Chemoinformatics

Basic Concepts and Methods

Edited by Thomas Engel and Johann Gasteiger Editors All books published by Wiley-VCH are carefully produced. Nevertheless, authors, Dr. Thomas Engel editors, and publisher do not warrant the LMU München information contained in these books, Department of Chemistry including this book, to be free of errors. Butenandtstraße 5-13 Readers are advised to keep in mind that 81377 München statements, data, illustrations, procedural Germany details or other items may inadvertently be inaccurate. Prof. Dr. Johann Gasteiger Library of Congress Card No.: Universität Erlangen-Nürnberg applied for Computer-Chemie-Centrum Nägelsbachstr. 25 British Library Cataloguing-in-Publication 91052 Erlangen Data Germany A catalogue record for this book is available from the British Library. Cover Design Dr. Christian R. Wick Bibliographic information published by University of Erlangen-Nürnberg the Deutsche Nationalbibliothek Institute for Theoretical Physics I The Deutsche Nationalbibliothek lists Nägelsbachstr. 49b (EAM) this publication in the Deutsche Nation- 91052 Erlangen albibliograﬁe; detailed bibliographic Germany dataareavailableontheInternetat .

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10987654321 Thomas Engel

To my family especially Benedikt and to Guido Kirsten, Markus Sitzmann, and Achim Zielesny for the very valuable feedback.

Johann Gasteiger

To all the friends, colleagues and coworkers that ventured with me into the exciting ﬁeld of chemoinformatics. And to my wife Uli for never complaining about my long working hours. If you want to build a ship, don’t drum up people to collect wood and don’t assign them tasks and work, but rather teach them to long for the endless immensity of the sea. Antoine de Saint-Exupéry vii

Contents

Foreword xxi List of Contributors xxv

1 Introduction 1 Thomas Engel and Johann Gasteiger 1.1 The Rationale for the Books 1 1.2 The Objectives of Chemoinformatics 2 1.3 Learning in Chemoinformatics 4 1.4 Outline of the Book 5 1.5 TheScopeoftheBook 7 1.6 Teaching Chemoinformatics 8 References 8

2 Principles of Molecular Representations 9 Thomas Engel 2.1 Introduction 9 2.2 Chemical Nomenclature 11 2.2.1 Non-systematic Nomenclature (Trivial Names) 11 2.2.2 Systematic Nomenclature of Chemical Compounds 12 2.2.3 Drawbacks of Chemical Nomenclature for Data Processing 12 2.3 Chemical Notations 12 2.3.1 Empirical Formulas of Inorganic and Organic Compounds 12 2.3.2 Line Notations 14 2.4 Mathematical Notations 14 2.4.1 Introduction into Graph Theory 15 2.4.2 Matrix Representations 18 2.4.2.1 Adjacency Matrix 18 2.4.2.2 Incidence Matrix 19 2.4.2.3 Distance Matrix 20 2.4.2.4 Bond Matrix 21 2.4.2.5 Bond–Electron Matrix 21 2.4.2.6 Summary on Matrix Representations 23 2.4.3 Connection Table 23 2.5 Speciﬁc Types of Chemical Structures 25 viii Contents

2.5.1 General Concepts of Isomerism 25 2.5.2 Tautomerism 26 2.5.3 Markush Structures 27 2.5.4 Beyond a Connection Table Representation 28 2.5.4.1 Representation of Molecular Structures by Electron Systems 28 2.6 Spatial Representation of Structures 31 2.6.1 Representation of Conﬁgurational Isomers 32 2.6.2 Chirality 33 2.6.3 3D Coordinate Systems 36 2.7 Molecular Surfaces 37 Selected Reading 38 References 39

3 Computer Processing of Chemical Structure Information 43 Thomas Engel 3.1 Introduction 43 3.2 Standard File Formats for Chemical Structure Information 44 3.2.1 SMILES 44 3.2.1.1 Stereochemistry in SMILES 47 3.2.1.2 Summary on SMILES 47 3.2.2 SMARTS 47 3.2.3 SYBYL Line Notation 48 3.2.4 The International Chemical Identifier (InChI) and InChIKey 48 3.2.5 XYZ Format 50 3.2.6 Z-Matrix 51 3.2.7 The Molfile Format Family 52 3.2.7.1 Structure of a Molfile 53 3.2.7.2 Stereochemistry in the Molfile 57 3.2.7.3 Structure of an SDfile 57 3.2.8 The PDB File Format 58 3.2.8.1 Introduction/History 58 3.2.8.2 General Description 58 3.2.8.3 Analysis of a Sample PDB File 60 3.2.9 Metadata Formats 65 3.2.9.1 STAR-Based File Formats and Dictionaries 65 3.2.9.2 CIF File Format 66 3.2.9.3 mmCIF File Format 67 3.2.9.4 CML 68 3.2.9.5 CSRML 68 3.2.10 Libraries for Handling Information in Structure File Formats 69 3.3 Input and Output of Chemical Structures 70 3.3.1 Molecule Editors 72 3.3.2 Molecule Viewers 73 3.4 Processing Constitutional Information 73 3.4.1 Structure Isomers and Isomorphism 73 3.4.2 Tautomerism 74 Contents ix

