Generation and validation of the first predictive pharmacophore model for cyclin-dependent kinase 9 inhibitors

Abstract

A three-dimensional (3D) pharmacophore modelling approach was applied to a diverse data set of known cyclin-dependent kinase 9 (CDK9) inhibitors. Diversity sampling and principal components analysis (PCA) were employed to ensure the rational selection of representative training sets. Twelve statistically robust pharmacophore models were generated using the HypoGen algorithm. The resulting models showed high homology and indicated great convergence in ascertaining pharmacophoric features essential for CDK9 inhibitory activity. One of the best models (Hypo 6) was assessed further by external predictive capability, randomization test, as well as its performance in virtual screening. The capability of the resulting models to reliably predict the inhibitory activity of external data sets and discriminate active structures from general databases would assist the identification and optimization of novel CDK9 inhibitors.