Machine Learning-Based Bioactivity Classification of Natural Products Using LC-MS/MS Metabolomics.

ABSTRACT: The rediscovery of known drug classes represents a major challenge in natural products drug discovery. Compound rediscovery inhibits the ability of researchers to explore novel natural products and wastes significant amounts of time and resources. This study introduces a novel machine learning framework that can effectively characterize the bioactivity of natural products by leveraging liquid chromatography tandem mass spectrometry and untargeted metabolomics analysis. This accelerates natural product drug discovery by addressing the challenge of dereplicating previously discovered bioactive compounds. Utilizing the SIRIUS 5 metabolomics software suite and in-silico-generated fragmentation spectra, we have trained a ML model capable of predicting a compound's drug class. This approa

SUBMITTER: Brittin NJ 

PROVIDER: S-EPMC12927030 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature