Computer-Assisted Lead Finding and Optimization
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Han van der Waterbeemd, Bernard Testa, Gerd Folkers (Eds.)

Computer-Assisted Lead Finding and Optimization

 

 

 

 

 

ISBN: 3-906390-16-0
Hardcover
566 pages
June 1997

Description

Computer-assisted techniques are well-integrated in modern drug discovery and used for the finding of new leads, the optimization of receptor or enzyme affinity, as well as of pharmacokinetic and physicochemical properties.
In this book an account is found of current strategies used in computer-assisted drug design. Important topics include progress in chemometrics, molecular modeling and three-dimensional QSAR approaches. Relatively new mathematical methods such as genetic algorithms or artificial neural networks and fuzzy logic have found their application in rational molecular design. As is amply illustrated, based on recent developments in these disciplines, important progress has been made in lead finding strategies. This is of great importance to the pharmaceutical industry.
Thus, all scientists investigating quantitative structure-activity relationships in their broadest sense, in medicinal, agricultural, or environmental chemistry will benefit from this book.
 

Index

  1. PART I: CHEMOMETRICS
    1. A General View on Similarity and QSAR Studies
    2. Modern Tools in Data Analysis
    3. Modelling without Boundary Conditions: An Issue in QSAR Validation
    4. Polynomially Expanded PLS (PE-PLS): A Simple but Efficient Way to Handle Nonlinear QSAR Models
  2. PART II: LEAD FINDING STRATEGIES
    1. Combinatorial Docking and Genetic Algorithms in Lead Finding
    2. Quantification of Molecular Similarity and Diversity
    3. Intelligent Combinatorial Libaries
    4. Exploiting Molecular Diversity Using Pharmacophore Searching and Compound Clustering
    5. Combinatorial Chemistry Library Diversity Measurement: A Comparison of 2D and 3D Metrics
  3. PART III: COMPUTATIONAL APPROACHES IN DRUG DISPOSITION
    1. Physicochemical Properties in Drug Metabolism and Pharmacokinetics
    2. QSAR Approaches in Mutagenecity and Carcinogenicity Estimation
    3. Dealing with Metabolic Variability during Drug Development
    4. Molecular Modelling of Cytochromes P450
  4. PART IV: 3D-QSAR MOLECULAR MODELLING
    1. Region Selection in 3D-QSAR
    2. Docking of Flexible Ligands into Active Sites of Protein Receptors
    3. Strategies for Modelling Protein-Carbohydrate Interactions
    4. Molecular Modelling and 3D-QSAR Studies on 5-HT Reuptake Inhibitors
    5. New 3D-QSAR Metodology
  5. PART V: FUTURE CHALLENGES
    1. Soft Modelling of Biological Data with Fuzzy Sets and Fuzzy Logic
    2. Complex Systems: On the Simulation of Nature and the Nature of Simulation
    3. Genetic Selection Test Series
    4. Protein Modelling and Ligand Design: Challenges in Computer-Assisted Approaches