Machine intelligence-driven framework for optimized hit selection in virtual screening.

ABSTRACT: Virtual screening (VS) aids in prioritizing unknown bio-interactions between compounds and protein targets for empirical drug discovery. In standard VS exercise, roughly 10% of top-ranked molecules exhibit activity when examined in biochemical assays, which accounts for many false positive hits, making it an arduous task. Attempts for conquering false-hit rates were developed through either ligand-based or structure-based VS separately; however, nonetheless performed remarkably well. Here, we present an advanced VS framework-automated hit identification and optimization tool (A-HIOT)-comprises chemical space-driven stacked ensemble for identification and protein space-driven deep learning architectures for optimization of an array of specific hits for fixed protein receptors. A-HIOT implem

SUBMITTER: Kumar N 

PROVIDER: S-EPMC9306080 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature