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Chemoinformatics


Chemoinformatics

Basic Concepts and Methods
1. Aufl.

von: Thomas Engel, Johann Gasteiger

79,99 €

Verlag: Wiley-VCH
Format: PDF
Veröffentl.: 18.05.2018
ISBN/EAN: 9783527693771
Sprache: englisch
Anzahl Seiten: 600

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Beschreibungen

This essential guide to the knowledge and tools in the field includes everything from the basic concepts to modern methods, while also forming a bridge to bioinformatics.The textbook offers a very clear and didactical structure, starting from the basics and the theory, before going on to provide an overview of the methods. Learning is now even easier thanks to exercises at the end of each section or chapter. Software tools are explained in detail, so that the students not only learn the necessary theoretical background, but also how to use the different software packages available. The wide range of applications is presented in the corresponding book Applied Chemoinformatics - Achievements and Future Opportunities (ISBN 9783527342013). For Master and PhD students in chemistry, biochemistry and computer science, as well as providing an excellent introduction for other newcomers to the field.
Foreword xxi List of Contributors xxv 1 Introduction 1Thomas 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 The Scope of the Book 7 1.6 Teaching Chemoinformatics 8 References 8 2 Principles of Molecular Representations 9Thomas 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 Speci?c Types of Chemical Structures 25 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 Con?gurational Isomers 32 2.6.2 Chirality 33 2.6.3 3D Coordinate Systems 36 2.7 Molecular Surfaces 37 Selected Reading 38 References 393 3 Computer Processing of Chemical Structure Information 43Thomas 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 Identi?er (InChI) and InChIKey 48 3.2.5 XYZ Format 50 3.2.6 Z-Matrix 51 3.2.7 The Mol?le Format Family 52 3.2.7.1 Structure of a Mol?le 53 3.2.7.2 Stereochemistry in the Mol?le 57 3.2.7.3 Structure of an SD?le 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 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 Speci?cation of Chirality 86 3.5.1.1 Detection of Chirality 87 3.5.1.2 Speci?cation 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 Work?ow Environments 109 Selected Reading 111 References 111 4 Representation of Chemical Reactions 121Oliver Sacher and Johann Gasteiger 4.1 Introduction 121 4.2 Reaction Equation 122 4.3 Reaction Types 123 4.4 Reaction Center and Reaction Mechanisms 125 4.5 Chemical Reactivity 126 4.5.1 Physicochemical E?ects 126 4.5.1.1 Charge Distribution 126 4.5.1.2 Inductive E?ect 127 4.5.1.3 Resonance E?ect 127 4.5.1.4 Polarizability E?ect 128 4.5.1.5 Steric E?ect 128 4.5.1.6 Stereoelectronic E?ects 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 Identi?er for Reactions (RinChI) 135 4.7.3 Input and Output of Reactions 135 4.8 Reaction Center Perception 138 4.9 Reaction Classi?cation 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 Classi?cation 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 5 The Data 155 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 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 185Engelbert Zass and Thomas Engel 6.1 Introduction 185 6.2 Chemical Literature and Databases 186 6.2.1 Classi?cation 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 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 231Nikolay 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 Speci?c 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 De?nition 255 7.6.2 Dynamic Programming Algorithm 256 7.6.3 Fast Sequence Searching in Large Databases 258 7.7 Summary 259 Selected Reading 261 References 262 8 Computational Chemistry 267 8.1 Empirical Approaches to the Calculation of Properties 269Johann 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 E?ect 275 Selected Reading 277 References 277 8.2 Molecular Mechanics 279Harald 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 301Harald 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 E?ects 305 8.3.3.4 Periodic Boundary Conditions 308 8.3.4 Constant Energy, Temperature, or Pressure? 308 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 320Tim 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 Largen 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) 345Johann Gasteiger 10 Calculation of Structure Descriptors 349Lothar Ter?oth and Johann Gasteiger 10.1 Introduction 349 10.1.1 QSPR/QSAR Modeling 349 10.1.2 Overview 349 10.1.3 Classi?cation of Compounds and Similarity Searching 350 10.1.4 De?nition of the Terms “Structure Descriptor” and “Molecular Descriptor” 351 10.1.5 Classi?cation of Structure Descriptors 351 10.1.6 Structure Descriptors with a Fixed Length 351 10.2 Structure Descriptors for Classi?cation 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 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 Di?raction 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 Selected Reading 390 References 390 11 Data Analysis and Data Handling (QSPR/QSAR) 397 11.1 Methods for Multivariate Data Analysis 399Kurt 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 Classi?cation 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 Classi?cation: 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 11.1.7.4 k-Nearest Neighbor (KNN) Classi?cation 430 11.1.7.5 Support Vector Machine (SVM) 431 11.1.7.6 Classi?cation Trees (CART) 432 11.1.7.7 Example: Classi?cation of Meteorite Samples Using Mass Spectral Data 432 Acknowledgements 434 Selected Reading 435 References 435 11.2 Arti?cial Neural Networks (ANNs) 438Jure Zupan 11.2.1 How to Learn a New Method? 438 11.2.2 Multivariate Representation of Data 439 11.2.3 Overview of Arti?cial 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 453David A. Winkler 11.3.1 Drug Design in the Era of Big Data and Arti?cial 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 465Alexander 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 Work?ow 468 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 497Heinrich 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 525Johann Gasteiger 14.1 Access to Chemical Information 525 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
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-Universitat in Munich, Germany. He received his academic degrees at the University of Wurzburg. 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).

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