Advanced Computer-Assisted Techniques in Drug Discovery,

Using robust desktops has revolutionized molecular layout and drug discovery. completely researched and well-structured, this complete instruction manual covers powerful and effective strategies in 3D-QSAR and complicated statistical research. The emphasis is on displaying clients tips on how to observe those tools and stay away from high priced and time-consuming methodical errors.

subject matters coated include
* mixture of statistical tools and molecular modeling tools
* rational use of databases
* complicated statistical techniques
* neural networks and professional platforms in molecular design

This publication addresses the practitioner in and learn, in addition to the amateur wishing to turn into conversant in glossy instruments in medicinal chemistry.

Content:
Chapter 1 advent (pages 1–7): Han van de Waterbeemd
Chapter 2 3D QSAR the combination of QSAR with Molecular Modeling (pages 9–88): Demetrio Pitea, Ugo Cosentino, Giorgio Moro, Laura Bonati, Elena Fraschini, Murina Lasagni, Roberto Todeschini, Andrew M. Davis, Gabriele Cruciani and Sergio Clementi
Chapter three Rational Use of Chemical and series Databases (pages 89–162): Mark A. Johnson, Gerald M. Maggiora, Michael S. Lajiness, Joseph B. Moon, James D. Petke, Douglas C. Rohrer, Geoffrey M. Downs, Peter Willett, Paul J. Lewi and Henri Moereels
Chapter four complex Statistical strategies (pages 163–292): Jonathan A. Malpass, David W. Salt, Martyn G. Ford, E. Watcyn Wynn, David J. Livingstone, Jean?Christophe Dore, Tiiu Ojasoo, Valerie S. Rose, John wooden, Halliday J. H. MacFie and Klaus?Jurgen Schaper
Chapter five Neural Networks and specialist structures in Molecular layout (pages 293–331): David T. Manallack, David J. Livingstone, Mohammed A?Razzak and Robert C. Glen

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Extra info for Advanced Computer-Assisted Techniques in Drug Discovery, Second Edition

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FcaIc. 12 7 In these models the PLS and the least squares results are coincident as the number of PLS components equals the number of variables. r a SDEC = ‘SDEP = :$ and RSS is the Residual Sum of Squares. 4~ PRESS and PRESS is the Predicted REsidual Sum of Squares. I 6 i s pKd calc. / / Ib /‘ /’ 6 i s 9 pIO5o calc. Figure 11. Calculated vs experimental values of pKd and pZD,, obtained with models Ia and Ib. (Reprinted with permission from Ref. 41, copyright 1993, Elsevier). 32 D. Pitea et al.

Syst. A. , ZEEE Trans. Comput. , J. Am. Stat. Ass. 84, 165-175 (1989) [ 151 Todeschini, R. , Chemometrics Zntell. Lab. Syst. 16, 25 - 35 (1 992) [16] Mardia, K. , Kent, J. T. and Bibby, J. , Friedman, J. A, and Stone, C. , Classification and Regression Trees, Wadsworth & Brooks, Monterey, 1984 [19] Bratko, I. 1 Chemometrics and Molecular Modeling 37 [21] Politzer, P. and Truhlar, D. , Chemometrics Intell. Lab. Syst. , Boggia, R. , J. , Esbensen, K. , J. E. , J. Chemometrics 1, 19-31 (1987) [26] GAUSSIAN 90, Revision H: Frisch, M.

35, copyright 1990, VCH). 7) 12 (100) 13 (> 5) 14 (24) I5 ( > 5 ) Figure 5. Structure and binding affinity (ZCs0,p ~ of ) the investigated non-peptide A11 receptor antagonists. Labeled atoms are considered in the pharmacophore definition. (Reprinted with permission from Ref. 37, copyright 1993, ESCOM). After a preliminary study [36], 8 active and 5 inactive non-peptide (Fig. 5) were examined [37]. For each compound, a random search for the minimum energy conformations was performed; a large number of minimum energy conformations, within 8 kcal mol-' with respect to each global minimum, was found for a total of approximately 9000 conformations.

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