{"product_id":"applied-chemoinformatics-achievements-and-future-opportunities-9783527342013","title":"Applied Chemoinformatics: Achievements and Future","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003eEdited by world-famous pioneers in chemoinformatics, this is a clearly structured and applications-oriented approach to the topic, providing up-to-date and focused information on the wide range of applications in this exciting field.\u003cbr\u003eThe authors explain methods and software tools, such that the reader will not only learn the basics but also how to use the different software packages available. Experts describe applications in such different fields as structure-spectra correlations, virtual screening, prediction of active sites, library design, the prediction of the properties of chemicals, the development of new cosmetics products, quality control in food, the design of new materials with improved properties, toxicity modeling, assessment of the risk of chemicals, and the control of chemical processes.\u003cbr\u003eThe book is aimed at advanced students as well as lectures but also at scientists that want to learn how chemoinformatics could assist them in solving their daily scientific tasks.\u003cbr\u003eTogether with the corresponding textbook Chemoinformatics - Basic Concepts and Methods (ISBN 9783527331093) on the fundamentals of chemoinformatics readers will have a comprehensive overview of the field.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cp\u003eForeword xvii\u003c\/p\u003e \u003cp\u003eList of Contributors xxi\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction 1\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eThomas Engel and Johann Gasteiger\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 The Rationale for the Books 1\u003c\/p\u003e \u003cp\u003e1.2 Development of the Field 2\u003c\/p\u003e \u003cp\u003e1.3 The Basis of Chemoinformatics and the Diversity of Applications 3\u003c\/p\u003e \u003cp\u003e1.3.1 Databases 3\u003c\/p\u003e \u003cp\u003e1.3.2 Fundamental Questions of a Chemist 4\u003c\/p\u003e \u003cp\u003e1.3.3 Drug Discovery 5\u003c\/p\u003e \u003cp\u003e1.3.4 Additional Fields of Application 6\u003c\/p\u003e \u003cp\u003eReference 7\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 QSAR\/QSPR 9\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eWolfgang Sippl and Dina Robaa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 9\u003c\/p\u003e \u003cp\u003e2.2 Data Handling and Curation 13\u003c\/p\u003e \u003cp\u003e2.2.1 Structural Data 13\u003c\/p\u003e \u003cp\u003e2.2.2 Biological Data 14\u003c\/p\u003e \u003cp\u003e2.3 Molecular Descriptors 14\u003c\/p\u003e \u003cp\u003e2.3.1 Structural Keys (1D) 15\u003c\/p\u003e \u003cp\u003e2.3.2 Topological Descriptors (2D) 16\u003c\/p\u003e \u003cp\u003e2.3.3 Geometric Descriptors (3D) 16\u003c\/p\u003e \u003cp\u003e2.4 Methods for Data Analysis 17\u003c\/p\u003e \u003cp\u003e2.4.1 Overview 17\u003c\/p\u003e \u003cp\u003e2.4.2 Unsupervised Learning 17\u003c\/p\u003e \u003cp\u003e2.4.3 Supervised Learning 18\u003c\/p\u003e \u003cp\u003e2.5 Classification Methods 19\u003c\/p\u003e \u003cp\u003e2.5.1 Principal Component Analysis 19\u003c\/p\u003e \u003cp\u003e2.5.2 Linear Discriminant Analysis 19\u003c\/p\u003e \u003cp\u003e2.5.3 Kohonen Neural Network 19\u003c\/p\u003e \u003cp\u003e2.5.4 Other Classification Methods 20\u003c\/p\u003e \u003cp\u003e2.6 Methods for Data Modeling 