Computational Drug Repurposing for Antituberculosis Therapy: Discovery of Multi-Strain Inhibitors.

ABSTRACT: Tuberculosis remains the most afflicting infectious disease known by humankind, with one quarter of the population estimated to have it in the latent state. Discovering antituberculosis drugs is a challenging, complex, expensive, and time-consuming task. To overcome the substantial costs and accelerate drug discovery and development, drug repurposing has emerged as an attractive alternative to find new applications for "old" drugs and where computational approaches play an essential role by filtering the chemical space. This work reports the first multi-condition model based on quantitative structure-activity relationships and an ensemble of neural networks (mtc-QSAR-EL) for the virtual screening of potential antituberculosis agents able to act as multi-strain inhibitors. The mtc-QSAR-EL m

SUBMITTER: Kleandrova VV 

PROVIDER: S-EPMC8388932 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature