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

Ligand-based F-19 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-19 NMR chemical shifts. Here, we introduce a complementary knowledge-based F-19 NMR screening approach, named (19)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-19 NMR chemical shifts. In this study, we developed a novel approach, named LEFshift, for F-19 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.

Automated summary

Ligand-based F-19 NMR screening is an effective method for discovering active compounds, especially for fragment screening. By assembling a large, diverse, well-designed, and characterized fragment library that is screened in mixtures based on experimental F-19 NMR chemical shifts, fluorinated fragment libraries can be generated. In addition, a knowledge-based screening approach, called 19Focused screening, allows for efficient screening of putative active molecules selected by computational hit finding methodologies in mixtures assembled and deconvoluted on-the-fly based on predicted F-19 NMR chemical shifts.