Sep 2026· Journal of Medicinal Chemistry· Vol 69 19, pp.
22565-22573
· 0 citations· 28 references
Medicine
TL;DR
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.
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.
Massive library-based platforms enable rapid early hit discovery, yet subsequent hit resynthesis and validation remain major bottlenecks in drug discovery. This delay is amplified when optimizing hits, or elaborating structures towards more advanced modalities, such as chemical inducers of proximity (CIPs). Here, we re...
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Efficient drug discovery workflows ideally generate data that directly address key translational milestones, including confirmation of target engagement and binding pose, structure–activity relationships (SARs), and biological relevance, within rapid and resource-efficient experimental cycles. Here, we describe a data-...
D. Muñoz-Reyes, K. Fieseler, M. Winokan et al.· Chemical Science· 0 citations
A transparent pre-screen can prioritise compounds ahead of structure-based calculation at a fraction of its cost, and the uncertainty and applicability-domain terms act as an abstention mechanism rather than an accuracy gain, and that abstention is not free.
BACKGROUND
Chalcones are α,β-unsaturated ketone scaffolds with broad documented bioactivity. No prior study has simultaneously integrated structural validation, PAINS assessment, chemical-space clustering, human-target classification, bioassay hit-rate benchmarking, and patent analysis within a single computational fra...
S. Arulsamy, Kiruthiga Natarajan, S. K. Krishnan et al.· Current Drug Targets· 0 citations
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