This volume teaches readers how to apply traditional ligand-based quantitative drug design methods, as a complement to recent 3D macromolecular structure-based methods. The text requires only a modest training in chemistry. Each subject is treated in enough detail that readers could apply the method to their own data. The book begins at an introductory level and builds from there. It includes a comprehensive integration of physical and computational chemistry, physical biochemistry, physical organic chemistry, and statistics at understandable levels. INDICE: Overview of Quantitative Drug Design Stages of Drug Discovery Computer Descriptions of Changes in Structure Related to Changes in Properties of Molecules Noncovalent Interactions in Biological Systems Factors That Influence the Strength of an Interaction The Importance of Water Electrostatic Interactions Hydrogen Bonds Dispersion Interactions Charge-Transfer Interactions Hydrophobic Interactions Steric Repulsion Preparation of 3D Structures of Molecules for 3D QSAR Preliminary Inspection of Molecules Generating 3D Structures of Molecules Strategies to Select the Conformation for 3D QSAR Calculating Physical Properties of Molecules The Electronic Properties of Molecules The Hydrogen-Bonding Properties of Molecules The Size of Substituents and Shape of Molecules The Hydrophobic Properties of Molecules Indicator or Substructure VariablesComposite Descriptors Calculated from 2D Structures Properties Calculated from the 3D Structure of the Ligand-Macromolecule Complex Organizing Molecular Properties for 3D QSAR Biological Data Consequences of Ligand-Biomolecule Interaction Selection of Data for Analysis: Characteristics of an Ideal Biological Test Calculation of Relative Potency Choice of Classification Boundaries Form of Equations Relating Potency and Physical Properties Introduction to Model-Based Equations Equation for an Equilibrium Model for Ionizable Compounds for Which Affinity Is a Function of Log P and Only the Neutral Form Binds Equations for Equilibrium Models for Ionizable Compounds That Differ in Tautomeric or Conformational Distribution and Affinity Is a Function of Log P Equations for Equilibrium Models for Which Affinity Depends on Steric or Electrostatic Properties in Addition to Log P Equations for Models That Include Equilibria and the Rates of Biological Processes Equations for Whole-Animal Tests for Which No Model Can Be Postulated Empirical Equations Statistical Basis of Regression and Partial Least-Squares Analysis Fundamental Concepts of Statistics Simple LinearRegression Multiple Linear Regression Nonlinear Regression Analysis PrincipalComponents Analysis (PCA) Partial Least-Squares Analysis Estimating the Predictivity a Model Strategy for the Statistical Evaluation of a Data Set of Related Molecules Preparing the Data Set for Analysis Finding the Important Multiple Linear Regression Equations for a Data Set Using Nonlinear Regression Analysis Fitting Partial Least-Squares Relationships Testing the Validity of a Computational Model Detailed Examples of QSAR Calculations on Erythromycin Esters Antibacterial Potencies versus Staphylococcus aureus Calculation of the Molecular Properties Statistical Analysis for Traditional QSAR Case Studies Inhibition of Dopamine I -Hydroxylase by 5-Substituted Picolinic Acid Analogs Rate of Hydrolysis of Amino Acid Amides of Dopamine Analgetic Potency of I-Carbolines Antibacterial Potency of Erythromycin Analogs D1 Dopamine Agonists Use of Ligand-Protein Structures for CoMFA 3D QSAR Methods to Approach Other Structure-Activity Problems Measuring the Similarity or Distances between Molecules Displaying Locations of Molecules in Multidimensional Space Analyzing Properties ThatDistinguish Classes of Molecules Lessons Learned and References appear at theend of each chapter.
- ISBN: 978-1-4200-7099-6
- Editorial: CRC Press
- Encuadernacion: Cartoné
- Páginas: 282
- Fecha Publicación: 01/10/2009
- Nº Volúmenes: 1
- Idioma: Inglés