Synergizing Virtual Screening and Zebrafish Models to Identify Resveratrol-Derived Antiaging Polyphenols

Abstract

Background: Telomere shortening and chronic inflammation are well-established hallmarks of aging and age-related diseases, often resulting in impaired cellular function. Identifying compounds with anti-aging potential is therefore crucial to promote healthy aging and extend lifespan. Virtual screening has emerged as a rapid and cost-effective strategy to assess the biological activity of large compound libraries. In parallel, the zebrafish (Danio rerio) model offers unique advantages for in vivo aging research and phenotypic screening. The integration of in silico and in vivo approaches has proven to enhance the efficiency and precision of therapeutic discovery. Methods: In this study, we combined ligand- and structure-based virtual screening to identify resveratrol-like polyphenols from the DrugBank database and evaluated their anti-aging effects in zebrafish models. Results: Among the top eight candidates, resveratrol and sakuranetin significantly improved telomerase-related parameters, while apigenin, genistein, and hesperetin exhibited notable anti-inflammatory activity. Conclusions: These findings underscore the value of combining computational and experimental models to accelerate the discovery of therapeutic agents targeting aging-related processes. The dual computational approach (pharmacophore similarity plus consensus docking) provided a robust prioritization pipeline directly validated in zebrafish assays.