Neural Networks in QSAR and Drug Design
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Table of contents
- Contentsv
- Contributorsix
- Prefacexi
- Chapter 1. Strengths and Weaknesses of the Backpropagation Neural Network in QSAR and QSPR Studies1
- Abstract1
- Introduction1
- Standard BNN Algorithm3
- Designing the Model6
- Selection of the Best BNN Model15
- Comparison of the Performances of a BNN Model with those Obtained with other Approaches18
- Software Availability20
- Hybrid Systems with BNN20
- Conclusion23
- Annex: Artificial Neural Networks (ANNs) on Internet24
- References24
- Chapter 2. AUTOLOGP Versus Neural Network Estimation of n-Octanol/Water Partition Coefficients47
- Abstract47
- Introduction47
- Materials and Methods49
- Results and Discussion53
- Concluding Remarks57
- References58
- Chapter 3. Use of a Backpropagation Neural Network and Autocorrelation Descriptors for Predicting th65
- Abstract65
- Introduction65
- Biodegradation Data66
- Molecular Descriptors76
- Statistics78
- Modeling Results79
- References81
- Chapter 4. Structure–Bell-Pepper Odor Relationships for Pyrazines and Pyridines Using Neural Netwo83
- Abstract83
- Introduction84
- Materials and Methods85
- Results and Discussion90
- Conclusion92
- References93
- Chapter 5. A Neural Structure–Odor Threshold Model for Chemicals of Environmental and Industrial C97
- Abstract97
- Introduction98
- Materials and Methods99
- Results and Discussion109
- Concluding Remarks114
- References115
- Chapter 6. Adaptive Resonance Theory Based Neural Networks Explored for Pattern Recognition Analysis119
- Abstract119
- Introduction120
- Neuro-Physiological Basis of ART121
- Taxonomy and State-of-the-Art122
- Theory of ART-2a and FuzzyART124
- Data Preprocessing by Complement Coding128
- Quantification or Qualification?129
- Case Study I: Classification of Rose Varieties from their Headspace Analysis130
- Case Study II: Optimal Selection of Aliphatic Substituents138
- Conclusions147
- References147
- Chapter 7. Multivariate Data Display Using Neural Networks157
- Abstract157
- Introduction157
- Methods159
- Results and Discussion167
- Conclusions173
- References174
- Chapter 8. Quantitative Structure–Activity Relationships of Nicotinic Agonists177
- Abstract177
- Introduction178
- Methods181
- Results188
- Discussion196
- References204
- Chapter 9. Evaluation of Molecular Surface Properties Using a Kohonen Neural Network209
- Abstract209
- Introduction209
- Materials and Methods210
- Kohonen Network211
- Template Approach212
- From a 3D-Space to a 2D-Map213
- Clustering of the Structures by an Investigation of their Maps215
- In Search of the Bioactive Conformation, the Best Superposition, SAR217
- Conclusions220
- References221
- Chapter 10. A New Nonlinear Neural Mapping Technique for Visual Exploration of QSAR Data223
- Abstract223
- Introduction223
- Background225
- Case Study I: Analysis of Sensor Data231
- Case Study II: Optimal Test Series Design237
- Concluding Remarks246
- References246
- Chapter 11. Combining Fuzzy Clustering and Neural Networks to Predict Protein Structural Classes255
- Abstract255
- Introduction256
- Methodology257
- Results and Discussion266
- Caveats and Conclusions275
- References277
- Index281
- Color Plate SectionColor Plate-1
Comprehensive and impeccably edited, Neural Networks in QSAR and Drug Design is the first book to present an all-inclusive coverage of the topic. The book provides a practice-oriented introduction to the different neural network paradigms, allowing the reader to easily understand and reproduce the results demonstrated. Numerous examples are detailed, demonstrating a variety of applications to QSAR and drug design.
The contributors include some of the most distinguished names in the field, and the book provides an exhaustive bibliography, guiding readers to all the literature related to a particular type of application or neural network paradigm. The extensive index acts as a guide to the book, and makes retrieving information from chapters an easy task. A further research aid is a list of software with indications of availablility and price, as well as the editors scale rating the ease of use and interest/price ratio of each software package. The presentation of new, powerful tools for modeling molecular properties and the inclusion of many important neural network paradigms, coupled with extensive reference aids, makes Neural Networks in QSAR and Drug Design an essential reference source for those on the frontiers of this field.
Key Features
* Presents the first coverage of neural networks in QSAR and Drug Design
* Allows easy understanding and reproduction of the results described within
* Includes an exhaustive bibliography with more than 200 references
* Provides a list of applicable software packages with availability and price
The contributors include some of the most distinguished names in the field, and the book provides an exhaustive bibliography, guiding readers to all the literature related to a particular type of application or neural network paradigm. The extensive index acts as a guide to the book, and makes retrieving information from chapters an easy task. A further research aid is a list of software with indications of availablility and price, as well as the editors scale rating the ease of use and interest/price ratio of each software package. The presentation of new, powerful tools for modeling molecular properties and the inclusion of many important neural network paradigms, coupled with extensive reference aids, makes Neural Networks in QSAR and Drug Design an essential reference source for those on the frontiers of this field.
Key Features
* Presents the first coverage of neural networks in QSAR and Drug Design
* Allows easy understanding and reproduction of the results described within
* Includes an exhaustive bibliography with more than 200 references
* Provides a list of applicable software packages with availability and price