Efficient Screening of Target-Specific Selected Compounds in Mixtures by 19 F NMR Binding Assay with Predicted 19 F NMR Chemical Shifts

• Published in: ChemMedChem Vol. 17; no. 13; p. e202200163
• Main Authors: Vulpetti, Anna, Lingel, Andreas, Dalvit, Claudio, Schiering, Nikolaus, Oberer, Lukas, Henry, Chrystelle, Lu, Yipin
• Format: Journal Article
• Language: English
• Published: Germany 05.07.2022
• Subjects: Fragment based screening; 19F NMR screening; Hit finding; 19F NMR chemical shift; Machine learning
• ISSN: 1860-7179; 1860-7187
• DOI: 10.1002/cmdc.202200163

Ligand-based F NMR screening is a highly effective and well-established hit-finding approach. The high sensitivity to protein binding makes it particularly suitable for fragment screening. Different criteria can be considered for generating fluorinated fragment libraries. One common strategy is to assemble a large, diverse, well-designed and characterized fragment library which is screened in mixtures, generated based on experimental F NMR chemical shifts. Here, we introduce a complementary knowledge-based F NMR screening approach, named Focused screening, enabling the efficient screening of putative active molecules selected by computational hit finding methodologies, in mixtures assembled and on-the-fly deconvoluted based on predicted F NMR chemical shifts. In this study, we developed a novel approach, named LEFshift, for F NMR chemical shift prediction using rooted topological fluorine torsion fingerprints in combination with a random forest machine learning method. A demonstration of this approach to a real test case is reported.