Pharmacophore modeling and virtual screening to identify potential RET kinase inhibitors

Chemical features based 3D pharmacophore model for REarranged during Transfection (RET) tyrosine kinase were developed by using a training set of 26 structurally diverse known RET inhibitors. The best pharmacophore hypothesis, which identified inhibitors with an associated correlation coefficient of 0.90 between their experimental and estimated anti-RET values, contained one hydrogen-bond acceptor, one hydrogen-bond donor, one hydrophobic, and one ring aromatic features. The model was further validated by a testing set, Fischer’s randomization test, and goodness of hit (GH) test. We applied this pharmacophore model to screen NCI database for potential RET inhibitors. The hits were docked to RET with GOLD and CDOCKER after filtering by Lipinski’s rules. Ultimately, 24 molecules were selected as potential RET inhibitors for further investigation.

Graphical abstractFigure optionsDownload full-size imageDownload as PowerPoint slideHighlights► Chemical features based 3D pharmacophore model for RET tyrosine kinase were developed. ► We also generated a workflow that integrated this pharmacophore modeling and in silico docking to search for new RET inhibitors. ► Based on the workflow, we screened NCI database and selected 24 molecules as potential RET inhibitors for further investigation.