Abstract
Efficiency metrics are a simple and effective medicinal chemistry tool to track small molecule progress toward a preferred profile in lead optimization. Targeted protein degradation can be mediated by small molecules that act as a molecular glue between an E3 ligase and a protein target. Molecular glue compounds are characterized by the potency and the depth of their protein degradation dose response measurement, representing additional complexity toward identifying drug candidates. We developed degradation efficiency metrics that are based on both potency and depth of degradation. They serve as basic scoring functions to effectively track lead optimization objectives. In recent years, applying machine learning (ML) has effectively accelerated lead optimization. We established a comprehensive scoring function to guide molecular glue design. This manuscript describes how such a merit score was retrospectively applied to track optimization of a clinical molecular glue degrader series that resulted in the identification of Golcadomide (CC-99282). The application of these efficiency metrics in conjunction with a ML based merit score may accelerate identification of glue molecules development candidates.