Details

Subject(s)

• Drugs—Structure-activity relationships [Browse]
• QSAR (Biochemistry) [Browse]
• Drugs—Design [Browse]

Summary note

"Incorporating the novel developments that have occurred in this field since the publication of the first edition, Quantitative Drug Design: A Critical Introduction, Second Edition shows scientists how to apply quantitative structure-activity relationship (QSAR) techniques at a state-of-the-art level. It presents computational methods that analyze the relationships between molecular structure and biological activity, emphasizing techniques that provide a quantitative forecast of biological potency." "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 discusses the physical chemistry of ligand-macromolecule interactions and the computer programs that translate a molecule's potential to participate in these interactions into interpretable quantitative descriptors. The book also covers the fundamentals of multivariate statistics and validation procedures needed to support a valid structure-activity model."--BOOK JACKET.

Bibliographic references

Includes bibliographical references and index.

Contents

• Chapter 1. Overview of Quantitative Drug Design
• I. Stages of Drug Discovery
• A. Discovery of the Initial Lead
• B. Development of Structure-Activity Relationships
• C. Refinement of the Structure-Activity Hypothesis
• II. Computer Descriptions of Changes in Structure Related to Changes in Properties of Molecules
• A. Substructure-Based QSARs
• B. Traditional Physical-Property-Based QSARs
• C. QSARs Based on 3D Properties of Molecules
• III. Lessons Learned
• References
• Chapter 2. Noncovalent Interactions in Biological Systems
• I. Factors that Influence the Strength of an Interaction
• II. The Importance of Water
• III. Electrostatic Interactions
• IV. Hydrogen Bonds
• V. Dispersion Interactions
• VI. Charge-Transfer Interactions
• VII. Hydrophobic Interactions
• VIII. Steric Repulsion
• IX. Lessons Learned
• Chapter 3. Preparation of 3D Structures of Molecules for 3D QSAR
• I. Preliminary Inspection of Molecules
• II. Generating 3D Structures of Molecules
• A. Sources of Starting 3D Structures
• B. Methods to Minimize the Energy of a 3D Molecular Structure
• 1. Molecular Mechanics
• 2. Quantum Chemistry
• C. Methods to Search Conformations
• 1. Templates
• 2. Rule-Based Generation of Conformers
• 3. Rigid Rotation
• 4. Monte Carlo Searching
• 5. Molecular Dynamics
• 6. Simulated Annealing
• 7. Distance Geometry
• III. Strategies to Select the Conformation for 3D QSAR
• A. 3D Structures of the Ligand-Biomolecular Complex Are Available
• B. 3D Structures of Some of the Bound Ligands Are Available
• C. One or More Conformationally Constrained Potent Ligands Is Known
• D. Ligands Are Members of a Series
• E. No 3D Structures of Bound Ligands nor Rigid Ligands Are Available
• IV. Lessons Learned
• Chapter 4. Calculating Physical Properties of Molecules
• I. The Electronic Properties of Molecules
• A. Electronic Properties Calculated from the Structure Diagram
• 1. Σ Values for Substituents on Aromatic Systems
• 2. Separate Field and Resonance Substituent Constants
• 3. Σ Values for Substituents on Aliphatic Systems
• 4. Calculations of Partial Atomic Charges, q
• B. Electronic Properties Calculated from the 3D Structure of the Molecules
• C. pKa Values and Calculation of Fraction Ionized and Nonionized
• 1. Sources of pKa Values
• 2. Calculation of the Fraction Ionized
• II. The Hydrogen-Bonding Properties of Molecules
• A. Counts of Hydrogen Bond Donors and Acceptors
• B. Polar Surface Area
• C. Hydrogen-Bonding Quantitation Based on 2D Structures
• D. Hydrogen-Bonding Quantitation Based on 3D Structures
• III. The Size of Substituents and Shape of Molecules
• A. Calculating Steric Effects from 2D Molecular Structures of Related Molecules
• 1. Molar Refractivity
• 2. Es Values
• 3. Sterimol Substituent Values
• 4. Steric Properties as a Component of Composite Properties
• B. Calculating Steric Effects from 3D Molecular Structures
• IV. The Hydrophobic Properties of Molecules
• A. Experimental Measures of Log D and Log P
• B. Tables of Measured Log P Values
• C. Calculation of Octanol-Water Log P from Molecular Structure
• 1. Monosubstituted Benzene Derivatives: Definition of π
• 2. Extension of Octanol-Water Log P Calculations to Aliphatic and Complex Systems
