Applied Chemoinformatics

Johann Gasteiger is Professor emeritus of Chemistry at the University of Erlangen-Nuremberg, Germany and the co-founder of "Computer-Chemie-Centrum". He has received numerous awards and is a member of several societies and editorial boards. His research interests are in the development of software for drug design, simulation of chemical reactions, organic synthesis design, simulation of spectra, and chemical information processing by neural networks and genetic algorithms. Thomas Engel is is coordinator at the Department of Chemistry and Biochemistry of the Ludwig-Maximilians-Universität in Munich, Germany. He received his academic degrees at the University of Würzburg. Since 2001 he is lecturer at various universities promoting and establishing courses in scientific computing. He is also a member of the Chemistry-Information-Computer section (CIC) of the GDCh and the Molecular Graphics and Modeling Society (German section).

• Foreword xviiList of Contributors xxi1 Introduction 1Thomas Engel and Johann Gasteiger1.1 The Rationale for the Books 11.2 Development of the Field 21.3 The Basis of Chemoinformatics and the Diversity of Applications 31.3.1 Databases 31.3.2 Fundamental Questions of a Chemist 41.3.3 Drug Discovery 51.3.4 Additional Fields of Application 6Reference 72 QSAR/QSPR 9Wolfgang Sippl and Dina Robaa2.1 Introduction 92.2 Data Handling and Curation 132.2.1 Structural Data 132.2.2 Biological Data 142.3 Molecular Descriptors 142.3.1 Structural Keys (1D) 152.3.2 Topological Descriptors (2D) 162.3.3 Geometric Descriptors (3D) 162.4 Methods for Data Analysis 172.4.1 Overview 172.4.2 Unsupervised Learning 172.4.3 Supervised Learning 182.5 Classification Methods 192.5.1 Principal Component Analysis 192.5.2 Linear Discriminant Analysis 192.5.3 Kohonen Neural Network 192.5.4 Other Classification Methods 202.6 Methods for Data Modeling 202.6.1 Regression-Based QSAR Approaches 202.6.2 3D QSAR 222.6.3 Nonlinear Models 252.7 Summary on Data Analysis Methods 302.8 Model Validation 302.8.1 Proper Use of Validation Routines 312.8.2 Modeling/Validation Workflow 322.8.3 Splitting of Datasets 322.8.4 Compilation of Modeling, Training, Validation, Test, and External Sets 342.8.5 Cross-Validation 362.8.6 Bootstrapping 372.8.7 Y-Randomization (Y-Scrambling) 382.8.8 Goodness of Prediction and Quality Criteria 392.8.9 Applicability Domain and Model Acceptability Criteria 412.8.10 Scope of External and Internal Validation 432.8.11 Validation of Classification Models 452.9 Regulatory Use of QSARs 46Selected Reading 48References 493 Prediction of Physicochemical Properties of Compounds 53Igor V. Tetko, Aixia Yan, and Johann Gasteiger3.1 Introduction 533.2 Overview of Modeling Approaches to Predict Physicochemical Properties 543.2.1 Prediction of Properties Based on Other Properties 553.2.2 Prediction of Properties Based on Theoretical Calculations 553.2.3 Additivity Schemes for Property Prediction 563.2.4 Statistical Quantitative Structure–Property Relationships (QSPRs) 593.3 Methods for the Prediction of Individual Properties 593.3.1 Mean Molecular Polarizability 593.3.2 Thermodynamic Properties 603.3.3 Octanol/Water Partition Coefficient (Log P) 633.3.4 Octanol/Water Distribution Coefficient (log D) 673.3.5 Estimation of Water Solubility (log S) 693.3.6 Melting Point (MP) 713.3.7 Acid Ionization Constants 733.4 Limitations of Statistical Methods 763.5 Outlook and Perspectives 76Selected Reading 78References 784 Chemical Reactions 834.1 Chemical Reactions – An Introduction 84Johann