Automated High-Throughput Ligand-Detected NMR for Medicinal Chemistry Hit Triage and Binding Assessment

Abstract

Early discovery campaigns routinely generate screening hits whose progression is limited by uncertain aqueous solubility, chemical integrity, and target engagement. We report a fully automated, ligand-detected high-throughput NMR (HT-NMR) workflow, applicable to aromatic-ring-containing compounds common to high throughput screening libraries, which delivers compound triage and binding assessment. Automated Echo/Tecan sample preparation is validated by Artel calibration and qNMR benchmarking, and band-selective SOFAST experiments centered on the aromatic region enable rapid acquisition in assay buffers. qNMR-SOFAST quantifies aqueous concentrations and flags degradation/impurities, while PE CPMG-SOFAST and STD-SOFAST provide orthogonal target engagement readouts. Python-based tools automate processing, analysis, and reporting. Ligand signals are detected down to 20 μM with quantitation error <25%, allowing binding decisions to be made from as few as four paired samples, <100 μg, per compound. A three-tier decision framework classifies hits by confidence and directs follow-up to orthogonal assays for affinity estimation, accelerating triage and reducing false progression.