Screening of antibacterial compounds with novel structure from the FDA approved drugs using machine learning methods.

ABSTRACT: Bacterial infection is one of the most important factors affecting the human life span. Elderly people are more harmed by bacterial infections due to their deficits in immunity. Because of the lack of new antibiotics in recent years, bacterial resistance has increasingly become a serious problem globally. In this study, an antibacterial compound predictor was constructed using the support vector machines and random forest methods and the data of the active and inactive antibacterial compounds from the ChEMBL database. The results showed that both models have excellent prediction performance (mean accuracy >0.9 and mean AUC >0.9 for the two models). We used the predictor to screen potential antibacterial compounds from FDA-approved drugs in the DrugBank database. The screening results showe

SUBMITTER: Li WX 

PROVIDER: S-EPMC8876917 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature