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Document Figure 5 Generation of a virtual combinatorial library by finding substituents of a custom scaffold that can be accommodated in the binding site of a molecular target or meet other 3D structural criteria. Once the virtual library is synthesized in the computer, individual members can be selected using structural or additional criteria and synthesized using automated equipment. Page 532 The first step in the process is the generation of a chemical template or scaffold that can be derivatized at multiple sites using reliable chemical reactions to produce a large combinatorial library (Figure 5). This custom chemical template is designed based on the structure of the target using the same heuristic set of rules used for traditional structure-based drug design. Useful 3-dimensional pharmacophore models are best derived from crystallographic or nuclear magnetic resonance structures of the target, but can also be derived from homology models based on the structures of related targets or 3-dimensional quantitative structure-activity relationships (3D QSAR) derived from a previously discovered series of active compounds [20]. In addition, the mechanism of action of the target or any other information that exists regarding the target or the target class can be used in the design to maximize the chances of finding hits [21,22]. The combinatorial libraries are designed so that a few thousand to millions of discrete molecules can be produced by reaction of the custom-designed template with appropriate proprietary and commercially available chemical building blocks. The next step in the process involves the implementation of the automated synthesis and generation of the library. Significant lead time is anticipated before a library can be produced because even the most reliable chemical reactions require optimization if they are to be carried out by a robot, particularly if http://legacy.netlibrary.com/nlreader/nlReader.dll?bookid=12640&filename=Page_532.html [4/9/2004 12:50:09 AM]
Document Page 533 the reactions are to be implemented with the template attached to a solid support. When the synthesis is optimized and fully automated, thousands to millions of compounds are accessible. One of the key features of this process is that all of the compounds that can potentially be made by elaboration of a custom scaffold are first “made” in virtual form in a computer. Each of the chemical reactions required to create the library is encoded in a program that then systematically combines the template and the building blocks to create a 2-dimensional representation of each member of the library. Next, 3-dimensional representations are created and molecular-property descriptors are calculated for each member of the library. Molecular property descriptors encompass molecular connectivity, dipole moments, calculated partition coefficients, and many other calculable molecular properties. The virtual library of compounds can then be computationally screened and the library members ranked according to their ability to interact with the target receptor or 3-dimensional pharmacophore model [23–25]. The compounds can also be ranked by their inability to interact with any number of alternative targets whose inhibition is undesirable, or their ability to meet any range of desired chemical or physical properties that may be important in drug pharmacology. Alternately or additionally, compounds can be ranked according to their ability to span or sample the physical-chemical property space to produce the most diverse set of compounds for initial screening [26,27]. Small sublibraries of the large virtual library that best satisfy the selection criteria are chemically synthesized using automated methods and then the biological and/or chemical properties of each compound are measured using automated assays. The SAR data that emerge from the assays are stored in a central database and used in the selection process to drive additional rounds of sublibrary selection, synthesis, and assay. Multiple mathematical models are developed to correlate the computed structure and properties of each synthesized library member with the biological, chemical, or physical properties that are measured during each cycle of testing [28]. A key feature of this approach is that compounds can be selected not only on the basis of which are predicted to perform the best in the target assay but also on the basis of their ability to perform the best in the target assay but also on the basis of their ability to distinguish between or validate the SAR models that are generated. The observed and predicted properties of a given sublibrary are compared so that the set of assumptions upon which property refinement is based is constantly updated. In principle, this process can become completely automated so that leads are discovered and refined with very little manual intervention [28]. To achieve the greatest improvements in drug discovery efficiency, empirical data of various kinds must be collected throughout the iterative refinement process. It is desirable to obtain more accurate dissociation constants rather than IC 50 or single-point percent-inhibition values. In addition, the 3dimensional structures of interesting target—inhibitor complexes are determined http://legacy.netlibrary.com/nlreader/nlReader.dll?bookid=12640&filename=Page_533.html [4/9/2004 12:50:11 AM]
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Structure-based Drug Design 14 Stru
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