20\u003c\/p\u003e \u003cp\u003e2.6.1 Regression-Based QSAR Approaches 20\u003c\/p\u003e \u003cp\u003e2.6.2 3D QSAR 22\u003c\/p\u003e \u003cp\u003e2.6.3 Nonlinear Models 25\u003c\/p\u003e \u003cp\u003e2.7 Summary on Data Analysis Methods 30\u003c\/p\u003e \u003cp\u003e2.8 Model Validation 30\u003c\/p\u003e \u003cp\u003e2.8.1 Proper Use of Validation Routines 31\u003c\/p\u003e \u003cp\u003e2.8.2 Modeling\/Validation Workflow 32\u003c\/p\u003e \u003cp\u003e2.8.3 Splitting of Datasets 32\u003c\/p\u003e \u003cp\u003e2.8.4 Compilation of Modeling, Training, Validation, Test, and External Sets 34\u003c\/p\u003e \u003cp\u003e2.8.5 Cross-Validation 36\u003c\/p\u003e \u003cp\u003e2.8.6 Bootstrapping 37\u003c\/p\u003e \u003cp\u003e2.8.7 Y-Randomization (Y-Scrambling) 38\u003c\/p\u003e \u003cp\u003e2.8.8 Goodness of Prediction and Quality Criteria 39\u003c\/p\u003e \u003cp\u003e2.8.9 Applicability Domain and Model Acceptability Criteria 41\u003c\/p\u003e \u003cp\u003e2.8.10 Scope of External and Internal Validation 43\u003c\/p\u003e \u003cp\u003e2.8.11 Validation of Classification Models 45\u003c\/p\u003e \u003cp\u003e2.9 Regulatory Use of QSARs 46\u003c\/p\u003e \u003cp\u003eSelected Reading 48\u003c\/p\u003e \u003cp\u003eReferences 49\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Prediction of Physicochemical Properties of Compounds 53\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eIgor V. Tetko, Aixia Yan, and Johann Gasteiger\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 53\u003c\/p\u003e \u003cp\u003e3.2 Overview of Modeling Approaches to Predict Physicochemical Properties 54\u003c\/p\u003e \u003cp\u003e3.2.1 Prediction of Properties Based on Other Properties 55\u003c\/p\u003e \u003cp\u003e3.2.2 Prediction of Properties Based on Theoretical Calculations 55\u003c\/p\u003e \u003cp\u003e3.2.3 Additivity Schemes for Property Prediction 56\u003c\/p\u003e \u003cp\u003e3.2.4 Statistical Quantitative Structure–Property Relationships (QSPRs) 59\u003c\/p\u003e \u003cp\u003e3.3 Methods for the Prediction of Individual Properties 59\u003c\/p\u003e \u003cp\u003e3.3.1 Mean Molecular Polarizability 59\u003c\/p\u003e \u003cp\u003e3.3.2 Thermodynamic Properties 60\u003c\/p\u003e \u003cp\u003e3.3.3 Octanol\/Water Partition Coefficient (Log P) 63\u003c\/p\u003e \u003cp\u003e3.3.4 Octanol\/Water Distribution Coefficient (log D) 67\u003c\/p\u003e \u003cp\u003e3.3.5 Estimation of Water Solubility (log S) 69\u003c\/p\u003e \u003cp\u003e3.3.6 Melting Point (MP) 71\u003c\/p\u003e \u003cp\u003e3.3.7 Acid Ionization Constants 73\u003c\/p\u003e \u003cp\u003e3.4 Limitations of Statistical Methods 76\u003c\/p\u003e \u003cp\u003e3.5 Outlook and Perspectives 76\u003c\/p\u003e \u003cp\u003eSelected Reading 78\u003c\/p\u003e \u003cp\u003eReferences 78\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Chemical Reactions 83\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4.1 Chemical Reactions – An Introduction 84\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJohann Gasteiger\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003eReferences 85\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4.2 Reaction Prediction and Synthesis Design 86\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJonathan M. Goodman\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.2.1 Introduction 86\u003c\/p\u003e \u003cp\u003e4.2.2 Reaction Prediction 87\u003c\/p\u003e \u003cp\u003e4.2.3 Synthesis Design 94\u003c\/p\u003e \u003cp\u003e4.2.4 Conclusion 102\u003c\/p\u003e \u003cp\u003eReferences 103\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4.3 Explorations into Biochemical Pathways 106\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eOliver Sacher and Johann Gasteiger\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.3.1 Introduction 106\u003c\/p\u003e \u003cp\u003e4.3.2 The BioPath.Database 110\u003c\/p\u003e \u003cp\u003e4.3.3 BioPath.Explore 111\u003c\/p\u003e \u003cp\u003e4.3.4 Search Results 112\u003c\/p\u003e \u003cp\u003e4.3.5 Exploitation of the Information in BioPath.Database 117\u003c\/p\u003e \u003cp\u003e4.3.6 Summary 129\u003c\/p\u003e \u003cp\u003eSelected Reading 130\u003c\/p\u003e \u003cp\u003eReferences 130\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Structure–Spectrum Correlations and Computer-Assisted Structure Elucidation 133\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJoao Aires de Sousa\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 133\u003c\/p\u003e \u003cp\u003e5.2 Molecular Descriptors 135\u003c\/p\u003e \u003cp\u003e5.2.1 Fragment-Based Descriptors 135\u003c\/p\u003e \u003cp\u003e5.2.2 Topological Structure Codes 135\u003c\/p\u003e \u003cp\u003e5.2.3 Three-Dimensional Molecular Descriptors 137\u003c\/p\u003e \u003cp\u003e5.3 Infrared Spectra 137\u003c\/p\u003e \u003cp\u003e5.3.1 Overview 137\u003c\/p\u003e \u003cp\u003e5.3.2 Infrared Spectra Simulation 138\u003c\/p\u003e \u003cp\u003e5.4 NMR Spectra 140\u003c\/p\u003e \u003cp\u003e5.4.1 Quantum Chemistry Prediction of NMR Properties 142\u003c\/p\u003e \u003cp\u003e5.4.2 NMR Spectra Prediction by Database Searching 142\u003c\/p\u003e \u003cp\u003e5.4.3 NMR Spectra Prediction by Increment-Based Methods 143\u003c\/p\u003e \u003cp\u003e5.4.4 NMR Spectra Prediction by Machine Learning Methods 144\u003c\/p\u003e \u003cp\u003e5.5 Mass Spectra 150\u003c\/p\u003e \u003cp\u003e5.5.1 Identification of Structures and Interpretation of MS 150\u003c\/p\u003e \u003cp\u003e5.5.2 Prediction of MS 151\u003c\/p\u003e \u003cp\u003e5.5.3 Metabolomics and Natural Products 151\u003c\/p\u003e \u003cp\u003e5.6 Computer-Aided Structure Elucidation (CASE) 153\u003c\/p\u003e \u003cp\u003eSelected Reading 157\u003c\/p\u003e \u003cp\u003eAcknowledgement 157\u003c\/p\u003e \u003cp\u003eReferences 158\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6.1 Drug Discovery: An Overview 165\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eLothar Terfloth, Simon Spycher, and Johann Gasteiger\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1.1 Introduction 165\u003c\/p\u003e \u003cp\u003e6.1.2 Definitions of Some Terms Used in Drug Design 167\u003c\/p\u003e \u003cp\u003e6.1.3 The Drug Discovery Process 167\u003c\/p\u003e \u003cp\u003e6.1.4 Bio- and Chemoinformatics Tools for Drug Design 168\u003c\/p\u003e \u003cp\u003e6.1.5 Structure-based and Ligand-Based Drug Design 168\u003c\/p\u003e \u003cp\u003e6.1.6 Target Identification and Validation 169\u003c\/p\u003e \u003cp\u003e6.1.7 Lead Finding 171\u003c\/p\u003e \u003cp\u003e6.1.8 Lead Optimization 182\u003c\/p\u003e \u003cp\u003e6.1.9 Preclinical and Clinical Trials 188\u003c\/p\u003e \u003cp\u003e6.1.10 Outlook: Future Perspectives 189\u003c\/p\u003e \u003cp\u003eSelected Reading 191\u003c\/p\u003e \u003cp\u003eReferences 191\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6.2 Bridging Information on Drugs, Targets, and Diseases 195\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAndreas Steffen and Bertram Weiss\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.2.1 Introduction 195\u003c\/p\u003e \u003cp\u003e6.2.2 Existing Data Sources 196\u003c\/p\u003e \u003cp\u003e6.2.3 Drug Discovery Use Cases in Computational Life Sciences 196\u003c\/p\u003e \u003cp\u003e6.2.4 Discussion and Outlook 201\u003c\/p\u003e \u003cp\u003eSelected Reading 202\u003c\/p\u003e \u003cp\u003eReferences 202\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6.3 Chemoinformatics in Natural Product Research 207\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eTeresa Kaserer, Daniela Schuster, and Judith M. Rollinger\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.3.1 Introduction 207\u003c\/p\u003e \u003cp\u003e6.3.2 Potential and Challenges 208\u003c\/p\u003e \u003cp\u003e6.3.3 Access to Software and Data 211\u003c\/p\u003e \u003cp\u003e6.3.4 In Silico Driven Pharmacognosy-Hyphenated Strategies 219\u003c\/p\u003e \u003cp\u003e6.3.5 Opportunities 220\u003c\/p\u003e \u003cp\u003e6.3.6 Miscellaneous Applications 228\u003c\/p\u003e \u003cp\u003e6.3.7 Limits 228\u003c\/p\u003e \u003cp\u003e6.3.8 Conclusion and Outlook 229\u003c\/p\u003e \u003cp\u003eSelected Reading 231\u003c\/p\u003e \u003cp\u003eReferences 231\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6.4 Chemoinformatics of Chinese Herbal Medicines 237\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJun Xu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.4.1 Introduction 237\u003c\/p\u003e \u003cp\u003e6.4.2 Type 2 Diabetes: The Western Approach 237\u003c\/p\u003e \u003cp\u003e6.4.3 Type 2 Diabetes: The Chinese Herbal Medicines Approach 238\u003c\/p\u003e \u003cp\u003e6.4.4 Building a Bridge 238\u003c\/p\u003e \u003cp\u003e6.4.5 Screening Approach 240\u003c\/p\u003e \u003cp\u003eSelected Reading 244\u003c\/p\u003e \u003cp\u003eReferences 244\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6.5 PubChem 245\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eWolf-D. Ihlenfeldt\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.5.1 Introduction 245\u003c\/p\u003e \u003cp\u003e6.5.2 Objectives 246\u003c\/p\u003e \u003cp\u003e6.5.3 Architecture 246\u003c\/p\u003e \u003cp\u003e6.5.4 Data Sources 247\u003c\/p\u003e \u003cp\u003e6.5.5 Submission Processing and Structure Representation 248\u003c\/p\u003e \u003cp\u003e6.5.6 Data Augmentation 249\u003c\/p\u003e \u003cp\u003e6.5.7 Preparation for Database Storage 249\u003c\/p\u003e \u003cp\u003e6.5.8 Query Data Preparation and Structure Searching 250\u003c\/p\u003e \u003cp\u003e6.5.9 Structure Query Input 253\u003c\/p\u003e \u003cp\u003e6.5.10 Query Processing 254\u003c\/p\u003e \u003cp\u003e6.5.11 Getting Started with PubChem 254\u003c\/p\u003e \u003cp\u003e6.5.12 Web Services 255\u003c\/p\u003e \u003cp\u003e6.5.13 Conclusion 255\u003c\/p\u003e \u003cp\u003eReferences 256\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6.6 Pharmacophore Perception and Applications 259\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eThomas Seidel, Gerhard Wolber, and Manuela S. Murgueitio\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.6.1 Introduction 259\u003c\/p\u003e \u003cp\u003e6.6.2 Historical Development of the Modern Pharmacophore Concept 260\u003c\/p\u003e \u003cp\u003e6.6.3 Representation of Pharmacophores 262\u003c\/p\u003e \u003cp\u003e6.6.4 Pharmacophore Modeling 268\u003c\/p\u003e \u003cp\u003e6.6.5 Application of Pharmacophores in Drug