By H. Peter Nestler (auth.), Edgar Jacoby (eds.)
The institution, research, prediction, and enlargement of a accomplished ligand-target Structure-Activity courting (SAR) within the post-genomic period provides a key study problem for this century. In Chemogenomics: equipment and Applications, specialists from academia and discover state-of-the-art in vitro and in silico methods on hand this present day and description the correct features of chemistry, biology, and molecular informatics that are the cornerstones of chemogenomics, hence combining introductory strategies with particular tools and protocols. masking subject matters equivalent to goal family-oriented compound library layout, drug discovery concentrating on the purinome and co-factor binding websites, in addition to the pocketome engine and molecular interplay box methods, the e-book emphasizes systemization as a bonus to furthering the tough technological know-how of drug discovery. As a quantity within the hugely winning Methods in Molecular Biology™ sequence structure, this paintings offers the type of designated description and implementation suggestion that's the most important for purchasing optimum results.
Authoritative and functional, Chemogenomics: equipment and Applications offers content material in an effort to be of large worth to these striving for the invention of recent and higher cures for diseases.
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Additional info for Chemogenomics: Methods and Applications
7. fi. 8. 9. 10. 11. com. com. ru. , and Timmerman, H. (2000) Handbook of Molecular Descriptors. Wiley, New York. 12. Kohonen, T. (1990) The self-organizing map. Proceedings of the IEEE 78, 1464–1480. 13. , and Wagener, M. (1998) The use of selforganizing neural networks in drug design. , and Martin, Y. C. ) 3D QSAR in Drug Design. Kluwer/ESCOM, Dordrecht, pp. 273–99. 14. , and Gasteiger, J. (1996) Locating biologically active compounds in medium-sized heterogeneous datasets by topological autocorrelation 15.
The time increases almost linearly with the size of the database. After the Kohonen network is trained, the 2D map can be created in a short time. It is important to note that focusing on physicochemical rather than structural features makes this approach complementary to any available ligand structure similarity technique. Our own experience and literature data demonstrate that SOMs are an efficient clustering, quantization, classification, and visualization tool very useful in the design of chemical libraries.
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