GasteigerReferences 854.2 Reaction Prediction and Synthesis Design 86Jonathan M. Goodman4.2.1 Introduction 864.2.2 Reaction Prediction 874.2.3 Synthesis Design 944.2.4 Conclusion 102References 1034.3 Explorations into Biochemical Pathways 106Oliver Sacher and Johann Gasteiger4.3.1 Introduction 1064.3.2 The BioPath.Database 1104.3.3 BioPath.Explore 1114.3.4 Search Results 1124.3.5 Exploitation of the Information in BioPath.Database 1174.3.6 Summary 129Selected Reading 130References 1305 Structure–Spectrum Correlations and Computer-Assisted Structure Elucidation 133Joao Aires de Sousa5.1 Introduction 1335.2 Molecular Descriptors 1355.2.1 Fragment-Based Descriptors 1355.2.2 Topological Structure Codes 1355.2.3 Three-Dimensional Molecular Descriptors 1375.3 Infrared Spectra 1375.3.1 Overview 1375.3.2 Infrared Spectra Simulation 1385.4 NMR Spectra 1405.4.1 Quantum Chemistry Prediction of NMR Properties 1425.4.2 NMR Spectra Prediction by Database Searching 1425.4.3 NMR Spectra Prediction by Increment-Based Methods 1435.4.4 NMR Spectra Prediction by Machine Learning Methods 1445.5 Mass Spectra 1505.5.1 Identification of Structures and Interpretation of MS 1505.5.2 Prediction of MS 1515.5.3 Metabolomics and Natural Products 1515.6 Computer-Aided Structure Elucidation (CASE) 153Selected Reading 157Acknowledgement 157References 1586.1 Drug Discovery: An Overview 165Lothar Terfloth, Simon Spycher, and Johann Gasteiger6.1.1 Introduction 1656.1.2 Definitions of Some Terms Used in Drug Design 1676.1.3 The Drug Discovery Process 1676.1.4 Bio- and Chemoinformatics Tools for Drug Design 1686.1.5 Structure-based and Ligand-Based Drug Design 1686.1.6 Target Identification and Validation 1696.1.7 Lead Finding 1716.1.8 Lead Optimization 1826.1.9 Preclinical and Clinical Trials 1886.1.10 Outlook: Future Perspectives 189Selected Reading 191References 1916.2 Bridging Information on Drugs, Targets, and Diseases 195Andreas Steffen and Bertram Weiss6.2.1 Introduction 1956.2.2 Existing Data Sources 1966.2.3 Drug Discovery Use Cases in Computational Life Sciences 1966.2.4 Discussion and Outlook 201Selected Reading 202References 2026.3 Chemoinformatics in Natural Product Research 207Teresa Kaserer, Daniela Schuster, and Judith M. Rollinger6.3.1 Introduction 2076.3.2 Potential and Challenges 2086.3.3 Access to Software and Data 2116.3.4 In Silico Driven Pharmacognosy-Hyphenated Strategies 2196.3.5 Opportunities 2206.3.6 Miscellaneous Applications 2286.3.7 Limits 2286.3.8 Conclusion and Outlook 229Selected Reading 231References 2316.4 Chemoinformatics of Chinese Herbal Medicines 237Jun Xu6.4.1 Introduction 2376.4.2 Type 2 Diabetes: The Western Approach 2376.4.3 Type 2 Diabetes: The Chinese Herbal Medicines Approach 2386.4.4 Building a Bridge 2386.4.5 Screening Approach 240Selected Reading 244References 2446.5 PubChem 245Wolf-D. Ihlenfeldt6.5.1 Introduction 2456.5.2 Objectives 2466.5.3 Architecture 2466.5.4 Data Sources 2476.5.5 Submission Processing and Structure Representation 2486.5.6 Data Augmentation 2496.5.7 Preparation for Database Storage 2496.5.8 Query Data Preparation and Structure Searching 2506.5.9 Structure Query Input 2536.5.10 Query Processing 2546.5.11 Getting Started with PubChem 2546.5.12 Web Services 2556.5.13 Conclusion 255References 2566.6 Pharmacophore Perception and Applications 259Thomas Seidel, Gerhard Wolber, and Manuela S. Murgueitio6.6.1 Introduction 2596.6.2 Historical Development of the Modern Pharmacophore Concept 2606.6.3 Representation of Pharmacophores 2626.6.4 Pharmacophore Modeling 2686.6.5 Application