Objectives: Protein kinases orchestrate activation of signalling cascades in response to extra- and intracellular stimuli for regulation of cell proliferation. They are directly involved in a variety of diseases, particularly cancers. Systems biology approaches have become increasingly important in understanding regulatory frameworks in cancer, and thus may facilitate future anti-cancer discoveries. Moreover, it has been suggested and confirmed that high-throughput virtual screening provides a novel, effective way to reveal small molecule protein kinase inhibitors. Accordingly, we aimed to identify kinase targets and novel kinase inhibitors. Materials and methods: A series of bioinformatics methods, such as network construction, molecular docking and microarray analyses were performed. Results: In this study, we computationally constructed the appropriate global human protein-protein interaction network with data from online databases, and then modified it into a kinase-related apoptotic protein-protein interaction network. Subsequently, we identified several kinases as potential drug targets according to their differential expression observed by microarray analyses. Then, we predicted relevant microRNAs, which could target the above-mentioned kinases. Ultimately, we virtually screened a number of small molecule natural products from Traditional Chinese Medicine (TCM)@Taiwan database and identified a number of compounds that are able to target polo-like kinase 1, cyclin-dependent kinase 1 and cyclin-dependent kinase 2 in HeLa cervical carcinoma cells. Conclusions: Taken together, all these findings might hopefully facilitate discovery of new kinase inhibitors that could be promising candidates for anti-cancer drug development.