Book Cover J. Zupan, J. Gasteiger
Neural Networks in Chemistry and Drug Design: An Introduction

Table of Contents

Part I Basic Concepts
1 Defining the Area
1.1 Learning from Information
1.2 General Objectives and Concepts
1.3 What Neural Networks are Good for
1.4 Notation, Conventions and Abbreviations
1.5 Beyond a Printed Edition
2 Neuron
2.1 Synapses and Input Signals
2.2 Weights
2.3 Linear Learning Machine
2.4 Transfer Functions in Neurons
2.5 Bias
2.6 Graphical Representation of Artificial Neurons
2.7 Essentials
2.8 References and Suggested Readings
3 Linking Neurons into Networks
3.1 General
3.2 One Layer
3.3 Input
3.4 Architectures
3.5 Hidden Layer; Output Layer
3.6 Graphical Representation of Neural Networks
3.7 Essentials
3.8 References and Suggested Readings
Part II One-Layer Networks
4 Hopfield Network
4.1 General
4.2 Architecture
4.3 Transfer Function
4.4 Weight Matrix
4.5 Iteration
4.6 Capacity of the Hopfield Network
4.7 Essentials
4.8 References and Suggested Readings
5 Adaptive Bidirectional Associative Memory (ABAM)
5.1 Unsupervised and Supervised Learning
5.2 General
5.3 ABAM Network
5.4 Learning Procedure
5.5 An Example
5.6 Significance of the Example
5.7 Essentials
5.8 References and Suggested Readings
6 Kohonen Network
6.1 General
6.2 Architecture
6.3 Competitive Learning
6.4 Mapping from Three to Two Dimensions
6.5 Another Example
6.6 Remarks
6.7 Essentials
6.8 References and Suggested Readings
Part III Multilayer Networks
7 Counter-Propagation
7.1 Transition from One to Two Layers
7.2 Lookup Table
7.3 Architecture
7.4 Supervised Competitive Learning
7.5 Learning to Play Tennis
7.6 Correlations Among the Variables
7.7 Essentials
7.8 References and Suggested Readings
8 Back-Propagation of Errors
8.1 General
8.2 Architecture
8.3 Learning by Back-Propagation
8.4 The Generalized Delta-Rule
8.5 Learning Algorithm
8.6 Example: Tennis Match
8.7 Essentials
8.8 References and Suggested Readings
Part IV Applications
9 General Comments on Chemical Applications
9.1 Introduction
9.2 Classification
9.3 Modeling
9.4 Mapping
9.5 Associations; Moving Window
9.6 Overview of the Examples in Chapters 10 to 20
9.7 Essentials
9.8 References and Suggested Readings
10 Clustering of Multi-Component Analytical Data for Olive Oils
10.1 The Problem
10.2 The Data
10.3 Preliminary Exploration of Possible Networks
10.4 Learning to Make Predictions
10.5 Concluding Remarks
10.6 References and Suggested Readings
11 The Reactivity of Chemical Bonds and the Classification of Chemical Reactions
11.1 The Problem and the Data
11.2 Architecture of the Network for Back-Propagation Learning
11.3 Using an Experimental Design Technique to Select the Training Set
11.4 Application of the Kohonen Learning
11.5 Application of the Trained Multilayer Network
11.6 Chemical Significance of the Kohonen Map
11.7 Classification of Reactions: The Data
11.8 Classification of Reactions: Results
11.9 References and Suggested Readings
12 HPLC Optimization of Wine Analysis
12.1 The Problem of Modeling
12.2 Modeling the Mobile Phase for HPLC by a Standard Method
12.3 Modeling the Mobile Phase for HPLC by a Neural Network
12.4 Comparison of Networks with Identical Architectures
12.5 References and Suggested Readings
13 Quantitative Structure-Activity Relationships
13.1 The Problem
13.2 Dataset I
13.3 Architecture and Learning Procedure
13.4 Prospects of the Method
13.5 Dataset II
13.6 Structure Representation by Autocorrelation of the Molecular Electrostatic Potential
13.7 Verification of Structure Representation by Unsupervised Learning
13.8 Modeling of Biological Activity by Supervised Learning
13.9 Data Set III
13.10 Structure Representation by Spectrum-Like Uniform Representation
13.11 Selection of the Most Important Variables Using a Genetic Algorithm
13.12 Cross-validation of the Counter-Propagation Model Obtained by the Optimal Reduced Representation
13.13 References and Suggested Readings
14 The Electrophilic Aromatic Substitution Reaction
14.1 The Problem
14.2 The Data
14.3 The Network
14.4 Learning and Results
14.5 A Third Representation of Data
14.6 Concluding Remarks
14.7 References and Suggested Readings
15 Modeling and Optimizing a Recipe for a Paint Coating
15.1 The Problem
15.2 The Data
15.3 The Network and Training
15.4 The Models
15.5 References and Suggested Readings
16 Fault Detection and Process Control
16.1 The Problems
16.2 The Data
16.3 The Methods
16.4 Predictions of Faults
16.5 Modeling and Controlling a Continuously Stirred Tank Reactor (CSTR)
16.6 References and Suggested Readings
17 Secondary Structure of Proteins
17.1 The Problem
17.2 Representation of Amino Acids as Input Data
17.3 Architecture of the Network
17.4 Learning and Prediction
17.5 References and Suggested Readings
18 Infrared Spectrum-Structure Correlation
18.1 The Problem
18.2 The Representation of Infrared Spectra as Intensities
18.3 The Dataset, and Learning by Back-Propagation
18.4 Adjustable Representation of an Infrared Spectrum
18.5 Representing Spectra using Truncated Sets of Fourier or Hadamard Coefficients
18.6 Results of Kohonen Learning
18.7 A Molecular Transform of the 3D Structure
18.8 Learning by Counter-Propagation
18.9 Different Strategies for the Selection of a Training Set
18.10 From the Infrared Spectrum to the 3D Structure
18.11 References and Suggested Readings
19 Properties of Molecular Surfaces
19.1 The Problems
19.2 The Network Architecture and Training
19.3 Tiling with Kohonen Maps; Conformational Effects
19.4 Investigation of Receptors of Biological Neural Networks
19.5 Comparison of Kohonen Maps
19.6 Bioisosteric Design
19.7 Molecular Shape Analysis
19.8 References and Suggested Readings
20 Libraries of Chemical Compounds
20.1 The Problems
20.2 Strucure Coding
20.3 Separation of Benzodiazepine and Dopamine Agonists
20.4 Finding Active Compounds in a Large Set of Inactive Compounds
20.5 Diversity and Similarity of Combinatorial Libraries
20.6 Deconvolution of Xanthene Sublibraries
20.7 References and Suggested Readings
21 Representation of Chemical Structures
21.1 The Problem
21.2 Coding the Constitution
21.3 Coding the 3D Structure I
21.4 Coding the 3D Structure II
21.5 Coding Molecular Surfaces
21.6 A Hierarchy of Representations
21.7 References and Suggested Readings
22 Prospects of Neural Networks for Chemical Applications
Appendices
* Programs
* Data Sets
* Presentation Material
* Publications
* Tutorials