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

ABSTRACT: 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 conducti

SUBMITTER: Kozlov KS 

PROVIDER: S-EPMC11911446 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature