Integrated Machine Learning and Structure-Based Virtual Screening Identifies Natural Product Targeting 50S Ribosome Inhibitory Activity Against Cutibacterium acnes

Acne vulgaris is a prevalent inflammatory disease of the pilosebaceous unit in which Cutibacterium acnes (C. acnes) contributes to lesion initiation and persistence, supporting antibacterial interventions as a component of clinical management. Given the essential role of the 50S large ribosomal subunit—particularly 23S rRNA sites in the peptidyl transferase center and nascent peptide exit tunnel—in C. acnes protein synthesis and viability, targeting the 50S offers an effective path to lead discovery for acne treatment. Here, we present an integrated computational–experimental workflow to identify anti-C. acnes candidates from a 186,659-compound natural product library. Curated 50S/23S ligands trained and validated two ML-QSAR regression models built on different molecular fingerprints (MACCS keys and PubChem 2D) to predict anti-C. acnes activity and rapidly triage the library. Compounds were further screened by ADMET filtering and structure-based docking to 23S rRNA pockets, followed by cluster and interaction analysis. Among six experimental hits, three compounds exhibited MICs against C. acnes of ≤8 μg/mL, with tripterin, a pentacyclic triterpenoid, being the most potent (0.5–2 μg/mL across two acne-relevant strains). Collectively, these results indicate that a 50S ribosomal-focused, multistage computational screening workflow, integrated with in vitro assays, efficiently prioritizes compounds with quantifiable anti-C. acnes activity across a broad range of natural products.