of Pharmacophores in Drug Design 2726.6.6 Software for Computer-Aided Pharmacophore Modeling and Screening 2786.6.7 Summary 278Selected Reading 279References 2806.7 Prediction, Analysis, and Comparison of Active Sites 283Andrea Volkamer, Mathias M. von Behren, Stefan Bietz, and Matthias Rarey6.7.1 Introduction 2836.7.2 Active Site Prediction Algorithms 2846.7.3 Target Prioritization: Druggability Prediction 2926.7.4 Search for Sequentially Homologous Pockets 2966.7.5 Target Comparison: Virtual Active Site Screening 2986.7.6 Summary and Outlook 304Selected Reading 306References 3066.8 Structure-Based Virtual Screening 313Adrian Kolodzik, Nadine Schneider, and Matthias Rarey6.8.1 Introduction 3136.8.2 Docking Algorithms 3156.8.3 Scoring 3176.8.4 Structure-Based Virtual Screening Workflow 3216.8.5 Protein-Based Pharmacophoric Filters 3236.8.6 Validation 3236.8.7 Summary and Outlook 326Selected Reading 328References 3286.9 Prediction of ADME Properties 333Aixia Yan6.9.1 Introduction 3336.9.2 General Consideration on SPR/QSPR Models 3346.9.3 Estimation of Aqueous Solubility (log S) 3366.9.4 Estimation of Blood–Brain Barrier Permeability (log BB) 3426.9.5 Estimation of Human Intestinal Absorption (HIA) 3466.9.6 Other ADME Properties 3496.9.7 Summary 354Selected Reading 355References 3556.10 Prediction of Xenobiotic Metabolism 359Anthony Long and Ernest Murray6.10.1 Introduction: The Importance of Xenobiotic Biotransformation in the Life Sciences 3596.10.2 Biotransformation Types 3626.10.3 Brief Review of Methods 3646.10.4 User Needs: Scientists Use Metabolism Information in Different Ways 3706.10.5 Case Studies 372Selected Reading 382References 3836.11 Chemoinformatics at the CADD Group of the National Cancer Institute 385Megan L. Peach and Marc C. Nicklaus6.11.1 Introduction and History 3856.11.2 Chemical Information Services 3866.11.3 Tools and Software 3886.11.4 Synthesis and Activity Predictions 3916.11.5 Downloadable Datasets 391References 3926.12 Uncommon Data Sources for QSAR Modeling 395Alexander Tropsha6.12.1 Introduction 3956.12.2 Observational Metadata and QSAR Modeling 3976.12.3 Pharmacovigilance and QSAR 3986.12.4 Conclusions 401Selected Reading 402References 4026.13 Future Perspectives of Computational Drug Design 405Gisbert Schneider6.13.1 Where Do the Medicines of the Future Come from? 4056.13.2 Integrating Design, Synthesis, and Testing 4086.13.3 Toward Precision Medicine 4096.13.4 Learning from Nature: From Complex Templates to Simple Designs 4116.13.5 Conclusions 413Selected Reading 414References 4147 Computational Approaches in Agricultural Research 417Klaus-Jürgen Schleifer7.1 Introduction 4177.2 Research Strategies 4187.2.1 Ligand-Based Approaches 4197.2.2 Structure-Based Approaches 4227.3 Estimation of Adverse Effects 4297.3.1 In Silico Toxicology 4297.3.2 Programs and Databases 4307.3.3 In Silico Toxicology Models 4327.4 Conclusion 435Selected Reading 436References 4368 Chemoinformatics in Modern Regulatory Science 439Chihae Yang, James F. Rathman, Aleksey Tarkhov, Oliver Sacher, Thomas Kleinoeder, Jie Liu, Thomas Magdziarz, Aleksandra Mostraq, Joerg Marusczyk, Darshan Mehta, Christof Schwab, and Bruno Bienfait8.1 Introduction 4398.1.1 Science and Technology Progress 4398.1.2 Regulatory Science in Twenty-First Century 4408.2 Data Gap Filling Methods in Risk Assessment 4418.2.1 QSAR and Structural Knowledge 4428.2.2 Threshold of Toxicological Concern (TTC) 4438.2.3 Read-Across (RA) 4458.3 Database and Knowledge Base 4488.3.1 Architecture of Structure-Searchable Toxicity Database 