title: Computational Modeling of Kinase Inhibitor Selectivity.
abstract: An exhaustive computational exercise on a comprehensive set of 15 therapeutic kinase inhibitors was undertaken to identify as to which compounds hit which kinase off-targets in the human kinome. Although the kinase selectivity propensity of each inhibitor against ∼480 kinase targets is predicted, we compared our predictions to ∼280 kinase targets for which consistent experimental data are available and demonstrate an overall average prediction accuracy and specificity of ∼90%. A comparison of the predictions was extended to an additional ∼60 kinases for sorafenib and sunitinib as new experimental data were reported recently with similar prediction accuracy. The successful predictive capabilities allowed us to propose predictions on the remaining kinome targets in an effort to repurpose known kinase inhibitors to these new kinase targets that could hold therapeutic potential.
authors.affiliation: Structure, Design and Informatics, sanofi-aventis U.S., 1041 Route 202-206, P.O. Box 6800, Bridgewater, New Jersey 08807.
authors.forename: Govindan
authors.initials: G
authors.lastname: Subramanian
authors.affiliation: MayaChemTools, 4411 Cather Avenue, San Diego, California 92122.
authors.forename: Manish
authors.initials: M
authors.lastname: Sud
keywords: Kinase selectivity; binding hot spots; binding site signature; computational prediction; kinase conformation; kinase inhibitors