Integrating UPLC-QTOF-MS Metabolomics and Multiple Bioactivity Phenotypes Reveals Shared Chemical Markers in Fabaceae

This study presents a KNIME-based biochemometric workflow integrating UPLC-QTOF-MS metabolomics data with three biological responses (DPPH, IL-10, and PGE₂) from 47 Fabaceae extracts. The combination of four complementary statistical approaches enabled the prioritization of molecular features, the identification of three features shared across all three biological endpoints, and four tentative chemical annotations through MS/MS analysis. These findings highlight the potential of this integrated, reproducible, and adaptable strategy to guide chemical dereplication and the investigation of potentially bioactive metabolites in complex natural product extracts.