Applied Chemoinformatics: Achievements and Future Opportunities

Edited 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. The 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. The 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. Together 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.

Table of Contents:
Foreword xvii List of Contributors xxi 1 Introduction 1 Thomas Engel and Johann Gasteiger 1.1 The Rationale for the Books 1 1.2 Development of the Field 2 1.3 The Basis of Chemoinformatics and the Diversity of Applications 3 1.3.1 Databases 3 1.3.2 Fundamental Questions of a Chemist 4 1.3.3 Drug Discovery 5 1.3.4 Additional Fields of Application 6 Reference 7 2 QSAR/QSPR 9 Wolfgang Sippl and Dina Robaa 2.1 Introduction 9 2.2 Data Handling and Curation 13 2.2.1 Structural Data 13 2.2.2 Biological Data 14 2.3 Molecular Descriptors 14 2.3.1 Structural Keys (1D) 15 2.3.2 Topological Descriptors (2D) 16 2.3.3 Geometric Descriptors (3D) 16 2.4 Methods for Data Analysis 17 2.4.1 Overview 17 2.4.2 Unsupervised Learning 17 2.4.3 Supervised Learning 18 2.5 Classification Methods 19 2.5.1 Principal Component Analysis 19 2.5.2 Linear Discriminant Analysis 19 2.5.3 Kohonen Neural Network 19 2.5.4 Other Classification Methods 20 2.6 Methods for Data Modeling 20 2.6.1 Regression-Based QSAR Approaches 20 2.6.2 3D QSAR 22 2.6.3 Nonlinear Models 25 2.7 Summary on Data Analysis Methods 30 2.8 Model Validation 30 2.8.1 Proper Use of Validation Routines 31 2.8.2 Modeling/Validation Workflow 32 2.8.3 Splitting of Datasets 32 2.8.4 Compilation of Modeling, Training, Validation, Test, and External Sets 34 2.8.5 Cross-Validation 36 2.8.6 Bootstrapping 37 2.8.7 Y-Randomization (Y-Scrambling) 38 2.8.8 Goodness of Prediction and Quality Criteria 39 2.8.9 Applicability Domain and Model Acceptability Criteria 41 2.8.10 Scope of External and Internal Validation 43 2.8.11 Validation of Classification Models 45 2.9 Regulatory Use of QSARs 46 Selected Reading 48 References 49 3 Prediction of Physicochemical Properties of Compounds 53 Igor V. Tetko, Aixia Yan, and Johann Gasteiger 3.1 Introduction 53 3.2 Overview of Modeling Approaches to Predict Physicochemical Properties 54 3.2.1 Prediction of Properties Based on Other Properties 55 3.2.2 Prediction of Properties Based on Theoretical Calculations 55 3.2.3 Additivity Schemes for Property Prediction 56 3.2.4 Statistical Quantitative Structure–Property Relationships (QSPRs) 59 3.3 Methods for the Prediction of Individual Properties 59 3.3.1 Mean Molecular Polarizability 59 3.3.2 Thermodynamic Properties 60 3.3.3 Octanol/Water Partition Coefficient (Log P) 63 3.3.4 Octanol/Water Distribution Coefficient (log D) 67 3.3.5 Estimation of Water Solubility (log S) 69 3.3.6 Melting Point (MP) 71 3.3.7 Acid Ionization Constants 73 3.4 Limitations of Statistical Methods 76 3.5 Outlook and Perspectives 76 Selected Reading 78 References 78 4 Chemical Reactions 83 4.1 Chemical Reactions – An Introduction 84 Johann Gasteiger References 85 4.2 Reaction Prediction and Synthesis Design 86 Jonathan M. Goodman 4.2.1 Introduction 86 4.2.2 Reaction Prediction 87 4.2.3 Synthesis Design 94 4.2.4 Conclusion 102 References 103 4.3 Explorations into Biochemical Pathways 106 Oliver Sacher and Johann Gasteiger 4.3.1 Introduction 106 4.3.2 The BioPath.Database 110 4.3.3 BioPath.Explore 111 4.3.4 Search Results 112 4.3.5 Exploitation of the Information in BioPath.Database 117 4.3.6 Summary 