3.4.3 Unambiguous and Biunique Representation by Canonicalization 76 3.4.3.1 The Morgan Algorithm 77 3.4.4 Ring Perception 79 3.4.4.1 Introduction 79 3.4.4.2 Graph Terminology 80 3.4.4.3 Ring Perception Strategies 81 3.5 Processing 3D Structure Information 86 3.5.1 Detection and Specification of Chirality 86 3.5.1.1 Detection of Chirality 87 3.5.1.2 Specification of Chirality 87 3.5.2 Automatic Generation of 3D Structures 90 3.5.3 Automatic Generation of Ensemble of Conformations 94 3.6 Visualization of Molecular Models 100 3.6.1 Introduction 100 3.6.2 Models of the 3D Structure 101 3.6.2.1 Wire Frame and Capped Sticks Model 101 3.6.2.2 Ball-and-Stick Model 101 3.6.2.3 Space-Filling Model 102 3.6.2.4 Crystallographic Models 102 3.6.3 Models of Biological Macromolecules 102 3.6.4 Virtual Reality 103 3.6.5 3D Printing 103 3.7 Calculation of Molecular Surfaces 103 3.7.1 Van der Waals Surface 104 3.7.2 Connolly Surface 104 3.7.3 Solvent-Accessible Surface 105 3.7.4 Enzyme Cavity Surface (Union Surface) 106 3.7.5 Isovalue-Based Electron Density Surface 106 3.7.6 Experimentally Determined Surfaces 106 3.7.7 Visualization of Molecular Surface Properties 107 3.7.8 Property-based Isosurfaces 107 3.7.8.1 Electrostatic Potentials 108 3.7.8.2 Hydrogen Bonding Potential 108 3.7.8.3 Polarizability and Hydrophobicity Potential 108 3.7.8.4 Spin Density 108 3.7.8.5 Vector Fields 108 3.7.8.6 Volumetric Properties 108 3.8 Chemoinformatic Toolkits and Workflow Environments 109 Selected Reading 111 References 111

4 Representation of Chemical Reactions 121 Oliver Sacher and Johann Gasteiger 4.1 Introduction 121 4.2 Reaction Equation 122 4.3 Reaction Types 123 x Contents

4.4 Reaction Center and Reaction Mechanisms 125 4.5 Chemical Reactivity 126 4.5.1 Physicochemical Effects 126 4.5.1.1 Charge Distribution 126 4.5.1.2 Inductive Effect 127 4.5.1.3 Resonance Effect 127 4.5.1.4 Polarizability Effect 128 4.5.1.5 Steric Effect 128 4.5.1.6 Stereoelectronic Effects 128 4.5.2 Simple Methods for Quantifying Chemical Reactivity 128 4.5.2.1 Frontier Molecular Orbital Theory 128 4.5.2.2 Linear Free Energy Relationships 130 4.6 Learning from Reaction Information 132 4.7 Building of Reaction Databases 133 4.7.1 Contents 133 4.7.2 Reaction Data Exchange Formats 134 4.7.2.1 RXN/RDF format by MDL/Symyx 134 4.7.2.2 Reaction SMILES/SMIRKS by Daylight Chemical Information Systems 134 4.7.2.3 Chemical Markup Language 135 4.7.2.4 International Chemical Identifier for Reactions (RinChI) 135 4.7.3 Input and Output of Reactions 135 4.8 Reaction Center Perception 138 4.9 Reaction Classification 139 4.9.1 Model-Driven Approaches 139 4.9.1.1 Ugi’s Scheme and Some Follow-Ups 140 4.9.1.2 InfoChem’s Reaction Classification 143 4.9.2 Data-Driven Approaches 145 4.9.2.1 HORACE 145 4.9.2.2 Reaction Landscapes 146 4.10 Stereochemistry of Reactions 148 4.11 Reaction Networks 149 Selected Reading 151 References 152

5TheData155 5.1 Introduction 155 5.2 Data Types 156 5.2.1 Numerical Data 157 5.2.2 Molecular Structures 159 5.2.3 Bit Vectors 160 5.2.3.1 Hash Codes 160 5.2.3.2 Structural Keys 162 5.2.3.3 Fingerprints 163 5.2.4 Chemical Reactions 164 5.2.5 Molecular Spectra 165 5.3 Storage and Manipulation of Data 169 5.3.1 Experimental Data 169 Contents xi

