity of traditional processing methods, underscoring the critical need for innovative and efficient algorithms capable of navigating through extensive existing experimental data. Addressing this challenge, our study introduces a machine learning (ML)-powered search engine specifically tailored for analyzing tera-scale high-resolution mass spectrometry (HRMS) data. This engine harnesses a novel isotope-distribution-centric search algorithm augmented by two synergistic ML models, assisting with the discovery of hitherto unknown chemical reactions. This methodology enables the rigorous investigation of existing data, thus providing efficient support for chemical hypotheses while reducing the need for conducting additional experiments. Moreover, we extend this approach with baseline methods for automated reaction hypothesis generation. In its practical validation, our approach successfully identified several reactions, unveiling previously undescribed transformations. Among these, the heterocycle-vinyl coupling process within the Mizoroki-Heck reaction stands out, highlighting the capability of the engine to elucidate complex chemical phenomena.

Kozlov K.S. , Boiko D.A. , Burykina J.V. , Ilyushenkova V.V. , Kostyukovich A.Y. , Patil E.D. , Ananikov V.P.
Discovering organic reactions with a machine-learning-powered deciphering of tera-scale mass spectrometry data
Nature Communications. 2025. V.16. N1. 2587 . DOI: 10.1038/s41467-025-56905-8 WOS OpenAlex

Web of science:	WOS:001446176000009
OpenAlex:	W4408488839

Пока нет цитирований