Machine Learning Guided Discovery of Microbiome Metabolites That Inhibit HDAC

Abstract Histone deacetylase (HDAC) is a family of key epigenetic regulator implicated in inflammation, metabolism, and cancer. Microbiome metabolites can modulate host histone acetylation, yet systematic identification of metabolites that target HDAC remains limited. Here we present a computationally driven discovery pipeline that combines training data composition optimization with experimental validation to prioritize microbiome-derived HDAC inhibitors. Starting from a large, public HDAC3 screening data set (314,129 compounds; 485 actives), we developed an automated iterative sampling strategy that balances active and inactive compounds while enriching the inactive class for metabolite-like chemistry. Models trained on the balanced metabolite-enriched subsets achieved substantially higher sensitivity and balanced accuracy than models trained on the full data set. Consensus predictions from multiple optimized runs were applied to a curated microbiome metabolite database to prioritize candidates for testing. Two top candidates, 5-(hydroxymethyl)furoic acid (HMFA) and d-glucuronolactone (DGL), were evaluated using an HDAC3-specific biochemical assay, followed by broader HDAC activity assessment. Both metabolites exhibited mild inhibitory activity overall. Docking simulations suggested that HMFA may interact with the HDAC3 catalytic site. Together, we show that targeted training set composition can improve machine learning-assisted discovery of microbiome-derived small molecule inhibitors and identified HMFA as a microbiome-associated metabolite with measurable in vitro HDAC inhibitory activity.