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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: 608

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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.<br />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.
<p>Foreword xxi</p> <p>List of Contributors xxv</p> <p><b>1 Introduction 1</b><i><br />Thomas Engel and Johann Gasteiger</i></p> <p>1.1 The Rationale for the Books 1</p> <p>1.2 The Objectives of Chemoinformatics 2</p> <p>1.3 Learning in Chemoinformatics 4</p> <p>1.4 Outline of the Book 5</p> <p>1.5 The Scope of the Book 7</p> <p>1.6 Teaching Chemoinformatics 8</p> <p>References 8</p> <p><b>2 Principles of Molecular Representations 9<br /></b><i>Thomas Engel</i></p> <p>2.1 Introduction 9</p> <p>2.2 Chemical Nomenclature 11</p> <p>2.2.1 Non-systematic Nomenclature (Trivial Names) 11</p> <p>2.2.2 Systematic Nomenclature of Chemical Compounds 12</p> <p>2.2.3 Drawbacks of Chemical Nomenclature for Data Processing 12</p> <p>2.3 Chemical Notations 12</p> <p>2.3.1 Empirical Formulas of Inorganic and Organic Compounds 12</p> <p>2.3.2 Line Notations 14</p> <p>2.4 Mathematical Notations 14</p> <p>2.4.1 Introduction into Graph Theory 15</p> <p>2.4.2 Matrix Representations 18</p> <p>2.4.2.1 Adjacency Matrix 18</p> <p>2.4.2.2 Incidence Matrix 19</p> <p>2.4.2.3 Distance Matrix 20</p> <p>2.4.2.4 Bond Matrix 21</p> <p>2.4.2.5 Bond–Electron Matrix 21</p> <p>2.4.2.6 Summary on Matrix Representations 23</p> <p>2.4.3 Connection Table 23</p> <p>2.5 Specific Types of Chemical Structures 25</p> <p>2.5.1 General Concepts of Isomerism 25</p> <p>2.5.2 Tautomerism 26</p> <p>2.5.3 Markush Structures 27</p> <p>2.5.4 Beyond a Connection Table Representation 28</p> <p>2.5.4.1 Representation of Molecular Structures by Electron Systems 28</p> <p>2.6 Spatial Representation of Structures 31</p> <p>2.6.1 Representation of Configurational Isomers 32</p> <p>2.6.2 Chirality 33</p> <p>2.6.3 3D Coordinate Systems 36</p> <p>2.7 Molecular Surfaces 37</p> <p>Selected Reading 38</p> <p>References 393</p> <p><b>3 Computer Processing of Chemical Structure Information 43<br /></b><i>Thomas Engel</i></p> <p>3.1 Introduction 43</p> <p>3.2 Standard File Formats for Chemical Structure Information 44</p> <p>3.2.1 SMILES 44</p> <p>3.2.1.1 Stereochemistry in SMILES 47</p> <p>3.2.1.2 Summary on SMILES 47</p> <p>3.2.2 SMARTS 47</p> <p>3.2.3 SYBYL Line Notation 48</p> <p>3.2.4 The International Chemical Identifier (InChI) and InChIKey 48</p> <p>3.2.5 XYZ Format 50</p> <p>3.2.6 Z-Matrix 51</p> <p>3.2.7 The Molfile Format Family 52</p> <p>3.2.7.1 Structure of a Molfile 53</p> <p>3.2.7.2 Stereochemistry in the Molfile 57</p> <p>3.2.7.3 Structure of an SDfile 57</p> <p>3.2.8 The PDB File Format 58</p> <p>3.2.8.1 Introduction/History 58</p> <p>3.2.8.2 General Description 58</p> <p>3.2.8.3 Analysis of a Sample PDB File 60</p> <p>3.2.9 Metadata Formats 65</p> <p>3.2.9.1 STAR-Based File Formats and Dictionaries 65</p> <p>3.2.9.2 CIF File Format 66</p> <p>3.2.9.3 mmCIF File Format 67</p> <p>3.2.9.4 CML 68</p> <p>3.2.9.5 CSRML 68</p> <p>3.2.10 Libraries for Handling Information in Structure File Formats 69</p> <p>3.3 Input and Output of Chemical Structures 70</p> <p>3.3.1 Molecule Editors 72</p> <p>3.3.2 Molecule Viewers 73</p> <p>3.4 Processing Constitutional Information 73</p> <p>3.4.1 Structure Isomers and Isomorphism 73</p> <p>3.4.2 Tautomerism 74</p> <p>3.4.3 Unambiguous and Biunique Representation by Canonicalization 76</p> <p>3.4.3.1 The Morgan Algorithm 77</p> <p>3.4.4 Ring Perception 79</p> <p>3.4.4.1 Introduction 79</p> <p>3.4.4.2 Graph Terminology 80</p> <p>3.4.4.3 Ring Perception Strategies 81</p> <p>3.5 Processing 3D Structure Information 86</p> <p>3.5.1 Detection and Specification of Chirality 86</p> <p>3.5.1.1 Detection of Chirality 87</p> <p>3.5.1.2 Specification of Chirality 87</p> <p>3.5.2 Automatic Generation of 3D Structures 90</p> <p>3.5.3 Automatic Generation of Ensemble of Conformations 94</p> <p>3.6 Visualization of Molecular Models 100</p> <p>3.6.1 Introduction 100</p> <p>3.6.2 Models of the 3D Structure 101</p> <p>3.6.2.1 Wire Frame and Capped Sticks Model 101</p> <p>3.6.2.2 Ball-and-Stick Model 101</p> <p>3.6.2.3 Space-Filling Model 102</p> <p>3.6.2.4 Crystallographic Models 102</p> <p>3.6.3 Models of Biological Macromolecules 102</p> <p>3.6.4 Virtual Reality 103</p> <p>3.6.5 3D Printing 103</p> <p>3.7 Calculation of Molecular Surfaces 103</p> <p>3.7.1 Van der Waals Surface 104</p> <p>3.7.2 Connolly Surface 104</p> <p>3.7.3 Solvent-Accessible Surface 105</p> <p>3.7.4 Enzyme Cavity Surface (Union Surface) 106</p> <p>3.7.5 Isovalue-Based Electron Density Surface 106</p> <p>3.7.6 Experimentally Determined Surfaces 106</p> <p>3.7.7 Visualization of Molecular Surface Properties 107</p> <p>3.7.8 Property-based Isosurfaces 107</p> <p>3.7.8.1 Electrostatic Potentials 108</p> <p>3.7.8.2 Hydrogen Bonding Potential 108</p> <p>3.7.8.3 Polarizability and Hydrophobicity Potential 108</p> <p>3.7.8.4 Spin Density 108</p> <p>3.7.8.5 Vector Fields 108</p> <p>3.7.8.6 Volumetric Properties 108</p> <p>3.8 Chemoinformatic Toolkits and Workflow Environments 109</p> <p>Selected Reading 111</p> <p>References 111</p> <p><b>4 Representation of Chemical Reactions 121<br /></b><i>Oliver Sacher and Johann Gasteiger</i></p> <p>4.1 Introduction 121</p> <p>4.2 Reaction Equation 122</p> <p>4.3 Reaction Types 123</p> <p>4.4 Reaction Center and Reaction Mechanisms 125</p> <p>4.5 Chemical Reactivity 126</p> <p>4.5.1 Physicochemical Effects 126</p> <p>4.5.1.1 Charge Distribution 126</p> <p>4.5.1.2 Inductive Effect 127</p> <p>4.5.1.3 Resonance Effect 127</p> <p>4.5.1.4 Polarizability Effect 128</p> <p>4.5.1.5 Steric Effect 128</p> <p>4.5.1.6 Stereoelectronic Effects 128</p> <p>4.5.2 Simple Methods for Quantifying Chemical Reactivity 128</p> <p>4.5.2.1 Frontier Molecular Orbital Theory 128</p> <p>4.5.2.2 Linear Free Energy Relationships 130</p> <p>4.6 Learning from Reaction Information 132</p> <p>4.7 Building of Reaction Databases 133</p> <p>4.7.1 Contents 133</p> <p>4.7.2 Reaction Data Exchange Formats 134</p> <p>4.7.2.1 