5.3.1.1 Types of Data on Properties 170 5.3.1.2 Accuracy of the Data 170 5.3.2 Data Storage and Exchange 171 5.3.2.1 DAT File 171 5.3.2.2 JCAMP-DX 171 5.3.2.3 Predictive Model Markup Language (PMML) 172 5.3.3 Real-World Data 173 5.3.3.1 Data Complexity 173 5.3.3.2 Outliers and Redundant Objects 174 5.3.4 Data Transformation 175 5.3.4.1 Fast Fourier Transformation 175 5.3.4.2 Wavelet Transformation 175 5.3.5 Preparation of Datasets for Building of Models and Validations of Their Quality 176 5.4 Conclusions 177 Selected Reading 178 References 179

6 Databases and Data Sources in Chemistry 185 Engelbert Zass and Thomas Engel 6.1 Introduction 185 6.2 Chemical Literature and Databases 186 6.2.1 Classiﬁcation of Chemical Literature 186 6.2.2 The Origin of Chemical Databases 187 6.2.3 Evolution of Database Systems and User Interfaces 187 6.3 Major Chemical Database Systems 188 6.3.1 SciFinder 188 6.3.2 Reaxys 189 6.3.3 SciFinder versus Reaxys 190 6.4 Compound Databases 191 6.4.1 2D Structures 191 6.4.1.1 Searching Organic Compounds 192 6.4.1.2 Searching Inorganic and Coordination Compounds 194 6.4.2 Sequences of Biopolymers 195 6.4.3 3D Structures 198 6.4.4 Catalog Databases 200 6.5 Databases with Properties of Compounds 200 6.5.1 Physical Properties 201 6.5.2 Thermodynamic and Thermochemical Data 202 6.5.3 Spectra 204 6.5.3.1 Spectroscopic Databases 205 6.5.3.2 Compound Databases with Spectroscopic Information 205 6.5.4 Biological, Environmental, and Safety Information Sources 206 6.5.4.1 Biological Information 207 6.5.4.2 Pharmaceutical and Medical Information 208 6.5.4.3 Toxicity, Environmental, and Safety Information 209 6.6 Reaction Databases 210 xii Contents

6.6.1 Comprehensive Reaction Databases 210 6.6.2 Synthetic Methodology Databases 212 6.7 Bibliographic and Citation Databases 212 6.7.1 Bibliographic Databases 213 6.7.1.1 Special Bibliographic Databases 213 6.7.1.2 Patent Bibliographic Databases 214 6.7.1.3 Searching Bibliographic Databases 216 6.7.1.4 Linking to Full Text 216 6.7.2 Citation Databases 217 6.7.2.1 General Citation Databases 218 6.7.2.2 Patent Citation Databases 219 6.8 Full-Text Databases 219 6.8.1 Electronic Journals 219 6.8.2 Patents 220 6.8.3 Lexika and Encyclopedias 221 6.9 Architecture of a Structure-Searchable Database 222 Selected Reading 224 References 224

7 Searching Chemical Structures 231 Nikolay Kochev, Valentin Monev, and Ivan Bangov 7.1 Introduction 231 7.2 Full Structure Search 232 7.3 Substructure Search 235 7.3.1 Basic Concepts 235 7.3.2 Backtracking Algorithm 236 7.3.3 Optimization of the Backtracking Algorithm 238 7.3.4 Screening 239 7.3.5 Superstructure Searching 241 7.3.6 Automorphism Searching 241 7.3.7 Maximum Common Substructure Searching 242 7.3.8 Speciﬁc Line Notations for Substructure Searching 243 7.3.9 Chemotypes for Database Searching 244 7.4 Similarity Search 245 7.4.1 Similarity Basics 245 7.4.2 Similarity Measures 247 7.4.3 Descriptor Selection and Coding 249 7.4.4 Similarity Measures Based on Maximum Common Substructure 250 7.5 Three-Dimensional Structure Search Methods 250 7.5.1 Pharmacophore Searching 251 7.5.2 3D Similarity Searching 252 7.6 Sequence Searching in Protein and Nucleic Acid Databases 254 7.6.1 Sequence Similarity Deﬁnition 255 7.6.2 Dynamic Programming Algorithm 256 7.6.3 Fast Sequence Searching in Large Databases 258 7.7 Summary 259 Contents xiii

Selected Reading 261 References 262

8 Computational Chemistry 267

8.1 Empirical Approaches to the Calculation of Properties 269 Johann Gasteiger 8.1.1 Introduction 269 8.1.2 Additivity of Atomic Contributions 269 8.1.3 Attenuation Models 271 8.1.3.1 Calculation of Charge Distribution 271 8.1.3.2 Polarizability Eﬀect 275 Selected Reading 277 References 277

