Traditional hit identification requires tailoring to each biological target and is reliant on multiple target-specific assay technologies. Combining Molecular Pool-based Active Learning (MolPAL) with a suite of in vitro biophysical methods, BioPALS shifts this paradigm by providing a standardized and data-rich hit identification platform applicable to most biological targets. The application to BRPF1b afforded a range of high-quality starting points.

Abstract

We report the development of BioPhysical and Active Learning Screening ( BioP ALS); 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. BioP ALS is a lean, data-rich, and standardized approach to hit identification applicable to a wide range of biological targets.