It was at this time that the techniques of mass screening and combinatorial chemistry began to gain widespread acceptance and use. The use of mass screening and combinatorial chemistry allowed researchers to discover lead compounds in a rapid and efficient manner. As such, denovo design tools and their associated problems were no longer needed to generate lead structures. One would surmise that computer-aided drug design technology would have soon ceased to exist. On the contrary, it soon became apparent that computational tools were needed that could optimize these lead compounds into potent drugs.
The concept of drug optimization versus denovo design is an important one. The difficulty with denovo ligand generation is that an entire structure is being created from scratch. The confidence one has of accurately predicting how this structure will interact and bind within a target receptor is shaky at best. In drug optimization, we begin with a lead compound whose bound structure within the receptor has been characterized, most likely through x-ray crystallography. Subtle modifications are then performed to generate derivative compounds using structure based drug design to improve binding affinity. Because we are making much smaller changes, our faith in the validity of the resulting structures is far greater. These derivatives then undergo testing to determine which modifications improve binding. The structures of the best ligands can then be elucidated to verify the accuracy of the modifications. This refinement process continues iteratively until optimal binding ligands are produced.
Since subtle modifications are being made to a common structure, the predictive ability of ligand refinement software is much higher. This is because the effect of a single chemical modification on ligand-receptor binding is far easier to quantitate than an extreme change. No longer are we trying to determine the binding affinities of drastically different structures. Instead, we are simply determining the rank order of a list of derivative compounds. This greatly increases the confidence that proposed structures will bind in a manner consistent with our understanding.
In addition, the act of generating chemical derivatives is highly amenable to computerized automation. Consider the application of targeted structure based combinatorial chemistry as discussed above. Libraries of derivative components are assembled based upon the analysis of the active site. Because of the combinatorial nature of this method, an extremely large number of candidate structures may be possible. A computer can rapidly generate and predict the binding of all potential derivatives, creating a list of the best potential candidates. In essence, the computer filters all weak-binding compounds, allowing the chemist to focus, synthesize, and test only the most promising ligands. Thus, utilizing computer aided drug design software to aid in the refinement of weak binding lead compounds is the most effective manner in which these tools can be employed. The use of computer modeling to refine structures has become standard practice in modern drug design.
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