An Automated Software Tool for Efficient Processing and Analysis of Ligand-Observed 1H and 19F NMR Binding Data

Due to their exceptional sensitivity and robustness, NMR spectroscopy-based binding assays are routinely applied in hit finding and validation during early stages of drug discovery, in particular in the field of fragment-based lead generation. To this end, compound libraries comprised of 500-3000 molecules are screened in mixtures by ligand-observed NMR binding experiments, and in addition, focused sets comprised of individual compounds are routinely assessed in follow-up activities. Most commonly, proton-detected experiments such as STD, T1ρ and WaterLOGSY are utilized and more recently fluorine-detected relaxation-edited experiments. While some level of automation is generally implemented for sample preparation and data acquisition, the subsequent data analysis of a high number of spectra remains largely a manual and slow process, which presents a critical bottleneck in NMR-based binding studies. Here, we report a novel software tool that processes, analyzes, and qualifies ligand-observed proton and fluorine NMR binding data in a fully automated fashion. The challenges associated with such complex data are addressed by carefully designed processes and filters built into the analysis. The output consists of a hit list and an interactive graphical presentation of the spectra and analysis results for easy user inspection and validation. The scope, performance and limitations of the tool are demonstrated on three datasets comprised of 19F-detected mixture screening binding experiments, as well as 1H-detected mixture and single compound binding experiments. From the comparison of automated and manual analysis results, we conclude that the program delivers robust, high-confidence hit lists in a fraction of the time needed for manual analysis and greatly facilitates the visual inspection of the associated NMR spectra. These features enable considerably shorter turn-around times, higher throughput and thereby greater impact of NMR-based binding experiments