• 3. Atom-Based Calculations of Log P
• 4. Incorporating 3D Properties into Calculation of Log P
• 5. Nonadditivity of Substituent Effects on Log P
• E. Calculation of 3D Hydrophobic Fields
• F. Computer Programs that Calculate Octanol-Water Log P
• V. Indicator or Substructure Variables
• A. Indicator Variables Combined with Physical Properties
• B. Substructural Descriptors as the Basis of a QSAR
• VI. Composite Descriptors Calculated from 2D Structures
• VII. Properties Calculated from the 3D Structure of the Ligand-Macromolecule Complex
• VIII. Organizing Molecular Properties for 3D QSAR
• A. Calculations Based on Molecular Superpositions
• B. Calculations Based on Spatial Relationships between the Atoms of the 3-Dimensional Structure
• Appendix 4.1. Rules for Encoding a Structure into SMILES
• Chapter 5. Biological Data
• I. Consequences of Ligand-Biomolecule Interaction
• II. Selection of Data for Analysis: Characteristics of an Ideal Biological Test
• A. Based on a Dose-Response Curve
• B. Based on the Time Course of Response of in vivo Studies
• C. Relevant Properties Considered
• D. Precision Required versus Potency Range
• E. Definitions of Some Pharmacological Terms
• III. Calculation of Relative Potency
• A. General Considerations
• B. Theoretical Description of an Idealized Dose-Response Curve
• C. Transformations of the Dose-Response Curve
• D. Relative Potency within a Series: Defined ED50 or LD50
• E. Relative Potency within a Series: Response at a Constant Dose
• F. Relative Potency within a Series: Response at One Variable Dose, and Slope of Dose-Response Curve Known
• IV. Choice of Classification Boundaries
• V. Lessons Learned
• Chapter 6. Form of Equations that Relate Potency and Physical Properties
• I. Introduction to Model-Based Equations
• II. Equation for an Equilibrium Model for Ionizable Compounds for Which Affinity Is a Function of Log P and Only the Neutral Form Binds
• III. Equations for Equilibrium Models for Ionizable Compounds that Differ in Tautomeric or Conformational Distribution and Affinity Is a Function of Log P
• A. One Aqueous, One Receptor, and One Inert Nonaqueous Compartment, Variable pKa within the Series
• B. One Aqueous, One Receptor, and One Inert Nonaqueous Compartment, Variable Tautomeric or Conformational Equilibria within the Series
• C. One Receptor Compartment and Multiple Nonaqueous and Aqueous Compartments of Different pH
• IV. Equations for Equilibrium Models for which Affinity Depends on Steric or Electrostatic Properties in Addition to Log P
• A. Affinity for the Target Biomolecule Is a Function of Hydrophobic Interactions with Only Certain Positions of the Molecules
• B. Potency Is a Function of Sigma or Es
• C. Affinity for the Target Biomolecule Is a Function of Sigma or Es
• D. Partitioning to the Inert Nonaqueous Compartment Is a Function of Sigma or Es
• E. Simplification of the Models if One or More Terms Is Not Significant
• V. Equations for Models that Include Equilibria and the Rates of Biological Processes
• VI. Equations for Whole-Animal Tests for which No Model Can Be Postulated
• VII. Empirical Equations
• VIII. Lessons Learned
• Chapter 7. Statistical Basis of Regression and Partial Least-Squares Analysis
• I. Fundamental Concepts of Statistics
• A. Definitions
• B. Probability, Estimation, and Hypothesis Testing
• C. Analysis of Variance (ANOVA)
• II. Simple Linear Regression
• A. Assumptions
• B. Least-Squares Line Calculation
• C. Significance of the Observed Relationship: F and R2
• D. Characteristics of s, the Standard Error of Estimate
• E. Correlation and Regression
• III. Multiple Linear Regression (MLR or OLS)
• IV. Nonlinear Regression Analysis (NLR)
• V. Principal Components Analysis (PCA)
• VI. Partial Least-Squares Analysis (PLS)
• VII. Estimating the Predictivity of a Model
• A. The Role of Statistics
• B. Methods that Test a Model Using External Data
• 1. Separate Training and Test Sets
• 2. Cross-Validation
• 3. Y-Scrambling
• Chapter 8. Strategy for the Statistical Evaluation of a Data Set of Related Molecules
• I. Preparing the Data Set for Analysis
• II. Finding the Important Multiple Linear Regression Equations for a Data Set --
• A. The Problem of Multiple Possible Equations
• B. The All-Equation Approach
• C. Stepwise Regression
• D. Follow-Up to Stepwise Regression
