Protein kinases are important targets for designing therapeutic drugs. We describe here a computational approach to extend the usefulness of a single protein-inhibitor structure in aiding the design of protein kinase inhibitors. This approach is based on using sensitivity analysis to identify the most significant functional groups of a lead compound in accounting for binding affinity and on using comparative sequence/structure analysis to examine whether these functional groups would present specificity. A sensitivity analysis study is similar to genetic or chemical modification experiments in which specific features of a lead compound are modified to examine whether they affect properties such as binding affinity. In this study, the binding affinity was estimated by using an implicit-solvent model in which the electrostatic contributions were obtained by solving the Poisson equation, and the hydrophobic effects were accounted for by using surface-area-dependent terms. The comparative sequence/structure analysis involves the study of the amino acid distributions of a large number of protein kinases (384 in this study) near the ligand-binding sites. This analysis provides useful guiding principles for designing specific inhibitors targeted towards a particular kinase. Here, we illustrate the utility of these computational approaches by applying them to identify the determinants of the recognition between the protein kinase A and two of its inhibitors. One inhibitor, balanol, binds to the ATP-binding pocket. The other, protein kinase inhibitor, binds to the substrate-binding site. These analyses have helped to construct pharmacophore models for mining new drug leads from small-molecule libraries and for suggesting how a lead compound or a peptide inhibitor may be modified to generate selective inhibitors.