Accelerating BRPF1b hit identification with BioPhysical and Active Learning Screening (BioPALS)

Abstract

We report the development of BioPhysical and Active Learning Screening (BioPALS); a rapid and versatile hit identification protocol combining AI-powered virtual screening with a GCI-driven biophysical confirmation workflow. Its application to the BRPF1b bromodomain afforded a range of novel micromolar binders with favorable ADMET properties. In addition to the excellent in silico/in vitro confirmation rate demonstrated with BRPF1b, binding kinetics were determined, and binding topologies predicted for all hits. BioPALS is a lean, data-rich, and standardized approach to hit identification applicable to a wide range of biological targets.

Keywords: BRPF1; Grating Coupled Interferometry; Hit identification; Machine Learning; Virtual Screening.