8.2 Molecular Mechanics 279 Harald Lanig 8.2.1 Introduction 279 8.2.2 No Force Field Calculation without Atom Types 280 8.2.3 The Functional Form of Common Force Fields 281 8.2.3.1 Bond Stretching 282 8.2.3.2 Angle Bending 283 8.2.3.3 Torsional Terms 284 8.2.3.4 Out-of-Plane Bending 285 8.2.3.5 Electrostatic Interactions 286 8.2.3.6 Van der Waals Interactions 287 8.2.3.7 Cross Terms 289 8.2.3.8 Advanced Interatomic Potentials and Future Development 290 8.2.4 Available Force Fields 291 8.2.4.1 Force Fields for Small Molecules 292 8.2.4.2 Force Fields for Biomolecules 293 Selected Readings 296 References 296

8.3 Molecular Dynamics 301 Harald Lanig 8.3.1 Introduction 301 8.3.2 The Continuous Movement of Molecules 302 8.3.3 Methods 302 8.3.3.1 Algorithms 303 8.3.3.2 Ways for Speeding up the Calculations 304 8.3.3.3 Solvent Eﬀects 305 8.3.3.4 Periodic Boundary Conditions 308 8.3.4 Constant Energy, Temperature, or Pressure? 308 xiv Contents

8.3.5 Long-Range Forces 310 8.3.6 Application of Molecular Dynamics Techniques 311 8.3.7 Future Perspectives 315 Selected Readings 317 References 317

8.4 Quantum Mechanics 320 Tim Clark 8.4.1 Hückel Molecular Orbital Theory 320 8.4.2 Semiempirical MO Theory 324 8.4.3 Ab Initio Molecular Orbital Theory 327 8.4.4 Density Functional Theory 332 8.4.5 Properties from Quantum Mechanical Calculations 334 8.4.5.1 Net Atomic Charges 334 8.4.5.2 Dipole and Higher Multipole Moments 335 8.4.5.3 Polarizabilities 335 8.4.5.4 Orbital Energies 336 8.4.5.5 Surface Descriptors 336 8.4.5.6 Local Ionization Potential 336 8.4.6 Quantum Mechanical Techniques for Very Large Molecules 337 8.4.6.1 Linear Scaling Methods 337 8.4.6.2 Hybrid QM/MM Calculations 338 8.4.7 The Future of Quantum Mechanical Methods in Chemoinformatics 338 Selected Reading 340 References 341

9 Modeling and Prediction of Properties (QSPR/QSAR) 345 Johann Gasteiger

10 Calculation of Structure Descriptors 349 Lothar Terfloth and Johann Gasteiger 10.1 Introduction 349 10.1.1 QSPR/QSAR Modeling 349 10.1.2 Overview 349 10.1.3 Classification of Compounds and Similarity Searching 350 10.1.4 Definition of the Terms “Structure Descriptor” and “Molecular Descriptor” 351 10.1.5 Classification of Structure Descriptors 351 10.1.6 Structure Descriptors with a Fixed Length 351 10.2 Structure Descriptors for Classification and Similarity Searching 352 10.2.1 2D Structure Descriptors (Topological Descriptors) 352 10.2.1.1 Structural Keys 352 10.2.1.2 Fingerprints 353 10.2.1.3 Distance and Similarity Measures 354 Contents xv

10.2.1.4 Chemotypes: Data Mining for Compounds with Structural Features 356 10.2.1.5 Multilevel Neighborhoods of Atoms 358 10.2.1.6 Descriptors from Shannon Entropy Calculations 359 10.2.1.7 Chemically Advanced Template Search (CATS2D) Descriptors 360 10.2.1.8 Descriptors from Chemical Bond Information 360 10.2.2 3D Descriptors 361 10.2.2.1 Geometric Atom Pair Descriptors 361 10.2.2.2 CATS3D and CHARGE3D 361 10.2.2.3 Pharmacophores 362 10.2.3 Field-Based Molecular Similarity 362 10.2.3.1 Electron Density 362 10.2.3.2 General Field-Based Similarity Indices 363 10.3 Structure Descriptors for Quantitative Modeling 363 10.3.1 0-D Molecular Descriptors 363 10.3.2 1D Molecular Descriptors 363 10.3.3 2D Molecular Descriptors (Topological Descriptors) 365 10.3.3.1 Single-Valued Descriptors 365 10.3.3.2 Topological Descriptors as Vectors 366 10.3.4 3D Descriptors 369 10.3.4.1 3D Structure Generation 369 10.3.4.2 3D Autocorrelation Vector 370 10.3.4.3 3D Molecule Representation of Structures Based on Electron Diﬀraction Code (3D MoRSE Code) 370 10.3.4.4 Radial Distribution Function Code 371 10.3.4.5 Other 3D Descriptors 375 10.3.5 Chirality Descriptors 375 10.3.5.1 Chirality Codes 376 10.3.5.2 Conformation-Independent Chirality Code (CICC) 376 10.3.5.3 Conformation-Dependent Chirality Code (CDCC) 377 10.3.5.4 Descriptors of Molecular Shape and Molecular Surfaces 377 10.3.5.5 Global Shape Descriptors 378 10.3.5.6 Autocorrelation of Molecular Surface Properties 378 10.3.5.7 2D Maps of Molecular Surfaces 379 10.3.5.8 Charged Partial Surface Area 382 10.3.6 Field-Based Methods 383 10.3.6.1 Comparative Molecular Field Analysis (CoMFA) 383 10.3.6.2 Comparative Molecular Similarity Analysis (CoMSIA) 384 10.3.6.3 3D Molecular Interaction Fields 384 10.3.7 Descriptors for an Ensemble of Conformations (4D Descriptors) 384 10.3.7.1 4D-QSAR 384 10.3.8 Quantum Chemical Descriptors 385 10.4 Descriptors That Are Not Calculated from the Chemical Structure 385 10.5 Summary and Outlook 387 xvi Contents