RXN/RDF format by MDL/Symyx 134</p> <p>4.7.2.2 Reaction SMILES/SMIRKS by Daylight Chemical Information Systems 134</p> <p>4.7.2.3 Chemical Markup Language 135</p> <p>4.7.2.4 International Chemical Identifier for Reactions (RinChI) 135</p> <p>4.7.3 Input and Output of Reactions 135</p> <p>4.8 Reaction Center Perception 138</p> <p>4.9 Reaction Classification 139</p> <p>4.9.1 Model-Driven Approaches 139</p> <p>4.9.1.1 Ugi’s Scheme and Some Follow-Ups 140</p> <p>4.9.1.2 InfoChem’s Reaction Classification 143</p> <p>4.9.2 Data-Driven Approaches 145</p> <p>4.9.2.1 HORACE 145</p> <p>4.9.2.2 Reaction Landscapes 146</p> <p>4.10 Stereochemistry of Reactions 148</p> <p>4.11 Reaction Networks 149</p> <p>Selected Reading 151</p> <p>References 152</p> <p><b>5 The Data 155</b></p> <p>5.1 Introduction 155</p> <p>5.2 Data Types 156</p> <p>5.2.1 Numerical Data 157</p> <p>5.2.2 Molecular Structures 159</p> <p>5.2.3 Bit Vectors 160</p> <p>5.2.3.1 Hash Codes 160</p> <p>5.2.3.2 Structural Keys 162</p> <p>5.2.3.3 Fingerprints 163</p> <p>5.2.4 Chemical Reactions 164</p> <p>5.2.5 Molecular Spectra 165</p> <p>5.3 Storage and Manipulation of Data 169</p> <p>5.3.1 Experimental Data 169</p> <p>5.3.1.1 Types of Data on Properties 170</p> <p>5.3.1.2 Accuracy of the Data 170</p> <p>5.3.2 Data Storage and Exchange 171</p> <p>5.3.2.1 DAT File 171</p> <p>5.3.2.2 JCAMP-DX 171</p> <p>5.3.2.3 Predictive Model Markup Language (PMML) 172</p> <p>5.3.3 Real-World Data 173</p> <p>5.3.3.1 Data Complexity 173</p> <p>5.3.3.2 Outliers and Redundant Objects 174</p> <p>5.3.4 Data Transformation 175</p> <p>5.3.4.1 Fast Fourier Transformation 175</p> <p>5.3.4.2 Wavelet Transformation 175</p> <p>5.3.5 Preparation of Datasets for Building of Models and Validations of Their Quality 176</p> <p>5.4 Conclusions 177</p> <p>Selected Reading 178</p> <p>References 179</p> <p><b>6 Databases and Data Sources in Chemistry 185</b><i><br />Engelbert Zass and Thomas Engel</i></p> <p>6.1 Introduction 185</p> <p>6.2 Chemical Literature and Databases 186</p> <p>6.2.1 Classification of Chemical Literature 186</p> <p>6.2.2 The Origin of Chemical Databases 187</p> <p>6.2.3 Evolution of Database Systems and User Interfaces 187</p> <p>6.3 Major Chemical Database Systems 188</p> <p>6.3.1 SciFinder 188</p> <p>6.3.2 Reaxys 189</p> <p>6.3.3 SciFinder versus Reaxys 190</p> <p>6.4 Compound Databases 191</p> <p>6.4.1 2D Structures 191</p> <p>6.4.1.1 Searching Organic Compounds 192</p> <p>6.4.1.2 Searching Inorganic and Coordination Compounds 194</p> <p>6.4.2 Sequences of Biopolymers 195</p> <p>6.4.3 3D Structures 198</p> <p>6.4.4 Catalog Databases 200</p> <p>6.5 Databases with Properties of Compounds 200</p> <p>6.5.1 Physical Properties 201</p> <p>6.5.2 Thermodynamic and Thermochemical Data 202</p> <p>6.5.3 Spectra 204</p> <p>6.5.3.1 Spectroscopic Databases 205</p> <p>6.5.3.2 Compound Databases with Spectroscopic Information 205</p> <p>6.5.4 Biological, Environmental, and Safety Information Sources 206</p> <p>6.5.4.1 Biological Information 207</p> <p>6.5.4.2 Pharmaceutical and Medical