Predictive Models for Fast and Effective Profiling of Kinase Inhibitors

In this study we
developed two-dimensional pharmacophore-based
random forest models for the effective profiling of kinase inhibitors.
One hundred seven prediction models were developed to address distinct
kinases spanning over all kinase groups. Rigorous external validation
demonstrates excellent virtual screening and classification potential
of the predictors and, more importantly, the capacity to prioritize
novel chemical scaffolds in large chemical libraries. The models built
upon more diverse and more potent compounds tend to exert the highest
predictive power. The analysis of ColBioS-FlavRC (Collection of Bioselective
Flavonoids and Related Compounds) highlighted several potentially
promiscuous derivatives with undesirable selectivity against kinases.
The prediction models can be downloaded from www.chembioinf.ro.