Design 272\u003c\/p\u003e \u003cp\u003e6.6.6 Software for Computer-Aided Pharmacophore Modeling and Screening 278\u003c\/p\u003e \u003cp\u003e6.6.7 Summary 278\u003c\/p\u003e \u003cp\u003eSelected Reading 279\u003c\/p\u003e \u003cp\u003eReferences 280\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6.7 Prediction, Analysis, and Comparison of Active Sites 283\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAndrea Volkamer, Mathias M. von Behren, Stefan Bietz, and Matthias Rarey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.7.1 Introduction 283\u003c\/p\u003e \u003cp\u003e6.7.2 Active Site Prediction Algorithms 284\u003c\/p\u003e \u003cp\u003e6.7.3 Target Prioritization: Druggability Prediction 292\u003c\/p\u003e \u003cp\u003e6.7.4 Search for Sequentially Homologous Pockets 296\u003c\/p\u003e \u003cp\u003e6.7.5 Target Comparison: Virtual Active Site Screening 298\u003c\/p\u003e \u003cp\u003e6.7.6 Summary and Outlook 304\u003c\/p\u003e \u003cp\u003eSelected Reading 306\u003c\/p\u003e \u003cp\u003eReferences 306\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6.8 Structure-Based Virtual Screening 313\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAdrian Kolodzik, Nadine Schneider, and Matthias Rarey\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.8.1 Introduction 313\u003c\/p\u003e \u003cp\u003e6.8.2 Docking Algorithms 315\u003c\/p\u003e \u003cp\u003e6.8.3 Scoring 317\u003c\/p\u003e \u003cp\u003e6.8.4 Structure-Based Virtual Screening Workflow 321\u003c\/p\u003e \u003cp\u003e6.8.5 Protein-Based Pharmacophoric Filters 323\u003c\/p\u003e \u003cp\u003e6.8.6 Validation 323\u003c\/p\u003e \u003cp\u003e6.8.7 Summary and Outlook 326\u003c\/p\u003e \u003cp\u003eSelected Reading 328\u003c\/p\u003e \u003cp\u003eReferences 328\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6.9 Prediction of ADME Properties 333\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAixia Yan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.9.1 Introduction 333\u003c\/p\u003e \u003cp\u003e6.9.2 General Consideration on SPR\/QSPR Models 334\u003c\/p\u003e \u003cp\u003e6.9.3 Estimation of Aqueous Solubility (log S) 336\u003c\/p\u003e \u003cp\u003e6.9.4 Estimation of Blood–Brain Barrier Permeability (log BB) 342\u003c\/p\u003e \u003cp\u003e6.9.5 Estimation of Human Intestinal Absorption (HIA) 346\u003c\/p\u003e \u003cp\u003e6.9.6 Other ADME Properties 349\u003c\/p\u003e \u003cp\u003e6.9.7 Summary 354\u003c\/p\u003e \u003cp\u003eSelected Reading 355\u003c\/p\u003e \u003cp\u003eReferences 355\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6.10 Prediction of Xenobiotic Metabolism 359\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAnthony Long and Ernest Murray\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.10.1 Introduction: The Importance of Xenobiotic Biotransformation in the Life Sciences 359\u003c\/p\u003e \u003cp\u003e6.10.2 Biotransformation Types 362\u003c\/p\u003e \u003cp\u003e6.10.3 Brief Review of Methods 364\u003c\/p\u003e \u003cp\u003e6.10.4 User Needs: Scientists Use Metabolism Information in Different Ways 370\u003c\/p\u003e \u003cp\u003e6.10.5 Case Studies 372\u003c\/p\u003e \u003cp\u003eSelected Reading 382\u003c\/p\u003e \u003cp\u003eReferences 383\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6.11 Chemoinformatics at the CADD Group of the National Cancer Institute 385\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMegan L. Peach and Marc