Information 208</p> <p>6.5.4.3 Toxicity, Environmental, and Safety Information 209</p> <p>6.6 Reaction Databases 210</p> <p>6.6.1 Comprehensive Reaction Databases 210</p> <p>6.6.2 Synthetic Methodology Databases 212</p> <p>6.7 Bibliographic and Citation Databases 212</p> <p>6.7.1 Bibliographic Databases 213</p> <p>6.7.1.1 Special Bibliographic Databases 213</p> <p>6.7.1.2 Patent Bibliographic Databases 214</p> <p>6.7.1.3 Searching Bibliographic Databases 216</p> <p>6.7.1.4 Linking to Full Text 216</p> <p>6.7.2 Citation Databases 217</p> <p>6.7.2.1 General Citation Databases 218</p> <p>6.7.2.2 Patent Citation Databases 219</p> <p>6.8 Full-Text Databases 219</p> <p>6.8.1 Electronic Journals 219</p> <p>6.8.2 Patents 220</p> <p>6.8.3 Lexika and Encyclopedias 221</p> <p>6.9 Architecture of a Structure-Searchable Database 222</p> <p>Selected Reading 224</p> <p>References 224</p> <p><b>7 Searching Chemical Structures 231</b><i><br />Nikolay Kochev, Valentin Monev, and Ivan Bangov</i></p> <p>7.1 Introduction 231</p> <p>7.2 Full Structure Search 232</p> <p>7.3 Substructure Search 235</p> <p>7.3.1 Basic Concepts 235</p> <p>7.3.2 Backtracking Algorithm 236</p> <p>7.3.3 Optimization of the Backtracking Algorithm 238</p> <p>7.3.4 Screening 239</p> <p>7.3.5 Superstructure Searching 241</p> <p>7.3.6 Automorphism Searching 241</p> <p>7.3.7 Maximum Common Substructure Searching 242</p> <p>7.3.8 Specific Line Notations for Substructure Searching 243</p> <p>7.3.9 Chemotypes for Database Searching 244</p> <p>7.4 Similarity Search 245</p> <p>7.4.1 Similarity Basics 245</p> <p>7.4.2 Similarity Measures 247</p> <p>7.4.3 Descriptor Selection and Coding 249</p> <p>7.4.4 Similarity Measures Based on Maximum Common Substructure 250</p> <p>7.5 Three-Dimensional Structure Search Methods 250</p> <p>7.5.1 Pharmacophore Searching 251</p> <p>7.5.2 3D Similarity Searching 252</p> <p>7.6 Sequence Searching in Protein and Nucleic Acid Databases 254</p> <p>7.6.1 Sequence Similarity Definition 255</p> <p>7.6.2 Dynamic Programming Algorithm 256</p> <p>7.6.3 Fast Sequence Searching in Large Databases 258</p> <p>7.7 Summary 259</p> <p>Selected Reading 261</p> <p>References 262</p> <p><b>8 Computational Chemistry 267</b></p> <p><b>8.1 Empirical Approaches to the Calculation of Properties 269</b><i><br />Johann Gasteiger</i></p> <p>8.1.1 Introduction 269</p> <p>8.1.2 Additivity of Atomic Contributions 269</p> <p>8.1.3 Attenuation Models 271</p> <p>8.1.3.1 Calculation of Charge Distribution 271</p> <p>8.1.3.2 Polarizability Effect 275</p> <p>Selected Reading 277</p> <p>References 277</p> <p><b>8.2 Molecular Mechanics 279<br /></b><i>Harald Lanig</i></p> <p>8.2.1 Introduction 279</p> <p>8.2.2 No Force Field Calculation without Atom Types 280</p> <p>8.2.3 The Functional Form of Common Force Fields 281</p> <p>8.2.3.1 Bond Stretching 282</p> <p>8.2.3.2 Angle Bending 283</p> <p>8.2.3.3 Torsional Terms 284</p> <p>8.2.3.4 Out-of-Plane Bending 285</p> <p>8.2.3.5 Electrostatic Interactions 286</p> <p>8.2.3.6 Van der Waals Interactions 287</p> <p>8.2.3.7 Cross Terms 