Reviews in Computational Chemistry, Volume 16

This volume, like those prior to it, features chapters by experts in various fields of computational chemistry. Volume 23 covers linear scaling methods for quantum chemistry variational transition state theory, coarse grain modeling of polymers, support vector machines, conical intersections, analysis of information content using shannon entropy and historical insights into how computing evolved in the pharmaceutical industry. Book jacket.

Kenny B. Lipkowitz, PhD, is a retired Professor of Chemistry from North Dakota State University.

Donald B. Boyd was apponted Research Professor of Chemistry at Indiana University - Purdue University Indianapolis in 1994. He has published over 100 refereed journal papers and book chapters.

Computer-Aided Molecular Diversity Analysis and Combinatorial Library Design

1

(53)

Richard A. Lewis

Stephen D. Pickett

David E. Clark

Introduction

1

(2)

Molecular Recognition: Similarity and Diversity

3

(4)

Describing Diversity Space

7

(13)

Types of Descriptor

8

(7)

Choosing Appropriate Descriptors

15

(1)

Validation of Descriptors

16

(4)

Applications

20

(13)

Diversity Analysis

20

(9)

Combinatorial Library Design

29

(4)

Diversity Is Not the Be-All and End-All!

33

(2)

Current Issues and Future Directions

35

(4)

Diversity Descriptors

35

(2)

Library Design

37

(1)

Speed Requirement

38

(1)

``Quick and Dirty'' QSAR

38

(1)

Integration with Other Modeling Tools

38

(1)

Persuading the Customers

39

(1)

Conclusions

39

(1)

Acknowledgments

39

(1)

References

39

(14)

Artificial Neural Networks and Their Use in Chemistry

53

(88)

Keith L. Peterson

Introduction

53

(3)

Overview and Goals

54

(2)

What Are Artificial Neural Networks?

56

(8)

Analogy with the Brain

56

(1)

Artificial Neural Networks

57

(1)

Summary of Neural Network Operation

58

(5)

Brief History of Neural Networks

63

(1)

What Can Neural Networks Be Used for and When Should You Use Them?

64

(10)

Classification

64

(6)

Modeling

70

(1)

Mapping and Associations

71

(2)

General Comments on ANNs, Statistics, and Artificial Intelligence

73

(1)

Processing Elements

74

(11)

Summation Functions

76

(1)

Transfer Functions

77

(2)

Output Functions

79

(1)

Error Functions

80

(1)

Learning Rules

80

(5)

Collections of Processing Elements

85

(3)

Different Types of Artificial Neural Network

88

(13)

Adaptive Resonance Theory (ART) Networks

88

(1)

Backpropagation (BP) and Related Networks

89

(4)

Biassociative Memory (BAM) Networks

93

(1)

Counterpropagation Networks

94

(2)

Generalized Regression Networks (GRN)

96

(1)

Hopfield Networks

97

(1)

Kohonen Self-Organizing Map (SOM) Networks

97

(1)

Perceptron Networks

98

(1)

Radial Basis Function (RBF) Networks

99

(1)

Recirculation Networks

100

(1)

Miscellaneous Networks

100

(1)

Practical Considerations in Solving Problems with Neural Networks

101

(12)

What Type of Network?

101

(1)

Data Preprocessing

102

(4)

Variable Selection, Reduction, and Orthogonalization

106

(1)

Training and Testing Sets

107

(1)

Training the Network

108

(3)

Learning Versus Generalization

111

(2)

Performance Metrics

113

(7)

Classification Problems

114

(4)

Nonclassification, Supervised Learning Problems

118

(2)

Miscellaneous Remarks

120

(1)

Analysis of Neural Networks

120

(4)

Concluding Remarks

124

(2)

Appendix: Neural Network Software

126

(2)

References

128

(13)

Use of Force Fields in Materials Modeling

141

(76)

Jorg-Rudiger Hill

Clive M. Freeman

Lalitha Subramanian

Introduction

141

(3)

The Force Field Approach to Describing Structures of Materials

144

(16)

What Are Force Fields?

144

(1)

Ion Pair and Shell Model Potentials

145

(9)

Molecular Mechanics Force Fields

154

(4)

Comparison of Ion Pair and Molecular Mechanics Force Fields

158

(2)

Force Field Parameterization

160

(15)

Ab Initio Based Force Fields

161

(7)

Empirical Force Fields

168

(2)

Transferability

170

(4)

Rule-Based Force Fields

174

(1)

Application of Force Fields in Materials Science

175

(26)

Metal Oxides and Ceramics

175

(2)

Superconductors

177

(3)

Zeolites and Related Microporous Materials

180

(15)

Glasses

195

(6)

Polymers

201

(1)

Conclusions

201

(1)

Acknowledgments

202

(1)

References

202

(15)

Free Energy Calculations: Use and Limitations in Predicting Ligand Binding Affinities

217

(88)

M. Rami Reddy

Mark D. Erion

Atul Agarwal

Introduction

217

(1)

Theory

218

(2)

Methodology Overview

219

(1)

Computational Details

220

(5)

Molecular Mechanics Force Fields

220

(1)

Treatment of Long-Range Forces

221

(1)

Polarization

222

(1)

Bond Length Constraints

222

(1)

Treatment of Boundaries

223

(1)

Solvent Models

223

(1)

Convergence of Free Energy Results

224

(1)

Free Energy Perturbation Calculations for Small Molecules

225

(13)

Tautomerization

226

(6)

Ionization

232

(1)

Log P

232

(2)

Covalent Hydration

234

(2)

Solvation

236

(2)

Free Energy Perturbation Calculations for Macromolecules

238

(35)

Nonprotein--Ligand Complexes

239

(9)

Protease Inhibitors

248

(9)

Lyases

257

(3)

Oxidases and Reductases

260

(3)

Allosteric Binding Site Ligands

263

(2)

DNA Binding Proteins

265

(2)

Miscellaneous Studies

267

(6)

Guide to Structure-Based Ligand Optimization

273

(4)

Computer Model

273

(2)

Characterization of the Binding Site

275

(1)

Lead Generation

276

(1)

Optimization of Lead Compounds

276

(1)

Optimization of Ligands to HIV-1 Protease: Using the FEP Method

277

(8)

Design Considerations

277

(2)

X-Ray Structures of HIV-1 Protease Complexes

279

(1)

Force Field Parameters

279

(1)

Computational Details for Solvent

279

(1)

Computational Details for Complex

280

(1)

Computer Model Validation

280

(1)

Validation of FEP Methodology

281

(2)

Convergence and Error Analysis

283

(1)

Binding Affinity Predictions

283

(1)

Summary

284

(1)

Conclusions

285

(1)

Advantages of Free Energy Calculations

286

(1)

Limitations of Free Energy Calculations

286

(1)

Brief Guide for Free Energy Calculations and Their Use in Ligand Optimization

286

(2)

Acknowledgments

288

(1)

References

288

(17)

Author Index

305

(22)

Subject Index

327

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Reviews in Computational Chemistry, Volume 16

Summary

Author Biography

Table of Contents

Supplemental Materials

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Rent More, Save More! Use code: BBRENTAL

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Reviews in Computational Chemistry, Volume 16

Summary

Author Biography

Table of Contents

Supplemental Materials

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