Molecular Modeling and Prediction of Bioactivity

1. Front Matter

   Pages i-xvi

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2. Overview

   1. Front Matter

      Pages 1-1

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   2. Strategies for Molecular Design Beyond the Millennium

      • James P. Snyder, Forrest D. Snyder

      Pages 3-23

3. New Developments and Applications of Multivariate QSAR

   1. Front Matter

      Pages 25-25

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   2. Multivariate Design and Modelling in QSAR, Combinatorial Chemistry, and Bioinformatics

      • Svante Wold, Michael Sjöström, Per M. Andersson, Anna Linusson, Maria Edman, Torbjörn Lundstedt et al.

      Pages 27-45

   3. QSAR Study of PAH Carcinogenic Activities: Test of a General Model for Molecular Similarity Analysis

      • William C. Herndon, Hung-Ta Chen, Yumei Zhang, Gabrielle Rum

      Pages 47-52

   4. Comparative Molecular Field Analysis of Aminopyridazine Acetylcholinesterase Inhibitors

      • Wolfgang Sippl, Jean-Marie Contreras, Yveline Rival, Camille G. Wermuth

      Pages 53-58

   5. The Influence of Structure Representation on QSAR Modelling

      • Marjana Novič, Matevž Pompe, Jure Zupan

      Pages 59-64

   6. The Constrained Principal Property (CPP) Space in QSAR — Directional and Non-Directional Modelling Approaches

      • Lennart Eriksson, Patrik Andersson, Erik Johansson, Mats Tysklind, Maria Sandberg, Svante Wold

      Pages 65-70

4. The Future of 3D-QSAR

   1. Front Matter

      Pages 71-71

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   2. Handling Information from 3D Grid Maps for QSAR Studies

      • Gabriele Cruciani, Manuel Pastor, Sergio Clementi

      Pages 73-81

   3. Gaussian-Based Approaches to Protein-Structure Similarity

      • Jordi Mestres, Douglas C. Rohrer, Gerald M. Maggiora

      Pages 83-88

   4. Molecular Field-Derived Descriptors for the Multivariate Modeling of Pharmacokinetic Data

      • Wolfgang Guba, Gabriele Cruciani

      Pages 89-94

   5. Validating Novel QSAR Descriptors for Use in Diversity Analysis

      • Robert D. Clark, Michael Brusati, Robert Jilek, Trevor Heritage, Richard D. Cramer

      Pages 95-100

5. Prediction of Ligand-Protein Binding

   1. Front Matter

      Pages 101-101

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   2. Structural and Energetic Aspects of Protein-Ligand Binding in Drug Design

      • Gerhard Klebe, Markus Böhm, Frank Dullweber, Ulrich Grädler, Holger Gohlke, Manfred Hendlich

      Pages 103-110

   3. Use of MD-Derived Shape Descriptors as a Novel Way to Predict the in Vivo Activity of Flexible Molecules

      • Abdelaziz Yasri, Michel Kaczorek, Roger Lahana, Gérard Grassy, Roland Buelow

      Pages 111-121

   4. A View on Affinity and Selectivity of Nonpeptidic Matrix Metalloproteinase Inhibitors from the Perspective of Ligands and Target

      • Hans Matter, Wilfried Schwab

      Pages 123-128

   5. On the Use of SCRF Methods in Drug Design Studies

      • Modesto Orozco, Carles Colominas, Xavier Barril, F. Javier Luque

      Pages 129-134

   6. 3D-QSAR Study of 1,4-Dihydropyridines Reveals Distinct Molecular Requirements of Their Binding Site in the Resting and the Inactivated State of Voltage-Gated Calcium Channels

      • Klaus-Jürgen Schleifer, Edith Tot, Hans-Dieter Höltje

      Pages 135-140

Much of chemistry, molecular biology, and drug design, are centered around the relationships between chemical structure and measured properties of compounds and polymers, such as viscosity, acidity, solubility, toxicity, enzyme binding, and membrane penetration. For any set of compounds, these relationships are by necessity complicated, particularly when the properties are of biological nature. To investigate and utilize such complicated relationships, henceforth abbreviated SAR for structure-activity relationships, and QSAR for quantitative SAR, we need a description of the variation in chemical structure of relevant compounds and biological targets, good measures of the biological properties, and, of course, an ability to synthesize compounds of interest. In addition, we need reasonable ways to construct and express the relationships, i. e. , mathematical or other models, as well as ways to select the compounds to be investigated so that the resulting QSAR indeed is informative and useful for the stated purposes. In the present context, these purposes typically are the conceptual understanding of the SAR, and the ability to propose new compounds with improved property profiles. Here we discuss the two latter parts of the SARlQSAR problem, i. e. , reasonable ways to model the relationships, and how to select compounds to make the models as "good" as possible. The second is often called the problem of statistical experimental design, which in the present context we call statistical molecular design, SMD. 1.

• chemistry
• chemometrics
• development
• drug discovery
• future
• protein
• receptor

• H. Lundbeck A/S, Valby, Denmark

  Klaus Gundertofte

• Royal Danish School of Pharmacy, Copenhagen, Denmark

  Flemming Steen Jørgensen

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