• E. Other Algorithms for Variable Selection
• F. Improving a Regression Equation
• G. Indicator Variables
• III. Using Nonlinear Regression Analysis
• A. Comparison of Linear and Nonlinear Regression
• B. Some Special Problems with Nonlinear Regression Analysis
• IV. Fitting Partial Least-Squares Relationships
• V. Testing the Validity of a Computational Model
• VI. Lessons Learned
• Chapter 9. Detailed Examples of QSAR Calculations on Erythromycin Esters
• I. Antibacterial Potencies versus Staphylococcus aureus
• II. Calculation of the Molecular Properties
• A. Partition Coefficients
• B. Other 2D Properties for Traditional QSAR
• III. Statistical Analysis for Traditional QSAR
• A. Simple Two-Variable Relationships
• B. Statistics for the All-Variable Equation
• C. Identification of the Best Equations
• D. Interpretation of the QSAR Results
• E. Follow-Up of the Equation
• Chapter 10. Case Studies
• I. Inhibition of Dopamine β-Hydroxylase by 5-Substituted Picolinic Acid Analogs
• A. Early QSARs
• B. Reexamination of the QSAR
• C. Lessons Learned from the Analysis of Fusaric Acid Analogs
• II. The Rate of Hydrolysis of Amino Acid Amides of Dopamine
• C. Lessons Learned from the Analysis of Amino Acid Pro-Drugs of Dopamine
• III. Analgetic Potency of y-Carbolines
• C. Lessons Learned from the Analysis of the Analgetic Properties of y-Carbolines
• IV. Antibacterial Potency of Erythromycin Analogs
• A. Early QSARs (1969)
• 1. Effect of Hydrophobicity on the Gram-Positive Antibacterial Activity of Erythromycin Esters
• 2. Effect of Hydrophobicity on the Antibacterial Activity of Leucomycin Esters
• 3. Effect of Hydrophobicity on the Antibacterial Activity of Alkyl Analogs of Lincomycin
• 4. The Optimum Partition Coefficient for Erythromycin Esters
• 5. The Effect of Variation of the pKa on the Potency of N-Benzyl Erythromycin Analogs
• B. Further Studies on Alkyl Esters (1971)
• C. Potency of New Analogs against Staphylococcus aureus (1972-1973)
• D. Potency of All Analogs against Other Bacteria (1971-1973)
• E. Overview of Conclusions from 2D QSARs (1973) of Erythromycin Analogs
• F. Fits to Model-Based Equations (1975)
• 1. Lincomycin Analogs
• 2. N-Benzyl Erythromycin Analogs
• G. Free-Wilson Analysis to Complement Property-Based QSAR
• H. CoMFA 3D QSAR Analysis to Complement Property-Based QSAR
• I. Lessons Learned from the Analysis of Structure-Activity Relationships of Erythromycin Analogs
• V. D1 Dopamine Agonists
• A. Molecular Modeling of α2 Adrenergic Ligands
• B. Molecular Modeling of D2 Dopamine Agonists
• C. Exploration of the Bioactive Conformation of Phenyl-Substituted Catechol Amines as D1 Dopamine Agonists
• D. CoMFA 3D QSAR Predictions of D1 Potency of Novel Catechol Amine Scaffolds
• E. Lessons Learned from Molecular Modeling of Dopaminergic and Adrenergic Ligands
• VI. Use of Ligand-Protein Structures for CoMFA 3D QSAR
• A. CoMFA Analyses to Compare with Literature Structure-Based QSARs (1997)
• B. CoMFA Analysis of Urokinase Inhibitors (2007)
• C. Lessons Learned from Structure-Based 3D QSARs
• VII. Lessons Learned
• Chapter 11. Methods to Approach Other Structure-Activity Problems
• I. Measuring the Similarity or Distances between Molecules
• A. Distance Calculations Based on Molecules Described by a Continuous Properties
• B. Distance Calculations Based on Molecules Described by a Vector of 1's and 0's
• II. Displaying Locations of Molecules in Multidimensional Space
• III. Grouping Similar Molecules Together
• A. Hierarchical Clustering
• B. Partitional Clustering
• IV. Analyzing Properties that Distinguish Classes of Molecules
• A. General Introduction
• B. Examples of Classification Methods
• 1. Linear Discriminant Analysis (LDA)
• 2. SIMCA
• 3. Support Vector Machine Classifiers (SVMs)
• 4. K-Nearest Neighbor Prediction (KNN)
• 5. Recursive Partitioning or Decision Trees
• C. Observations from Analyses of Monoamine Oxidase Inhibition Data Set
• References.

Show 220 more Contents items

ISBN

• 9781420070996 (hardcover : alk. paper)
• 1420070991 (hardcover : alk. paper)

LCCN

2009028151

OCLC

166872519

RCP

C - S

Statement on responsible collection description

Princeton University Library aims to describe library materials in a manner that is respectful to the individuals and communities who create, use, and are represented in the collections we manage. Read more...