Algorithms for the automated selection of fragment-like molecules using single-point surface plasmon resonance measurements

Abstract

Fragment-based approaches have added to the arsenal of tools used to identify novel developable leads for drug discovery with high ligand efficiencies. A variety of label-free technologies have been developed and used throughout the industry for fragment screening. Using surface plasmon resonance (SPR) as a fragment screening platform is a relatively new approach. The miniaturization and automation of this technology has led to an associated problem: the large volume of raw data often makes it challenging to analyze and integrate the results of SPR data into the workflow of project teams engaged in the discovery process in a timely fashion. As such, several sets of equations were derived and implemented on Merck's intranet to score single sensorgrams to distinguish stable binders from weak or anomalous binders. This set of equations was optimized and validated on simulated data to both capture "fragment-like" behavior from SPR experiments and filter out much of the anomalous behavior commonly observed. It has subsequently been applied successfully to several in-house discovery programs.