Annual Reports in Computational Chemistry, Vol. 4 by Ralph A. Wheeler, David C. Spellmeyer PDF

By Ralph A. Wheeler, David C. Spellmeyer

ISBN-10: 0444532501

ISBN-13: 9780444532503

Annual experiences in Computational Chemistry is a brand new periodical delivering well timed and significant experiences of vital themes in computational chemistry as utilized to all chemical disciplines. themes lined comprise quantum chemistry, molecular mechanics, strength fields, chemical schooling, and functions in educational and commercial settings. each one quantity is geared up into (thematic) sections with contributions written through specialists. concentrating on the newest literature and advances within the box, each one article covers a particular subject of value to computational chemists. Annual stories in Computational Chemistry is a "must" for researchers and scholars wishing to stick up to date on present advancements in computational chemistry.

* vast assurance of computational chemistry and up to date info
* themes lined contain bioinformatics, drug discovery, protein NMR, simulation methodologies, and functions in educational and commercial settings
* each one bankruptcy stories the newest literature on a selected subject of curiosity to computational chemists

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57. D. Is there a difference between leads and drugs? A historical perspective. J. Chem. Inf. Comput. Sci. 2001, 41, 1308–15. 58. Lipinski, C. Filtering in drug discovery. Ann. Reports Comput. Chem. 2005, 1, 155–68. 59. W. Druggability indices for protein targets derived from NMRbased screening data. J. Med. Chem. 2005, 48, 2518–25. 60. , Tse, C. Predicting protein druggability. Drug Discov. Today 2005, 10, 1675– 82. 61. R. The druggable genome. Nat. Rev. Drug Discov. 2002, 1, 727–30. 62. L. How many drug targets are there?

Molecular analyses are imperfect in that serendipitous binding modes are always possible. For instance, crystal structures of PXR show that ligands have multiple binding modes [50], and protein kinases can adopt multiple inactive conformations that are druggable [51]. C. 1 Predicting Selectivity and Druggability in Drug Discovery 29 lectivity, most likely because of a flip in the binding of a terminal benzimidazole group [52]. However, in protein kinases, most, if not all, of the major binding modes have probably been identified and can be used in selectivity analyses [51], although there is a possibility that allosteric sites away from the ATP active site exist [53].

D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 2002, 45, 2615–23. 57. D. Is there a difference between leads and drugs? A historical perspective. J. Chem. Inf. Comput. Sci. 2001, 41, 1308–15. 58. Lipinski, C. Filtering in drug discovery. Ann. Reports Comput. Chem. 2005, 1, 155–68. 59. W. Druggability indices for protein targets derived from NMRbased screening data. J. Med. Chem. 2005, 48, 2518–25. 60. , Tse, C. Predicting protein druggability. Drug Discov.

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Annual Reports in Computational Chemistry, Vol. 4 by Ralph A. Wheeler, David C. Spellmeyer


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