Integration of virtual screening with high-throughput screening for the identification of novel Rho-kinase I inhibitors

Abstract

Rho-kinase inhibitors are effective candidates for the treatment of neural and cardiovascular disorders. The present paper reports the discovery of a novel class of ROCK-I inhibitors by integrating virtual screening with high-throughput screening methods. We developed common-feature pharmacophore models based on known representative ROCK inhibitors and employed them to screen a database of 12,280 compounds. We then applied a LigandFit model to reduce the number of hits. A new high-throughput screening model based on Kinase-Glo Luminescent Kinase Assay was established to identify inhibitors observed among the virtual screening models. Ten hits were found to have larger than 70% inhibition at 10 micromol l(-1) and were worthy of further investigation.