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Chemoinformatics for Drug Discovery.
Chemoinformatics strategies to improve drug discovery results With contributions from leading researchers in academia and the pharmaceutical industry as well as experts from the software industry, this book explains how chemoinformatics enhances drug discovery and pharmaceutical research efforts, de...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken :
Wiley,
2013.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Chemoinformatics for Drug Discovery; Copyright; Contents; Preface; Contributors; 1 What Are Our Models Really Telling Us? A Practical Tutorial on Avoiding Common Mistakes When Building Predictive Models; 1.1 Introduction; 1.2 Preliminaries; 1.3 Datasets; 1.3.1 Exploring Datasets; 1.4 Building Predictive Models; 1.5 Evaluating the Performance of Predictive Models; 1.5.1 Pearson's r; 1.5.2 Kendall's Tau; 1.5.3 Root-Mean-Square Deviation (RMSD); 1.6 Molecular Descriptors; 1.7 Building and Testing a Random Forest Model; 1.8 Experimental Error and Model Performance; 1.9 Model Applicability.
- 1.10 Comparing Predictive Models1.11 Conclusion; References; Source Code Listings; 2 The Challenge of Creativity in Drug Design; 2.1 Drug Design History: Incrementalism and Serendipity; 2.2 Physical Reality and Computational Methods; 2.2.1 Protein Structure-Based Methods; 2.2.2 Molecular Similarity; 2.2.3 3D QSAR: Physically Realistic Activity Prediction; 2.3 Summary; References; 3 A Rough Set Theory Approach to the Analysis of Gene Expression Profiles; 3.1 Introduction; 3.2 Methodology; 3.2.1 Basic Theory; 3.2.2 Measures of Classification Accuracy and Quality; 3.2.3 An Illustrative Example.
- 3.2.4 Essential and Superfluous Information3.2.5 Rule Generation; 3.3 Drug-Induced Gene Expression and Phospholipidosis in Human Hepatoma HepG2 Cells; 3.3.1 Dataset; 3.3.2 Determination of D-Reducts; 3.3.3 Generation of Preliminary Rules; 3.3.4 Rule Simplification-Reduction of Attribute Values; 3.4 Discussion; 3.5 Summary and Conclusions; Notes; References; 4 Bimodal Partial Least-Squares Approach and Its Application to Chemogenomics Studies for Molecular Design; 4.1 Introduction; 4.2 Material and Methods; 4.2.1 Aminergic GPCR Inhibitory Activity Data.
- 4.2.2 Ligand and Protein Descriptors for LPLS Analysis4.2.3 L-Shaped PLS; 4.2.4 Atom Colorings Derived from Regression Coefficient Matrix; 4.3 Results and Discussion; 4.3.1 LPLS Analysis; 4.3.2 Atom Colorings and Support by Molecular Modeling; 4.4 Conclusion; 4.5 Acknowledgments; References; 5 Stability in Molecular Fingerprint Comparison; 5.1 Introduction; 5.2 Methods; 5.2.1 2D Methods; 5.2.2 Generation of Molecular Isosteres: WABE; 5.2.3 Tanimoto and Significance; 5.3 Results; 5.4 Conclusions and Directions; References; 6 Critical Assessment of Virtual Screening for Hit Identification.
- 6.1 Introduction6.2 Factors Affecting the Outcome and Evaluation of Virtual Screening Campaigns; 6.2.1 General Scientific Factors; 6.2.2 Characteristics of Practical Applications; 6.3 How to Evaluate Virtual Screening Performance?; 6.4 Virtual Versus High-Throughput Screening; 6.4.1 Do We Need Virtual Screening?; 6.4.2 Underutilized Strengths; 6.5 Structural Novelty Revisited: Exemplary Cases; 6.6 Expectations and Selected Applications; 6.6.1 Inhibitors of Multifunctional Proteins: Cytohesins; 6.6.2 First-in-Class Inhibitor for Ecto-52 Nucleotidase.