A first step in fragment-based drug discovery (FBDD) often entails a fragment-based screen (FBS) to identify fragment "hits." However, the integration of conflicting results from orthogonal screens remains a challenge. Here we present a meta-analysis of 35 fragment-based campaigns at Novartis, which employed a generic 1400-fragment library against diverse target families using various biophysical and biochemical techniques. By statistically interrogating the multidimensional FBS data, we sought to investigate three questions: (1) What makes a fragment amenable for FBS? (2) How do hits from different fragment screening technologies and target classes compare with each other? (3) What is the best way to pair FBS assay technologies? In doing so, we identified substructures that were privileged for specific target classes, as well as fragments that were privileged for authentic activity against many targets. We also revealed some of the discrepancies between technologies. Finally, we uncovered a simple rule of thumb in screening strategy: when choosing two technologies for a campaign, pairing a biochemical and biophysical screen tends to yield the greatest coverage of authentic hits.
Keywords: fragment; fragment-based screen; naive Bayesian model; privileged substructure.
© 2014 Society for Laboratory Automation and Screening.