's Advanced Computer-Assisted Techniques in Drug Discovery, PDF

ISBN-10: 3527292489

ISBN-13: 9783527292486

ISBN-10: 3527615679

ISBN-13: 9783527615674

Using strong pcs has revolutionized molecular layout and drug discovery. completely researched and well-structured, this entire instruction manual covers powerful and effective thoughts in 3D-QSAR and complicated statistical research. The emphasis is on exhibiting clients how you can practice those equipment and steer clear of high priced and time-consuming methodical errors.

subject matters lined include
* mixture of statistical tools and molecular modeling tools
* rational use of databases
* complicated statistical techniques
* neural networks and specialist structures in molecular design

This publication addresses the practitioner in and examine, in addition to the amateur wishing to develop into familiar with sleek instruments in medicinal chemistry.

Content:
Chapter 1 creation (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 thoughts (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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New PDF release: Advanced Computer-Assisted Techniques in Drug Discovery,

Using strong pcs has revolutionized molecular layout and drug discovery. completely researched and well-structured, this complete instruction manual covers powerful and effective innovations in 3D-QSAR and complicated statistical research. The emphasis is on exhibiting clients find out how to practice those tools and keep away from high priced and time-consuming methodical blunders.

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

Example text

Pitea et al. Thus, the relationship between the naPp and the activities quantitatively supports the hypothesis that the two acyl groups must be anti-periplanar. On the other hand, the relationship between the MEP minima values and the activities suggests that the presence of electron-rich aromatic fragments enhances the activity. As a consequence, the II electron distribution seems to be directly involved in receptor recognition. Moreover, the same model accounts for the activities observed both in vitro and in vivo.

J. Med. Chem. 35, 1019- 1031 (1992) [46] Youssefyeh, R. , Campbell, H. , Airey, J. , Fitzpatrick, L. E. , J. Med. Chem. , Evrard, G. , Eur. J. Med. , Giraldo, E. , J. Med. Chem. 33, 2101-2108 (1990) 1491 Cartier, A. and Rivail, J. , Chemometrics and Intell. Lab. Pitea et al. E. , Theor. Chim. , Lasagni, M. , J. Chim. Phys. , Moro, G. , J. Comput. Chem. , Fraschini, E. and Pitea, D. On the Relationships Between the Biological Activity and the Molecular Electrostatic Potential Distribution of Tetrachlorodibenzo-p-dioxinisomers.

Nevertheless, none of the four pharmacophore Active Inactive ACTIVE 1 ACTIVE 2 ACTIVE 3 FUZZY @ Initial estimate ACTIVE 4 Figure 6. LDCT models for A11 receptor antagonists. 1 Chemometrics and Molecular Modeling 25 geometric models contains conformations of all the active compounds, thus, it is difficult to suggest a unique set of distances defining the geometric pharmacophore, and an extension of the indirect approach to other molecular descriptors seems necessary to better rationalize the activity of this set of A11 receptor antagonists.

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