A Comprehensive 19F NMR Framework for Fragment-Based Drug Discovery: The Validated Screening Library OpenFL600 and Efficient Affinity Ranking by CSAR

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Authors: Simon Rüdisser, Gabriela R. Stadler, Giacomo Padroni, Barbash, Daria, M. Ruckstuhl, Nils Lorz, Christina Jordan, Sarah Skibinski, Timo T. Stühlinger, Sarah E. Kratzwald, Katharina M. Siess, Sven Brüschweiler
+5 more Gerald Platzer, Antoine Cléry, Robert Konrat, Frédéric H.‐T. Allain, Alvar D. Gossert
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Abstract

Small-molecule drug discovery relies on the identification of initial chemical starting points, or screening hits, and their subsequent optimization for potency and selectivity. Fragment-based drug discovery (FBDD), particularly through ¹⁹F NMR screening, is a powerful method for hit identification due to its high sensitivity. Here, we present the design and validation of a non-proprietary NMR screening library (OpenFL600) capable of probing diverse binding pockets, as demonstrated for RNA, G protein-coupled receptors (GPCRs), kinases, protein-protein interactions, and proteases. Our screening campaigns yielded specific hits for 11 respective targets and therefore valuable structure-activity relationship (SAR). The absence of promiscuous binders confirms the high quality of the OpenFL600 library, a result of both careful fragment selection and rigorous molecular quality control. Subsequent affinity ranking of identified hits-typically a laborious task-is considerably streamlined by fast chemical shift anisotropy (CSA) Affinity Ranking (CSAR). CSAR is enabled by providing CSA values for each fragment. This creates a highly efficient workflow, from initial hit screening to the prioritization of leads. In summary, we provide a comprehensive framework for NMR-based screening across diverse target classes, assessing their druggability, and enabling efficient affinity ranking to facilitate hit-to-lead progression.

Fields of Study & Subjects

Advanced NMR Techniques and Applications Computational Drug Discovery Methods Chemical Synthesis and Analysis

Publication details

Publication date
9 January 2026
Language
EN
Document type
Dataset