Homology modeling has become an essential tool for studying proteins that are targets for medical drug design. This paper describes the approach we developed that combines sequence decomposition techniques with distance geometry algorithms for homology modeling to determine functionally important regions of proteins. We show here the application of these techniques to targets of medical interest chosen from those included in the CASP5 (Critical Assessment of Techniques for Protein Structure Prediction) competition, including the dihydroneopterin aldolase from Mycobacterium tuberculosis, RNase III of Thermobacteria maritima, and the NO-transporter nitrophorin from saliva of the bedbug Cimex lectularius. Physical chemical property (PCP) motifs, identified in aligned sequences with our MASIA program, can be used to select among different alignments returned by fold recognition servers. They can also be used to suggest functions for hypothetical proteins, as we illustrate for target T188. Once a suitable alignment has been made with the template, our modeling suite MPACK generates a series of possible models. The models can then be selected according to their match in areas known to be conserved in protein families. Alignments based on motifs can improve the structural matching of residues in the active site. The quality of the local structure of our 3D models near active sites or epitopes makes them useful aids for drug and vaccine design. Further, the PCP motif approach, when combined with a structural filter, can be a potent way to detect areas involved in activity and to suggest function for novel genome sequences.