129 Selected Reading 130 References 130 5 Structure–Spectrum Correlations and Computer-Assisted Structure Elucidation 133 Joao Aires de Sousa 5.1 Introduction 133 5.2 Molecular Descriptors 135 5.2.1 Fragment-Based Descriptors 135 5.2.2 Topological Structure Codes 135 5.2.3 Three-Dimensional Molecular Descriptors 137 5.3 Infrared Spectra 137 5.3.1 Overview 137 5.3.2 Infrared Spectra Simulation 138 5.4 NMR Spectra 140 5.4.1 Quantum Chemistry Prediction of NMR Properties 142 5.4.2 NMR Spectra Prediction by Database Searching 142 5.4.3 NMR Spectra Prediction by Increment-Based Methods 143 5.4.4 NMR Spectra Prediction by Machine Learning Methods 144 5.5 Mass Spectra 150 5.5.1 Identification of Structures and Interpretation of MS 150 5.5.2 Prediction of MS 151 5.5.3 Metabolomics and Natural Products 151 5.6 Computer-Aided Structure Elucidation (CASE) 153 Selected Reading 157 Acknowledgement 157 References 158 6.1 Drug Discovery: An Overview 165 Lothar Terfloth, Simon Spycher, and Johann Gasteiger 6.1.1 Introduction 165 6.1.2 Definitions of Some Terms Used in Drug Design 167 6.1.3 The Drug Discovery Process 167 6.1.4 Bio- and Chemoinformatics Tools for Drug Design 168 6.1.5 Structure-based and Ligand-Based Drug Design 168 6.1.6 Target Identification and Validation 169 6.1.7 Lead Finding 171 6.1.8 Lead Optimization 182 6.1.9 Preclinical and Clinical Trials 188 6.1.10 Outlook: Future Perspectives 189 Selected Reading 191 References 191 6.2 Bridging Information on Drugs, Targets, and Diseases 195 Andreas Steffen and Bertram Weiss 6.2.1 Introduction 195 6.2.2 Existing Data Sources 196 6.2.3 Drug Discovery Use Cases in Computational Life Sciences 196 6.2.4 Discussion and Outlook 201 Selected Reading 202 References 202 6.3 Chemoinformatics in Natural Product Research 207 Teresa Kaserer, Daniela Schuster, and Judith M. Rollinger 6.3.1 Introduction 207 6.3.2 Potential and Challenges 208 6.3.3 Access to Software and Data 211 6.3.4 In Silico Driven Pharmacognosy-Hyphenated Strategies 219 6.3.5 Opportunities 220 6.3.6 Miscellaneous Applications 228 6.3.7 Limits 228 6.3.8 Conclusion and Outlook 229 Selected Reading 231 References 231 6.4 Chemoinformatics of Chinese Herbal Medicines 237 Jun Xu 6.4.1 Introduction 237 6.4.2 Type 2 Diabetes: The Western Approach 237 6.4.3 Type 2 Diabetes: The Chinese Herbal Medicines Approach 238 6.4.4 Building a Bridge 238 6.4.5 Screening Approach 240 Selected Reading 244 References 244 6.5 PubChem 245 Wolf-D. Ihlenfeldt 6.5.1 Introduction 245 6.5.2 Objectives 246 6.5.3 Architecture 246 6.5.4 Data Sources 247 6.5.5 Submission Processing and Structure Representation 248 6.5.6 Data Augmentation 249 6.5.7 Preparation for Database Storage 249 6.5.8 Query Data Preparation and Structure Searching 250 6.5.9 Structure Query Input 253 6.5.10 Query Processing 254 6.5.11 Getting Started with PubChem 254 6.5.12 Web Services 255 6.5.13 Conclusion 255 References 256 6.6 Pharmacophore Perception and Applications 259 Thomas Seidel, Gerhard Wolber, and Manuela S. Murgueitio 6.6.1 Introduction 259 6.6.2 Historical Development of the Modern Pharmacophore Concept 260 6.6.3 Representation of Pharmacophores 262 6.6.4 Pharmacophore Modeling 268 6.6.5 Application of Pharmacophores in Drug Design 272 6.6.6 Software for Computer-Aided Pharmacophore Modeling and Screening 278 6.6.7 Summary 278 Selected Reading 279 References 280 6.7 Prediction, Analysis, and Comparison of Active Sites 283 Andrea Volkamer, Mathias