Selected Reading 390 References 390

11 Data Analysis and Data Handling (QSPR/QSAR) 397

11.1 Methods for Multivariate Data Analysis 399 Kurt Varmuza 11.1.1 Introduction into Multivariate Data Analysis 399 11.1.1.1 Aims 399 11.1.1.2 Notation and Symbols 400 11.1.2 Basics of Statistical Data Evaluation 401 11.1.2.1 Data Distribution, Central Value, and Spread 401 11.1.2.2 Correlation 404 11.1.2.3 Discrimination 405 11.1.3 Multivariate Data 406 11.1.3.1 Overview 406 11.1.3.2 Preprocessing 407 11.1.3.3 Distances and Similarities 408 11.1.3.4 Linear Latent Variables 410 11.1.4 Evaluation of Empirical Models 412 11.1.4.1 Overview 412 11.1.4.2 Optimum Model Complexity 412 11.1.4.3 Performance Criteria for Calibration Models 413 11.1.4.4 Performance Criteria for Classiﬁcation Models 414 11.1.4.5 Cross-Validation 415 11.1.4.6 Bootstrap 416 11.1.5 Exploration: Analyzing the Independent Variables 417 11.1.5.1 Overview 417 11.1.5.2 Principal Component Analysis (PCA) 417 11.1.5.3 Nonlinear Mapping 419 11.1.5.4 Cluster Analysis 419 11.1.5.5 Example: Exploratory Data Analysis of Mass Spectra from Meteorite Samples 421 11.1.6 Calibration: Building a Quantitative Model 423 11.1.6.1 Overview 423 11.1.6.2 Ordinary Least Squares (OLS) Regression 424 11.1.6.3 Principal Component Regression (PCR) 424 11.1.6.4 Partial Least Squares (PLS) Regression 425 11.1.6.5 Variable Selection 426 11.1.6.6 Example: Prediction of Gas Chromatographic Retention Indices for Polycyclic Aromatic Hydrocarbons 427 11.1.7 Classiﬁcation: Discriminating Samples 428 11.1.7.1 Overview 428 11.1.7.2 Linear Discriminant Analysis (LDA) 430 11.1.7.3 Discriminant Partial Least Squares (D-PLS) Analysis 430 Contents xvii

11.1.7.4 k-Nearest Neighbor (KNN) Classification 430 11.1.7.5 Support Vector Machine (SVM) 431 11.1.7.6 Classification Trees (CART) 432 11.1.7.7 Example: Classification of Meteorite Samples Using Mass Spectral Data 432 Acknowledgements 434 Selected Reading 435 References 435

11.2 Artiﬁcial Neural Networks (ANNs) 438 Jure Zupan 11.2.1 How to Learn a New Method? 438 11.2.2 Multivariate Representation of Data 439 11.2.3 Overview of Artiﬁcial Neural Networks (ANNs) 442 11.2.4 Error Back-Propagation ANNs 443 11.2.5 Kohonen and Counter-Propagation ANN 445 11.2.6 Training of the ANN: Adapting the Weights 448 11.2.7 Controlling Model Complexity and Optimizing Predictivity 450 11.2.8 Few General Remarks about ANNs 450 Selected Reading 451 References 451

11.3 Deep and Shallow Neural Networks 453 David A. Winkler 11.3.1 Drug Design in the Era of Big Data and Artiﬁcial Intelligence (AI) 453 11.3.2 Deep Learning 454 11.3.3 Controlling Model Complexity and Optimizing Predictivity Using Regularization 455 11.3.4 Universal Approximation Theorem 458 11.3.5 Do QSAR Models Generated by Neural Networks Meet the Requirements of the Universal Approximation Theorem? 458 11.3.6 Comparison of the Performance of Deep and Shallow Regularized Neural Networks on Drug Datasets 459 11.3.7 A Few General Remarks about Neural Networks for Drug Discovery 460 Selected Reading 462 References 462