C. Nicklaus\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.11.1 Introduction and History 385\u003c\/p\u003e \u003cp\u003e6.11.2 Chemical Information Services 386\u003c\/p\u003e \u003cp\u003e6.11.3 Tools and Software 388\u003c\/p\u003e \u003cp\u003e6.11.4 Synthesis and Activity Predictions 391\u003c\/p\u003e \u003cp\u003e6.11.5 Downloadable Datasets 391\u003c\/p\u003e \u003cp\u003eReferences 392\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6.12 Uncommon Data Sources for QSAR Modeling 395\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAlexander Tropsha\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.12.1 Introduction 395\u003c\/p\u003e \u003cp\u003e6.12.2 Observational Metadata and QSAR Modeling 397\u003c\/p\u003e \u003cp\u003e6.12.3 Pharmacovigilance and QSAR 398\u003c\/p\u003e \u003cp\u003e6.12.4 Conclusions 401\u003c\/p\u003e \u003cp\u003eSelected Reading 402\u003c\/p\u003e \u003cp\u003eReferences 402\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6.13 Future Perspectives of Computational Drug Design 405\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eGisbert Schneider\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.13.1 Where Do the Medicines of the Future Come from? 405\u003c\/p\u003e \u003cp\u003e6.13.2 Integrating Design, Synthesis, and Testing 408\u003c\/p\u003e \u003cp\u003e6.13.3 Toward Precision Medicine 409\u003c\/p\u003e \u003cp\u003e6.13.4 Learning from Nature: From Complex Templates to Simple Designs 411\u003c\/p\u003e \u003cp\u003e6.13.5 Conclusions 413\u003c\/p\u003e \u003cp\u003eSelected Reading 414\u003c\/p\u003e \u003cp\u003eReferences 414\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Computational Approaches in Agricultural Research 417\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKlaus-Jürgen Schleifer\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 417\u003c\/p\u003e \u003cp\u003e7.2 Research Strategies 418\u003c\/p\u003e \u003cp\u003e7.2.1 Ligand-Based Approaches 419\u003c\/p\u003e \u003cp\u003e7.2.2 Structure-Based Approaches 422\u003c\/p\u003e \u003cp\u003e7.3 Estimation of Adverse Effects 429\u003c\/p\u003e \u003cp\u003e7.3.1 In Silico Toxicology 429\u003c\/p\u003e \u003cp\u003e7.3.2 Programs and Databases 430\u003c\/p\u003e \u003cp\u003e7.3.3 In Silico Toxicology Models 432\u003c\/p\u003e \u003cp\u003e7.4 Conclusion 435\u003c\/p\u003e \u003cp\u003eSelected Reading 436\u003c\/p\u003e \u003cp\u003eReferences 436\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Chemoinformatics in Modern Regulatory Science 439\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eChihae Yang, James F. Rathman, Aleksey Tarkhov, Oliver Sacher, Thomas Kleinoeder, Jie Liu, Thomas Magdziarz, Aleksandra Mostraq, Joerg Marusczyk, Darshan Mehta, Christof Schwab, and Bruno Bienfait\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 439\u003c\/p\u003e \u003cp\u003e8.1.1 Science and Technology Progress 439\u003c\/p\u003e \u003cp\u003e8.1.2 Regulatory Science in Twenty-First Century 440\u003c\/p\u003e \u003cp\u003e8.2 Data Gap Filling Methods in Risk Assessment 441\u003c\/p\u003e \u003cp\u003e8.2.1 QSAR and Structural Knowledge 442\u003c\/p\u003e \u003cp\u003e8.2.2 Threshold of Toxicological Concern (TTC) 443\u003c\/p\u003e \u003cp\u003e8.2.3 Read-Across (RA) 445\u003c\/p\u003e \u003cp\u003e8.3 Database and Knowledge Base 448\u003c\/p\u003e \u003cp\u003e8.3.1 Architecture of Structure-Searchable Toxicity Database 448\u003c\/p\u003e \u003cp\u003e8.3.2 