M. von Behren, Stefan Bietz, and Matthias Rarey 6.7.1 Introduction 283 6.7.2 Active Site Prediction Algorithms 284 6.7.3 Target Prioritization: Druggability Prediction 292 6.7.4 Search for Sequentially Homologous Pockets 296 6.7.5 Target Comparison: Virtual Active Site Screening 298 6.7.6 Summary and Outlook 304 Selected Reading 306 References 306 6.8 Structure-Based Virtual Screening 313 Adrian Kolodzik, Nadine Schneider, and Matthias Rarey 6.8.1 Introduction 313 6.8.2 Docking Algorithms 315 6.8.3 Scoring 317 6.8.4 Structure-Based Virtual Screening Workflow 321 6.8.5 Protein-Based Pharmacophoric Filters 323 6.8.6 Validation 323 6.8.7 Summary and Outlook 326 Selected Reading 328 References 328 6.9 Prediction of ADME Properties 333 Aixia Yan 6.9.1 Introduction 333 6.9.2 General Consideration on SPR/QSPR Models 334 6.9.3 Estimation of Aqueous Solubility (log S) 336 6.9.4 Estimation of Blood–Brain Barrier Permeability (log BB) 342 6.9.5 Estimation of Human Intestinal Absorption (HIA) 346 6.9.6 Other ADME Properties 349 6.9.7 Summary 354 Selected Reading 355 References 355 6.10 Prediction of Xenobiotic Metabolism 359 Anthony Long and Ernest Murray 6.10.1 Introduction: The Importance of Xenobiotic Biotransformation in the Life Sciences 359 6.10.2 Biotransformation Types 362 6.10.3 Brief Review of Methods 364 6.10.4 User Needs: Scientists Use Metabolism Information in Different Ways 370 6.10.5 Case Studies 372 Selected Reading 382 References 383 6.11 Chemoinformatics at the CADD Group of the National Cancer Institute 385 Megan L. Peach and Marc C. Nicklaus 6.11.1 Introduction and History 385 6.11.2 Chemical Information Services 386 6.11.3 Tools and Software 388 6.11.4 Synthesis and Activity Predictions 391 6.11.5 Downloadable Datasets 391 References 392 6.12 Uncommon Data Sources for QSAR Modeling 395 Alexander Tropsha 6.12.1 Introduction 395 6.12.2 Observational Metadata and QSAR Modeling 397 6.12.3 Pharmacovigilance and QSAR 398 6.12.4 Conclusions 401 Selected Reading 402 References 402 6.13 Future Perspectives of Computational Drug Design 405 Gisbert Schneider 6.13.1 Where Do the Medicines of the Future Come from? 405 6.13.2 Integrating Design, Synthesis, and Testing 408 6.13.3 Toward Precision Medicine 409 6.13.4 Learning from Nature: From Complex Templates to Simple Designs 411 6.13.5 Conclusions 413 Selected Reading 414 References 414 7 Computational Approaches in Agricultural Research 417 Klaus-Jürgen Schleifer 7.1 Introduction 417 7.2 Research Strategies 418 7.2.1 Ligand-Based Approaches 419 7.2.2 Structure-Based Approaches 422 7.3 Estimation of Adverse Effects 429 7.3.1 In Silico Toxicology 429 7.3.2 Programs and Databases 430 7.3.3 In Silico Toxicology Models 432 7.4 Conclusion 435 Selected Reading 436 References 436 8 Chemoinformatics in Modern Regulatory Science 439 Chihae 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 8.1 Introduction 439 8.1.1 Science and Technology Progress 439 8.1.2 Regulatory Science in Twenty-First Century 440 8.2 Data Gap Filling Methods in Risk Assessment 441 8.2.1 QSAR and Structural Knowledge 442 8.2.2 Threshold of Toxicological Concern (TTC) 443 8.2.3 Read-Across (RA) 445 8.3 Database and Knowledge Base 448 8.3.1 Architecture of Structure-Searchable Toxicity Database 448 8.3.2 Data Model for Chemistry-Centered Toxicity Database 449 8.3.3 Inventories 452 8.4 New Approach Descriptors 453 8.4.1 ToxPrint Chemotypes 453 8.4.2 Liver BioPath Chemotypes 458 8.4.3 Dynamic