An improved model for fragment-based lead generation at AstraZeneca

Abstract

Modest success rates in fragment-based lead generation (FBLG) projects at AstraZeneca (AZ) prompted operational changes to improve performance. In this review, we summarize these changes, emphasizing the construction and composition of the AZ fragment library, screening practices and working model. We describe the profiles of the screening method for specific fragment subsets and statistically assess our ability to follow up on fragment hits through near-neighbor selection. Performance analysis of our second-generation fragment library (FL2) in screening campaigns illustrates the complementary nature of flat and 3D fragments in exploring protein-binding pockets and highlights our ability to deliver fragment hits using multiple screening techniques for various target classes. The new model has had profound impact on the successful delivery of lead series to drug discovery projects.