Quantitative Drug Design: A Critical Introduction, Second Edition, 2nd Edition (Hardback) book cover

Quantitative Drug Design

A Critical Introduction, Second Edition, 2nd Edition

By Yvonne C. Martin

CRC Press

292 pages | 156 B/W Illus.

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Hardback: 9781420070996
pub: 2010-05-06
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Since the publication of the first edition, the field has changed dramatically. Scientists can now explicitly consider 3D features in quantitative structure-activity relationship (QSAR) studies and often have the 3D structure of the macromolecular target to guide the 3D QSAR. Improvements in computer hardware and software have also made the methods more accessible to scientists. Taking these developments into account, Quantitative Drug Design: A Critical Introduction, Second Edition shows scientists how to apply QSAR techniques at a state-of-the-art level.

New to the Second Edition

  • A new chapter on methods that identify the 3D conformations to use for 3D QSAR
  • New discussions on partial least squares, multidimensional scaling, clustering, support vector machines, kNN potency prediction, and recursive partitioning
  • Expanded case studies that include the results of data that has been re-analyzed using newer methods
  • A new case study on the discovery of novel dopaminergics with pharmacophore mapping and CoMFA
  • A new case study on the application of CoMFA to series in which the 3D structure of the ligand-protein complex is known

Based on the author’s four decades of experience in all areas of ligand-based computer-assisted drug design, this invaluable book describes how to transform ligand structure-activity relationships into models that predict the potency or activity/inactivity of new molecules. It will help you avoid traps when dealing with quantitative drug design.

Table of Contents

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 Variables

Composite 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 Linear Regression

Multiple Linear Regression

Nonlinear Regression Analysis

Principal Components 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 β-Hydroxylase by 5-Substituted Picolinic Acid Analogs

Rate of Hydrolysis of Amino Acid Amides of Dopamine

Analgetic Potency of γ-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 That Distinguish Classes of Molecules

Lessons Learned and References appear at the end of each chapter.

About the Author

For more than forty years, Yvonne Connelly Martin worked in drug research at Abbott Laboratories. A long-time leader in computer-assisted drug design, she was a recipient of the Herman Skolnik award of the American Chemical Society in 2009 and the accomplishment award of the Society for Biomolecular Sciences in 2005. Dr. Martin is currently a member of the editorial advisory board and Perspectives editor of the Journal of Computer-Aided Molecular Design.

Subject Categories

BISAC Subject Codes/Headings:
MEDICAL / Pharmacy
SCIENCE / Chemistry / Physical & Theoretical