Generation of Annotated Linear Paths 459 8.4.4 Other Examples of Descriptors 461 8.5 Chemical Space Analysis 462 8.5.1 Principal Component Analysis 462 8.6 Summary 464 Selected Reading 466 References 466 9 Chemometrics in Analytical Chemistry 471 Anita Rácz, Dávid Bajusz, and Károly Héberger 9.1 Introduction 471 9.2 Sources of Data: Data Preprocessing 472 9.3 Data Analysis Methods 475 9.3.1 Qualitative Methods 475 9.3.2 Quantitative Methods 483 9.4 Validation 488 9.5 Applications 492 9.6 Outlook and Prospects 492 Selected Reading 496 References 496 10 Chemoinformatics in Food Science 501 Andrea Peña-Castillo, Oscar Méndez-Lucio, John R. Owen, Karina Martínez-Mayorga, and José L. Medina-Franco 10.1 Introduction 501 10.2 Scope of Chemoinformatics in Food Chemistry 502 10.3 Molecular Databases of Food Chemicals 503 10.4 Chemical Space of Food Chemicals 506 10.4.1 General Considerations 506 10.4.2 Chemical Space Analysis of Food Chemical Databases 508 10.5 Structure–Property Relationships 510 10.5.1 Structure–Flavor Relationships and Flavor Cliffs 511 10.5.2 Quantitative Structure–Odor Relationships 512 10.6 Computational Screening and Data Mining of Food Chemicals Libraries 513 10.6.1 Anticonvulsant Effect of Sweeteners and Pharmaceutical and Food Preservatives 514 10.6.2 Mining Food Chemicals as Potential Epigenetic Modulators 516 10.7 Conclusion 521 Selected Reading 522 References 523 11 Computational Approaches to Cosmetics Products Discovery 527 Soheila Anzali, Frank Pflücker, Lilia Heider, and Alfred Jonczyk 11.1 Introduction: Cosmetics Demands on Computational Approaches 527 11.2 Case I: The Multifunctional Role of Ectoine as a Natural Cell Protectant (Product: Ectoine, "Cell Protection Factor", and Moisturizer) 528 11.2.1 Molecular Dynamics (MD) Simulations 530 11.2.2 Results and Discussion: Ectoine Retains the Power of Water 531 11.3 Case II: A Smart Cyclopeptide Mimics the RGD Containing Cell Adhesion Proteins at the Right Site (Product: Cyclopeptide-5: Antiaging) 533 11.3.1 Methods 536 11.3.2 Results and Discussion 536 11.4 Conclusions: Cases I and II 542 References 545 12 Applications in Materials Science 547 Tu C. Le, and David A. Winkler 12.1 Introduction 547 12.2 Why Materials Are Harder to Model than Molecules 548 12.3 Why Are Chemoinformatics Methods Important Now? 548 12.4 How Do You Describe Materials Mathematically? 549 12.5 How Well do Chemoinformatics Methods Work on Materials? 551 12.6 What Are the Pitfalls when Modeling Materials? 551 12.7 How Do You Make Good Models and Avoid the Pitfalls? 553 12.8 Materials Examples 554 12.8.1 Inorganic Materials and Nanomaterials 554 12.8.2 Polymers 557 12.8.3 Catalysts 558 12.8.4 Metal–Organic Frameworks (MOFs) 560 12.9 Biomaterials Examples 561 12.9.1 Bioactive Polymers 561 12.9.2 Microarrays 564 12.10 Perspectives 566 Selected Reading 567 References 567 13 Process Control and Soft Sensors 571 Kimito Funatsu 13.1 Introduction 571 13.2 Roles of Soft Sensors 573 13.3 Problems with Soft Sensors 574 13.4 Adaptive Soft Sensors 576 13.5 Database Monitoring for Soft Sensors 578 13.6 Efficient Process Control Using Soft Sensors 581 13.7 Conclusions 582 Selected Readings 583 References 583 14 Future Directions 585 Johann Gasteiger 14.1 Well-Established Fields of Application 585 14.2 Emerging Fields of Application 586 14.3 Renaissance of Some Fields 587 14.4 Combined Use of Chemoinformatics Methods 588 14.5 Impact on Chemical Research 589 Index 591