4488.3.2 Data Model for Chemistry-Centered Toxicity Database 4498.3.3 Inventories 4528.4 New Approach Descriptors 4538.4.1 ToxPrint Chemotypes 4538.4.2 Liver BioPath Chemotypes 4588.4.3 Dynamic Generation of Annotated Linear Paths 4598.4.4 Other Examples of Descriptors 4618.5 Chemical Space Analysis 4628.5.1 Principal Component Analysis 4628.6 Summary 464Selected Reading 466References 4669 Chemometrics in Analytical Chemistry 471Anita Rácz, Dávid Bajusz, and Károly Héberger9.1 Introduction 4719.2 Sources of Data: Data Preprocessing 4729.3 Data Analysis Methods 4759.3.1 Qualitative Methods 4759.3.2 Quantitative Methods 4839.4 Validation 4889.5 Applications 4929.6 Outlook and Prospects 492Selected Reading 496References 49610 Chemoinformatics in Food Science 501Andrea Peña-Castillo, Oscar Méndez-Lucio, John R. Owen, Karina Martínez-Mayorga, and José L. Medina-Franco10.1 Introduction 50110.2 Scope of Chemoinformatics in Food Chemistry 50210.3 Molecular Databases of Food Chemicals 50310.4 Chemical Space of Food Chemicals 50610.4.1 General Considerations 50610.4.2 Chemical Space Analysis of Food Chemical Databases 50810.5 Structure–Property Relationships 51010.5.1 Structure–Flavor Relationships and Flavor Cliffs 51110.5.2 Quantitative Structure–Odor Relationships 51210.6 Computational Screening and Data Mining of Food Chemicals Libraries 51310.6.1 Anticonvulsant Effect of Sweeteners and Pharmaceutical and Food Preservatives 51410.6.2 Mining Food Chemicals as Potential Epigenetic Modulators 51610.7 Conclusion 521Selected Reading 522References 52311 Computational Approaches to Cosmetics Products Discovery 527Soheila Anzali, Frank Pflücker, Lilia Heider, and Alfred Jonczyk11.1 Introduction: Cosmetics Demands on Computational Approaches 52711.2 Case I: The Multifunctional Role of Ectoine as a Natural Cell Protectant (Product: Ectoine, "Cell Protection Factor", and Moisturizer) 52811.2.1 Molecular Dynamics (MD) Simulations 53011.2.2 Results and Discussion: Ectoine Retains the Power of Water 53111.3 Case II: A Smart Cyclopeptide Mimics the RGD Containing Cell Adhesion Proteins at the Right Site (Product: Cyclopeptide-5: Antiaging) 53311.3.1 Methods 53611.3.2 Results and Discussion 53611.4 Conclusions: Cases I and II 542References 54512 Applications in Materials Science 547Tu C. Le, and David A. Winkler12.1 Introduction 54712.2 Why Materials Are Harder to Model than Molecules 54812.3 Why Are Chemoinformatics Methods Important Now? 54812.4 How Do You Describe Materials Mathematically? 54912.5 How Well do Chemoinformatics Methods Work on Materials? 55112.6 What Are the Pitfalls when Modeling Materials? 55112.7 How Do You Make Good Models and Avoid the Pitfalls? 55312.8 Materials Examples 55412.8.1 Inorganic Materials and Nanomaterials 55412.8.2 Polymers 55712.8.3 Catalysts 55812.8.4 Metal–Organic Frameworks (MOFs) 56012.9 Biomaterials Examples 56112.9.1 Bioactive Polymers 56112.9.2 Microarrays 56412.10 Perspectives 566Selected Reading 567References 56713 Process Control and Soft Sensors 571Kimito Funatsu13.1 Introduction 57113.2 Roles of Soft Sensors 57313.3 Problems with Soft Sensors 57413.4 Adaptive Soft Sensors 57613.5 Database Monitoring for Soft Sensors 57813.6 Efficient Process Control Using Soft Sensors 58113.7 Conclusions 582Selected Readings 583References 58314 Future Directions 585Johann Gasteiger14.1 Well-Established Fields of Application 58514.2 Emerging Fields of Application 58614.3 Renaissance of Some Fields 58714.4 Combined Use of Chemoinformatics Methods 58814.5 Impact on Chemical Research 589Index 591