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

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.

Dereplication of Natural Product Antifungals via Liquid Chromatography-Tandem Mass Spectrometry and Chemical Genomics.

Recently expanded reports of multidrug-resistant fungal infections underscore the need to develop new and more efficient methods for antifungal drug discovery. A ubiquitous problem in natural product drug discovery campaigns is the rediscovery of known compounds or their relatives; accordingly, we have integrated Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) for structural dereplication and Yeast Chemical Genomics for bioprocess evaluation into a screening platform to identify such compounds early in the screening process. We identified 450 fractions inhibiting and the resistant strains of and among more than 40,000 natural product fractions.