Large-scale prediction of human kinase-inhibitor interactions using protein sequences and molecular topological structures.

The kinase family is one of the largest target families in the human genome. The family's key function in signal transduction for all organisms makes it a very attractive target class for the therapeutic interventions in many diseases states such as cancer, diabetes, inflammation and arthritis. A first step toward accelerating kinase drug discovery process is to fast identify whether a chemical and a kinase interact or not. Experimentally, these interactions can be identified by in vitro binding assay - an expensive and laborious procedure that is not applicable on a large scale. Therefore, there is an urgent need to develop statistically efficient approaches for identifying kinase-inhibitor interactions. For the first time, the quantitative binding affinities of kinase-inhibitor pairs are differentiated as a measurement to define if an inhibitor interacts with a kinase, and then a chemogenomics framework using an unbiased set of general integrated features (drug descriptors and protein descriptors) and random forest (RF) is employed to construct a predictive model which can accurately classify kinase-inhibitor pairs. Our results show that RF with integrated features gave prediction accuracy of 93.76%, sensitivity of 92.26%, and specificity of 95.27%, respectively. The results are superior to those by only considering two separated spaces (chemical space and protein space), demonstrating that these integrated features contribute cooperatively. Based on the constructed model, we provided a high confidence list of drug-target associations for subsequent experimental investigation guidance at a low false discovery rate.