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often suggest that peptide and nonpeptide ligands have different modes of binding to their receptors (see
below). It is also important to point out that the discovery of nonpeptide agonists will likely provide
important structural information to advance our understanding of ligand binding and activation of
receptors as well as insight for pharmacophore modeling. In this regard, nonpeptide agonists 78 (growth
hormone-releasing peptide receptor) and 81 (cholecystokinin receptor CCK A subtype) are noteworthy
exceptions to the rule that nonpeptide screening-based leads are antagonists (see above, Figure 17).
Finally, beyond receptor targets the advantages of high-throughput screening of chemical files, natural
products, and synthetic libraries are increasing for proteases as well as other enzyme and noncatalytic
targets. An recent example of such efforts is the nonpeptide HIV protease inhibitor 90 [105], which was
originally identified from screening a chemical file. Through iterative structure-based drug design
studies, including x-ray crystallographic analysis of both ligand and inhibitor-enzyme complexes, the
pyrone template has led to the discovery of highly potent, selective, and cellularly active lead
compounds (see below). In retrospect, the original concept of superimposing the key substructural
elements of the nonpeptide ligand 90 to a known peptidomimetic inhibitor of HIV protease is illustrated
in Figure 19. A more detailed account of this effort and successful elaboration of the nonpeptide lead
structure is described below.
IV. Protein Target 3D Structural Models and Structure-Based Drug Design
A significant impact in both peptidomimetic and nonpeptide drug discovery has emerged over recent
years as the result of the determination of the 3D structures of protein targets by x-ray crystallography or
NMR spectroscopy [113–119]. In addition, computational methodologies such as QSAR, 3D
QSAR/CoMFA, homology-modeling, ligand docking, molecular dynamics, and mechanics, and solventaccessible
surface visualization have greatly impacted such research. Furthermore, programs such as
GRID, GROW, GrowMol, LEGEND, BUILDER, LUDI, FOUNDATION-SPLICE, and CONCERTS
have enabled 2D and 3D database searching and de novo design [115,120,121]. Overall, the iterative
cycle of structure-based drug design (Figure 20) has evolved to be an “engine of invention” for several
examples of peptidomimetic and nonpeptide drug discovery.
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