Subramanian Govindan, Sud Manish
Structure, Design and Informatics, sanofi-aventis U.S., 1041 Route 202-206, P.O. Box 6800, Bridgewater, New Jersey 08807.
MayaChemTools, 4411 Cather Avenue, San Diego, California 92122.
ACS Med Chem Lett. 2010 Jul 28;1(8):395-9. doi: 10.1021/ml1001097. eCollection 2010 Nov 11.
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.
我们针对一组全面的15种治疗性激酶抑制剂进行了详尽的计算分析，以确定哪些化合物作用于人类激酶组中的哪些激酶脱靶位点。尽管预测了每种抑制剂对约480个激酶靶点的激酶选择性倾向，但我们将预测结果与约280个有一致实验数据的激酶靶点进行了比较，结果显示总体平均预测准确率和特异性约为90%。随着最近有新的实验数据报道，我们将索拉非尼和舒尼替尼的预测比较扩展到另外约60种激酶，预测准确率相似。成功的预测能力使我们能够对其余的激酶组靶点进行预测，从而尝试将已知的激酶抑制剂重新用于这些可能具有治疗潜力的新激酶靶点。