Data Model for Chemistry-Centered Toxicity Database 449\u003c\/p\u003e \u003cp\u003e8.3.3 Inventories 452\u003c\/p\u003e \u003cp\u003e8.4 New Approach Descriptors 453\u003c\/p\u003e \u003cp\u003e8.4.1 ToxPrint Chemotypes 453\u003c\/p\u003e \u003cp\u003e8.4.2 Liver BioPath Chemotypes 458\u003c\/p\u003e \u003cp\u003e8.4.3 Dynamic Generation of Annotated Linear Paths 459\u003c\/p\u003e \u003cp\u003e8.4.4 Other Examples of Descriptors 461\u003c\/p\u003e \u003cp\u003e8.5 Chemical Space Analysis 462\u003c\/p\u003e \u003cp\u003e8.5.1 Principal Component Analysis 462\u003c\/p\u003e \u003cp\u003e8.6 Summary 464\u003c\/p\u003e \u003cp\u003eSelected Reading 466\u003c\/p\u003e \u003cp\u003eReferences 466\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Chemometrics in Analytical Chemistry 471\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAnita Rácz, Dávid Bajusz, and Károly Héberger\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 471\u003c\/p\u003e \u003cp\u003e9.2 Sources of Data: Data Preprocessing 472\u003c\/p\u003e \u003cp\u003e9.3 Data Analysis Methods 475\u003c\/p\u003e \u003cp\u003e9.3.1 Qualitative Methods 475\u003c\/p\u003e \u003cp\u003e9.3.2 Quantitative Methods 483\u003c\/p\u003e \u003cp\u003e9.4 Validation 488\u003c\/p\u003e \u003cp\u003e9.5 Applications 492\u003c\/p\u003e \u003cp\u003e9.6 Outlook and Prospects 492\u003c\/p\u003e \u003cp\u003eSelected Reading 496\u003c\/p\u003e \u003cp\u003eReferences 496\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Chemoinformatics in Food Science 501\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAndrea Peña-Castillo, Oscar Méndez-Lucio, John R. Owen, Karina Martínez-Mayorga, and José L. Medina-Franco\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 501\u003c\/p\u003e \u003cp\u003e10.2 Scope of Chemoinformatics in Food Chemistry 502\u003c\/p\u003e \u003cp\u003e10.3 Molecular Databases of Food Chemicals 503\u003c\/p\u003e \u003cp\u003e10.4 Chemical Space of Food Chemicals 506\u003c\/p\u003e \u003cp\u003e10.4.1 General Considerations 506\u003c\/p\u003e \u003cp\u003e10.4.2 Chemical Space Analysis of Food Chemical Databases 508\u003c\/p\u003e \u003cp\u003e10.5 Structure–Property Relationships 510\u003c\/p\u003e \u003cp\u003e10.5.1 Structure–Flavor Relationships and Flavor Cliffs 511\u003c\/p\u003e \u003cp\u003e10.5.2 Quantitative Structure–Odor Relationships 512\u003c\/p\u003e \u003cp\u003e10.6 Computational Screening and Data Mining of Food Chemicals Libraries 513\u003c\/p\u003e \u003cp\u003e10.6.1 Anticonvulsant Effect of Sweeteners and Pharmaceutical and Food Preservatives 514\u003c\/p\u003e \u003cp\u003e10.6.2 Mining Food Chemicals as Potential Epigenetic Modulators 516\u003c\/p\u003e \u003cp\u003e10.7 Conclusion 521\u003c\/p\u003e \u003cp\u003eSelected Reading 522\u003c\/p\u003e \u003cp\u003eReferences 523\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 Computational Approaches to Cosmetics Products Discovery 527\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSoheila Anzali, Frank Pflücker, Lilia Heider, and Alfred Jonczyk\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction: Cosmetics Demands on Computational Approaches 527\u003c\/p\u003e \u003cp\u003e11.2 Case I: The Multifunctional Role of Ectoine as a Natural Cell Protectant (Product: Ectoine, \"Cell Protection Factor\", and Moisturizer) 528\u003c\/p\u003e \u003cp\u003e11.2.1 Molecular Dynamics (MD) Simulations 530\u003c\/p\u003e \u003cp\u003e11.2.2 Results and