12 QSAR/QSPR Revisited 465 Alexander Golbraikh and Alexander Tropsha 12.1 Best Practices of QSAR Modeling 466 12.1.1 Introduction 466 12.1.2 Key Concepts 467 12.1.3 Predictive QSAR Modeling Workﬂow 468 xviii Contents

12.1.4 Dataset Curation 469 12.1.5 Modelability Studies 470 12.1.6 Development of QSAR Models: Internal and External Validation 471 12.1.7 Prediction Accuracy Criteria for QSAR Models for a Continuous Response Variable 472 12.1.8 Prediction Accuracy Criteria for Category QSAR Models 473 12.1.9 Time-Split Validation 475 12.1.10 Validation by Y-Randomization 475 12.1.11 Applicability Domain of QSAR Models 475 12.1.11.1 Leverage AD for Regression QSAR Models 476 12.1.11.2 Residual Standard Deviation (RSD) as AD 476 12.1.11.3 Other widely Used ADs 476 12.1.12 Ensemble Modeling 478 12.1.13 Model Interpretation: Structural Alerts 478 12.1.14 Virtual Screening 479 12.1.15 Conclusions 480 12.2 The Data Science of QSAR Modeling 480 12.2.1 Introduction 480 12.2.2 Data Curation: Trust but Verify! 482 12.2.3 Models as Decision Support Tools 487 12.2.4 Conclusions 487 Selected Reading 489 References 489

13 Bioinformatics 497 Heinrich Sticht 13.1 Introduction 497 13.2 Sequence Databases 499 13.2.1 GenBank 499 13.2.2 UniProt 501 13.3 Searching Sequence Databases 502 13.3.1 Tools for Sequence Database Searches 503 13.3.2 Scoring Matrices 503 13.3.3 Interpretation of the Results of a Database Search 507 13.4 Characterization of Protein Families 509 13.4.1 Multiple Sequence Alignment 509 13.4.2 Sequence Signatures 512 13.5 Homology Modeling 515 Selected Reading 520 References 520

14 Future Directions 525 Johann Gasteiger 14.1 Access to Chemical Information 525 Contents xix

14.2 Representation of Chemical Compounds 527 14.3 Representation of Chemical Reactions 527 14.4 Learning from Chemical Information 528 14.5 Training in Chemoinformatics 529

Answers Section 531

Index 555

xxi

Foreword

Chemistry began with magic. Who but a wizard could, with a puff of smoke, turn one thing into another? The alchemists believed that the ability to transform materials was a valuable skill, so valuable in fact that they devised complex descriptions and alchemical symbols, known only to them, to represent their secret methods. Information was encoded and hidden, suffused with allegorical and religious symbolism, slowing progress. Medicinal chemists today may be particularly interested in a legendary stone called a Bezoar, found in the bodies of animals (if you knew which animal to dissect), that had universal curative properties. I’m still looking. However, to plagiarize a recent Nobel Prize winner for literature, the times they were a changin’. Departing from the secretive “alchemist” approach, Berzelius (1779–1848) suggested compounds should be named from the elements that made them up, and Archibald Scott Couper (1831–1892) devised the “connections” between “atoms,” which gave rise to structural diagrams (1858). In 1887, the symbols created by Jean Henri Hassenfratz and Pierre Auguste Adet to complement the Methode de Nomen- clature Chimique were a revolutionary approach to chemical information. A jumbled, confused, and incorrect nomenclature was replaced by our modern-day designations such as oxygen, hydrogen, and sodium chloride. The new chemistry of Lavoisier was becoming systematized. The “Age of Enlightenment” created a new philosophy of science where information, validated by experiment, could be tested by an expanding community of “scientists” (a term coined by William Whewell in 1833), placing data at the core of chemistry. With the accumulation of knowledge, and a language to communicate chemistry, the stage was set for the creation of a new science of information in the domain of chemistry. Up stepped Friedrich Beilstein (1838–1906), who systematically collected chemical data on substances, reactions, and properties of chemical compounds in the Handbuch der organischen Chemie (Handbook of Organic Chemistry, published in 1881). The naming of compounds was a key feature that enabled the storage and retrieval of chemical information on a “grand” scale (1500 compounds). The indexing of chemical information meant chemistry could be reliably stored, common links between data established, and – most importantly – the information could be retrieved without loss. This drive for efficient indexing was the dominant feature of chemical information research for the next half century. As chemistry (and its many related disciplines) continued on an ever upward trajectory of innovation (and data collection), xxii Foreword

the paper trail required to go from perfectly reasonable questions like “how do I synthesize this compound?” to “has this compound been made before?” became rather complex and time-consuming. I remember many happy hours spent in the library of the Wellcome Foundation trawling through the multitude of bookshelves of Chemical Abstracts to ﬁnd one compound and, if lucky, a synthesis simple enough that I could perform with a yield better than my usual ten percent. Of course things got worse (or better if you were a librarian), and I recall an interesting RSC symposium in 1994 called “The Chemical Information Explosion: Chaos, Chemists and Computers.” We had clearly reached a point where someone had to invent chemoinformatics. Although the “someone” is of course a worldwide community of scientists interested in chemical data, the term was coined by Frank Brown in 1998, and he deﬁned it as

“The mixing of those information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug lead identiﬁcation and optimization.”

