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function. Iterative refinement and optimization of drug leads is an effective strategy for generating
potent preclinical candidates. Structure-based design can also be used to design new chemical classes of
compounds that present similar substituents to the target using a template or scaffold which is
chemically distinct from previously characterized leads [3,4].
Structure determination typically relies on x-ray crystallography or high-field nuclear magnetic
resonance to directly visualize the 3-dimensional structure of a molecular target and the structures of
complexes of the target with drug leads. Alternately, many targets fall into identifiable classes that
frequently enable the development of homology models of the 3-dimensional target structure or a
mechanism-based strategy for drug-lead generation. Ongoing genome sequencing efforts have led to the
identification of hundreds of potential therapeutic targets, many of which represent possible sources of
crossover pharmacology. Homology modeling is a key feature of an integrated drug discovery effort
because it allows this genomics information to be utilized early in the development of target ligands or
in the engineering of ligand specificity.
Although structure-based design is an effective technology, current limitations center on the inability to
quantitatively predict how specific modifications of the lead will actually affect ligand binding affinity
[5,6]. This reflects the complexity of the drug-binding process and our inability to accurately predict the
conformational response of macromolecular structures to ligand binding. In addition, we have only
limited ability to accurately calculate molecular energy parameters or to accurately estimate the effects
of factors such as polarizability, solvation, and entropy that may have an important influence on drugbinding
energetics. Although computational methods will continue to improve, most design work (and
algorithms) still relies heavily on heuristic rules (Figure 2) that have been developed through experience
and that guide the structural and medicinal chemists in the systematic modification of lead compounds
[7]. As a practical consequence, many cycles of serial lead modification are required in order to produce
molecules of suitable potency and specificity to be considered preclinical drug candidates.
Structural information can increase the efficiency with which pharmacokinetic or toxicological liabilities
in lead compounds are eliminated by suggesting where compounds can be modified so as to alter drug
properties without affecting target potency. Structural data can also be used to direct de novo design of
alternate and distinct chemical classes of lead compounds, each of which might be expected to have a
different pharmacological profile [3,4]. New chemical compound classes can also be designed from
existing lead compounds by recombining substituents and core regions (scaffolds) from existing lead
compounds. Chemically distinct lead series can then be optimized in parallel so that when a preclinical
candidate is found to have inadequate drug properties, a backup is immediately available for preclinical
evaluation. As
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