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comprise CADD means that the computational specialist will not always be intimately versed in the chemistry and biology of the program at hand. The reverse is also true; it is unlikely that someone in the laboratory will have the same command of the computational approaches as the specialist. A close coupling between the computational specialists and experimentalists allows information to flow immediately and directly between the two. This helps the CADD specialist better understand the details of the problem and so refine his/her approach. It also provides valuable information to the experimentalist that helps to guide further experimental planning and potentially make this process more efficient. The synergy provided by this relationship means that it is difficult to imagine out-sourcing being as productive as in-house effort. |
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VI. Is CADD Necessary and Worth the Investment? |
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Hopefully, by now it is clear that the questions that CADD hopes to answer are integral to a drug discovery program. As such, their answers are important to the thought processes of the experimentalists involved in the project. Especially when dealing with protein structures, computational approaches such as molecular graphics, molecular modeling, and protein/ligand docking are absolutely required. It is difficult to place a dollar figure on the time and effort that properly implemented CADD will save a company. It is not a direct route to new drugs, but rather provides a somewhat more detailed map to the goal. The hope is that by providing bits and pieces of information and by helping to coordinate that information, CADD will help to shave days and dollars off drug discovery projects. Conservative estimates are that overall, these techniques can save ten percent of a project's overall effort, more in some cases, less in others. Computer-aided drug design is often linked to analytical instrumentation. By themselves they do not discover new drugs, however the information that they provide is invaluable to the process. Boyd lists examples of the direct application of computational approaches to drug design [7]. Successes, especially in structure-based approaches, appear regularly in the literature and only a few are referenced here [21,3945]. As mentioned below, CADD approaches can become only more powerful in the future with a consequential greater return on the investment. |
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How many people are required for a CADD effort and what should be their background? This depends largely on the company, its size, its efforts, and the problems that must be solved. Most large drug companies have groups of 8 to |
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