The combination of multidisciplinarity, the reduction of data to knowledge, and the driving force of the pharmaceutical industry have been key features of the advance of chemoinformatics. The enabling technologies have been the availability of unprecedented amounts of chemical data (increasingly pubicly available) and the continuous development of new algorithms, designed specif- ically for chemistry, to achieve the goal of turning information into knowledge. Of course Moore’s law (an observation by Gordon Moore at Intel), wherein the density of computer components (and the computation power offered) doubles every 2 years, has underpinned the hardware necessary to keep pace with the data explosion. But perhaps some of the chaos remains, hence the popularity of software such as Babel (which converts many data formats to many data formats!). Some numbers here are interesting. If we recall that the first edition of Beilsteins Handbuch contained 1500 compounds, the Chemical Abstracts Service of the American Chemical Society reported in 2015 that they had registered their 100 millionth chemical substance. What is truly transformational (if you think about it) is that a new student, with a basic knowledge of chemistry, when asked to search for a single compound from the 100M registry, gets the correct result in a microsecond. Not only that, a host of measured and predicted chemical properties, synthesis strategies, available reagents, structurally similar compounds and Internet links to a multitude of other diverse, information-rich databases is available. Clearly, chemoinformatics has come of age. In fact the term “chemoinformatics” has gained a certain elastic quality. The methodologies and data analysis tools developed for chemical information have evolved and extended to the data analytics of essentially any data that includes chemistry. Examples include the simulations of large systems of molecules such as proteins, machine learning (and the recent resurgence in artificial intelligence) to create predictive models, for example, metabolism, ADME properties, and quantitative structure–activity Foreword xxiii relationships of drugs (including quantum chemistry, bioinformatics, and analytical chemistry) and the detection and analysis of drug binding sites. Although much of the early work in chemoinformatics has been applied to problems of the pharmaceutical industry, the subject has been embraced across the sciences wherever chemistry is required, for example, in agricultural and food research, cosmetics, and materials science. But in an age when computers can do “magic,” (“Anysufficientlyadvancedtech- nology is indistinguishable from magic” – Arthur C. Clark), it is tempting to return towherewewereinthetimeofBerzeliusandhidethetechnologybehindan alchemical mask of symbols, for example, a simple interface that hides highly complex search and retrieval algorithms or a machine learning application to predict metabolism. The antidote to this is of course education. A firm grounding in the principles and practice of chemoinformatics provides students and expert practitioners alike with the knowledge of the underlying algorithms, how they are implemented, their availability and of course limitations of software for a given purpose as well as future challenges for those with a keen interest in developing the field. The best textbooks are naturally written from the viewpoint of those who are intimately connected to their subject. The chemoinformatics group at the Computer-Chemie-Centrum (CCC) at the University of Erlangen-Nuremberg has been pioneers in chemoinformatics for over thirty years and are recognized as both innovators and experts at applying these methods to a large variety of chemical problems. However it is as educators that perhaps their greatest impact on the field may accrue over time. The new textbook Chemoinfor- matics – Basic Concepts and Methods builds on the successful first edition Chemoinformatics: A Textbook (published in 2003) and is again edited by Johann Gasteiger and Thomas Engel. This volume is complemented by an additional book Applied Chemoinformatics – Achievements and Future Opportunities, which shows the many fields chemoinformatics is now applied to. Johann Gasteiger has had a distinguished career in chemistry and is well known for his seminal contributions to chemoinformatics. He was the recipient of the 1991 Gmelin-Beilstein Medal of the German Chemical Society for Achieve- ments in Computer Chemistry, the 2005 Mike Lynch Award of the Chemical Structure Association, the 2006 ACS Award for Computers in Chemical and Pharmaceutical Research for his outstanding achievements in research and education in the field of Chemoinformatics, and the 1997 Herman Skolnik Award of the Division of Chemical Information of the American Chemical Society. Thomas Engel is a specialist in chemoinformatics who studied chemistry and education at the University of Würzburg and spent a significant tenure at the CCC at the University of Erlangen-Nürnberg, followed by the Chemical Computing Group AG in Cologne and is presently at the Ludwig- Maximilians-Universität, Munich. As editors, they have brought together a wide range of experts and topics, which will inform, educate, and motivate the reader to delve deeper into the subject of chemoinformatics. The new edition provides both the foundations for chemoinformatics and also a range of developing topics of active research, providing the reader with an introduction to the subject as well as advanced topics and future xxiv Foreword

directions. This new edition is complemented by the Handbook of Chemoinfor- matics: From Data to Knowledge (by the same editors). It belongs on the bookshelf of students and experts alike, all who have an interest in the ﬁeld of chemoinformatics, and especially those who see “magic” in chemistry.

