Princeton University Library Catalog

Quantitative drug design : a critical introduction / Yvonne Connolly Martin.

Martin, Yvonne Connolly, 1936- [Browse]
Boca Raton : CRC Press/Taylor & Francis, c2010.
2nd ed.
xv, 282 p. : ill. ; 25 cm.
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.
  • 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 -- References -- 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 -- References -- 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 -- IX. Lessons Learned -- Appendix 4.1. Rules for Encoding a Structure into SMILES -- References -- 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 -- References -- 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 -- References -- 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 -- VIII. Lessons Learned -- References -- 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 -- References -- 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 -- IV. Lessons Learned -- References -- 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 -- A. Early QSARs -- B. Reexamination of the QSAR -- C. Lessons Learned from the Analysis of Amino Acid Pro-Drugs of Dopamine -- III. Analgetic Potency of y-Carbolines -- A. Early QSARs -- B. Reexamination of the QSAR -- 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 -- References -- 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 -- V. Lessons Learned -- References.
  • 9781420070996 (hardcover : alk. paper)
  • 1420070991 (hardcover : alk. paper)
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