Discussion: Ectoine Retains the Power of Water 531\u003c\/p\u003e \u003cp\u003e11.3 Case II: A Smart Cyclopeptide Mimics the RGD Containing Cell Adhesion Proteins at the Right Site (Product: Cyclopeptide-5: Antiaging) 533\u003c\/p\u003e \u003cp\u003e11.3.1 Methods 536\u003c\/p\u003e \u003cp\u003e11.3.2 Results and Discussion 536\u003c\/p\u003e \u003cp\u003e11.4 Conclusions: Cases I and II 542\u003c\/p\u003e \u003cp\u003eReferences 545\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Applications in Materials Science 547\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eTu C. Le, and David A. Winkler\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction 547\u003c\/p\u003e \u003cp\u003e12.2 Why Materials Are Harder to Model than Molecules 548\u003c\/p\u003e \u003cp\u003e12.3 Why Are Chemoinformatics Methods Important Now? 548\u003c\/p\u003e \u003cp\u003e12.4 How Do You Describe Materials Mathematically? 549\u003c\/p\u003e \u003cp\u003e12.5 How Well do Chemoinformatics Methods Work on Materials? 551\u003c\/p\u003e \u003cp\u003e12.6 What Are the Pitfalls when Modeling Materials? 551\u003c\/p\u003e \u003cp\u003e12.7 How Do You Make Good Models and Avoid the Pitfalls? 553\u003c\/p\u003e \u003cp\u003e12.8 Materials Examples 554\u003c\/p\u003e \u003cp\u003e12.8.1 Inorganic Materials and Nanomaterials 554\u003c\/p\u003e \u003cp\u003e12.8.2 Polymers 557\u003c\/p\u003e \u003cp\u003e12.8.3 Catalysts 558\u003c\/p\u003e \u003cp\u003e12.8.4 Metal–Organic Frameworks (MOFs) 560\u003c\/p\u003e \u003cp\u003e12.9 Biomaterials Examples 561\u003c\/p\u003e \u003cp\u003e12.9.1 Bioactive Polymers 561\u003c\/p\u003e \u003cp\u003e12.9.2 Microarrays 564\u003c\/p\u003e \u003cp\u003e12.10 Perspectives 566\u003c\/p\u003e \u003cp\u003eSelected Reading 567\u003c\/p\u003e \u003cp\u003eReferences 567\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Process Control and Soft Sensors 571\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eKimito Funatsu\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 571\u003c\/p\u003e \u003cp\u003e13.2 Roles of Soft Sensors 573\u003c\/p\u003e \u003cp\u003e13.3 Problems with Soft Sensors 574\u003c\/p\u003e \u003cp\u003e13.4 Adaptive Soft Sensors 576\u003c\/p\u003e \u003cp\u003e13.5 Database Monitoring for Soft Sensors 578\u003c\/p\u003e \u003cp\u003e13.6 Efficient Process Control Using Soft Sensors 581\u003c\/p\u003e \u003cp\u003e13.7 Conclusions 582\u003c\/p\u003e \u003cp\u003eSelected Readings 583\u003c\/p\u003e \u003cp\u003eReferences 583\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Future Directions 585\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJohann Gasteiger\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Well-Established Fields of Application 585\u003c\/p\u003e \u003cp\u003e14.2 Emerging Fields of Application 586\u003c\/p\u003e \u003cp\u003e14.3 Renaissance of Some Fields 587\u003c\/p\u003e \u003cp\u003e14.4 Combined Use of Chemoinformatics Methods 588\u003c\/p\u003e \u003cp\u003e14.5 Impact on Chemical Research 589\u003c\/p\u003e \u003cp\u003eIndex 591\u003c\/p\u003e","brand":"Wiley-VCH Verlag GmbH","offers":[{"title":"Default Title","offer_id":51021171720535,"sku":"9783527342013","price":96.86,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9783527342013.jpg?v=1750785357","url":"https:\/\/bookcurl.com\/products\/applied-chemoinformatics-achievements-and-future-opportunities-9783527342013","provider":"Book Curl","version":"1.0","type":"link"}