Robert C. Glen Professor of Molecular Sciences Informatics Director of the Centre for Molecular Informatics Department of Chemistry University of Cambridge Cambridge United Kingdom xxv

List of Contributors

Ivan Bangov Alexander Golbraikh Konstantin Preslavski Shumen University of North Carolina University Eshelman School of Pharmacy, Natural Sciences Faculty, Department Laboratory for Molecular Modeling, of General Chemistry Division of Chemical Biology and 115 Universitetska Street Medicinal Chemistry 9712 Shumen Chapel Hill, NC, 27599 Bulgaria USA Email: [email protected] Nikolay Kochev Tim Clark University of Plovdiv Friedrich-Alexander-University Faculty of Chemistry, Department of Erlangen-Nuremberg Analytical Chemistry and Computer Computer-Chemie-Centrum and Chemistry Interdisciplinary Center for Molecular 24, Tzar Assen Street Materials 4000 Plovdiv Nägelsbachstrasse 25 Bulgaria 91052 Erlangen Email: [email protected] Germany Adrian Kolodzik University of Hamburg Thomas Engel ZBH – Center for Bioinformatics Ludwig-Maximilians-Universität Bundesstraße 32 München 20146 Hamburg Department Chemie Germany Butenandtstraße 5-13, Haus F Email: [email protected] 81377 Munich Germany Harald Lanig Friedrich-Alexander-University Johann Gasteiger Erlangen-Nuremberg Friedrich-Alexander-Universität Central Institute for Scientiﬁc Erlangen-Nürnberg Computing (ZISC) Computer-Chemie-Centrum Martensstrasse 5a Nägelsbachstrasse 25 91058 Erlangen 91052 Erlangen Germany Germany Email: [email protected] xxvi List of Contributors

Giorgi Lekishvili Heinrich Sticht Tbilisi State Medical University Friedrich-Alexander-University Faculty of Pharmacy, Department of Erlangen-Nuremberg Medical Chemistry Institut für Biochemie, Emil-Fischer 33, Vazha-Pshavela avenue Centrum 0186 Tbilisi Fahrstraße 17 Georgia 91054 Erlangen Germany Valentin Monev Bulgarian Academy of Sciences Lothar Terﬂoth Institute of Organic Chemistry Insilico Biotechnology AG Acad. Georgi Bonchev Street, Meitnerstrasse 9 Soﬁa 1113 70563 Stuttgart Bulgaria Germany Email: [email protected] Jarosław Tomczak Matthias Rarey Informatics Unlimited Ltd University of Hamburg 8 Station Road, Histon ZBH – Center for Bioinformatics Cambridge CB24 9LQ Bundesstraße 32 UK 20146 Hamburg Germany Alexander Tropsha Email: [email protected] University of North Carolina Eshelman School of Pharmacy, Oliver Sacher Laboratory for Molecular Modeling, Molecular Networks GmbH Division of Chemical Biology and Neumeyerstraße 28 Medicinal Chemistry 90411 Nürnberg Chapel Hill, NC, 27599 Germany USA E-mail: [email protected]. Christof Schwab Molecular Networks GmbH Kurt Varmuza Neumeyerstraße 28 Vienna University of Technology, 90411 Nürnberg Vienna, Austria Germany Institute of Statistics and Mathematical Methods in Economics, Joao Aires de Sousa and Institute of Chemical Engineering Universidade Nova de Lisboa Wiedner Hauptstrasse 7 Faculdade de Ciencias e Tecnologia, 1040 Vienna Departamento de Quimica Austria 2829-516 Caparica [email protected]; Portugal www.lcm.tuwien.ac.at/vk/ www: http://joao.airesdesousa.com List of Contributors xxvii

David A. Winkler Engelbert Zass Latrobe University ETH Zurich (retired) Latrobe Institute for Molecular Laboratory of Organic Chemistry Science Zürich 3082 Bundoora Switzerland Australia Jure Zupan and National Institute of Chemistry Laboratory of Chemometrics, Monash University Hajdrihova 19 Monash Institute of Pharmaceutical SI-1000, Ljubljana Sciences Slovenia Parkville 3052 E-mail: [email protected] Australia [email protected]