Machine learning-guided in Silico identification of Na⁺-NQR inhibitors from Berberis vulgaris and Hydrastis Canadensis phytochemicals against Vibrio cholerae.

ABSTRACT: Cholera, a life-threatening diarrheal disease caused by Vibrio cholerae, remains a global health concern. Traditional medicinal plants such as Berberis vulgaris (barberry) and Hydrastis canadensis (goldenseal) have long been used for their antimicrobial properties. This study employed integrated computational approaches to identify potential anti-cholera compounds from these plants by targeting sodium-pumping NADH-ubiquinone oxidoreductase (Na⁺-NQR). Virtual screening identified compounds 122623, 197835, and 638024, along with the control Korormicin, exhibiting favorable binding affinities and hydrogen bonding interactions. Machine learning-based prediction models were further applied to assess functional activity and binding affinity. The study incorporated ADMET (Absorption, Distribution

SUBMITTER: Bajrai LH 

PROVIDER: S-EPMC12612261 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature