  Kinase-targeted drug design is challenging. It requires designing inhibitors
that can bind to specific kinases when all kinase catalytic domains share a
common folding scaffold that binds ATP. Thus, obtaining the desired
selectivity, given the whole human kinome, is a fundamental task during
early-stage drug discovery. This begins with deciphering the kinase-ligand
characteristics, analyzing the structure-activity relationships, and
prioritizing the desired drug molecules across the whole kinome. Currently,
there are more than 300 kinases with released PDB structures, which provides a
substantial structural basis to gain these necessary insights. Here, we review
in silico structure-based methods - notably, a function-site interaction
fingerprint approach used in exploring the complete human kinome. In silico
methods can be explored synergistically with multiple cell-based or
protein-based assay platforms such as KINOMEscan. We conclude with new drug
discovery opportunities associated with kinase signaling networks and using
machine/deep learning techniques broadly referred to as structural biomedical
data science.