289</p> <p>8.2.3.8 Advanced Interatomic Potentials and Future Development 290</p> <p>8.2.4 Available Force Fields 291</p> <p>8.2.4.1 Force Fields for Small Molecules 292</p> <p>8.2.4.2 Force Fields for Biomolecules 293</p> <p>Selected Readings 296</p> <p>References 296</p> <p><b>8.3 Molecular Dynamics 301<br /></b><i>Harald Lanig</i></p> <p>8.3.1 Introduction 301</p> <p>8.3.2 The Continuous Movement of Molecules 302</p> <p>8.3.3 Methods 302</p> <p>8.3.3.1 Algorithms 303</p> <p>8.3.3.2 Ways for Speeding up the Calculations 304</p> <p>8.3.3.3 Solvent Effects 305</p> <p>8.3.3.4 Periodic Boundary Conditions 308</p> <p>8.3.4 Constant Energy, Temperature, or Pressure? 308</p> <p>8.3.5 Long-Range Forces 310</p> <p>8.3.6 Application of Molecular Dynamics Techniques 311</p> <p>8.3.7 Future Perspectives 315</p> <p>Selected Readings 317</p> <p>References 317</p> <p><b>8.4 Quantum Mechanics 320</b><i><br />Tim Clark</i></p> <p>8.4.1 Hückel Molecular Orbital Theory 320</p> <p>8.4.2 Semiempirical MO Theory 324</p> <p>8.4.3 Ab Initio Molecular Orbital Theory 327</p> <p>8.4.4 Density Functional Theory 332</p> <p>8.4.5 Properties from Quantum Mechanical Calculations 334</p> <p>8.4.5.1 Net Atomic Charges 334</p> <p>8.4.5.2 Dipole and Higher Multipole Moments 335</p> <p>8.4.5.3 Polarizabilities 335</p> <p>8.4.5.4 Orbital Energies 336</p> <p>8.4.5.5 Surface Descriptors 336</p> <p>8.4.5.6 Local Ionization Potential 336</p> <p>8.4.6 Quantum Mechanical Techniques for Very Largen Molecules 337</p> <p>8.4.6.1 Linear Scaling Methods 337</p> <p>8.4.6.2 Hybrid QM/MM Calculations 338</p> <p>8.4.7 The Future of Quantum Mechanical Methods in Chemoinformatics 338</p> <p>Selected Reading 340</p> <p>References 341</p> <p><b>9 Modeling and Prediction of Properties (QSPR/QSAR) 345<br /></b><i>Johann Gasteiger</i></p> <p><b>10 Calculation of Structure Descriptors 349<br /></b><i>Lothar Terfloth and Johann Gasteiger</i></p> <p>10.1 Introduction 349</p> <p>10.1.1 QSPR/QSAR Modeling 349</p> <p>10.1.2 Overview 349</p> <p>10.1.3 Classification of Compounds and Similarity Searching 350</p> <p>10.1.4 Definition of the Terms “Structure Descriptor” and “Molecular Descriptor” 351</p> <p>10.1.5 Classification of Structure Descriptors 351</p> <p>10.1.6 Structure Descriptors with a Fixed Length 351</p> <p>10.2 Structure Descriptors for Classification and Similarity Searching 352</p> <p>10.2.1 2D Structure Descriptors (Topological Descriptors) 352</p> <p>10.2.1.1 Structural Keys 352</p> <p>10.2.1.2 Fingerprints 353</p> <p>10.2.1.3 Distance and Similarity Measures 354</p> <p>10.2.1.4 Chemotypes: Data Mining for Compounds with Structural Features 356</p> <p>10.2.1.5 Multilevel Neighborhoods of Atoms 358</p> <p>10.2.1.6 Descriptors from Shannon Entropy Calculations 359</p> <p>10.2.1.7 Chemically Advanced Template Search (CATS2D) Descriptors 360</p> <p>10.2.1.8 Descriptors from Chemical Bond Information 360</p> <p>10.2.2 3D Descriptors 361</p> <p>10.2.2.1 Geometric Atom Pair Descriptors 361</p> <p>10.2.2.2 CATS3D and CHARGE3D 361</p> <p>10.2.2.3 Pharmacophores 362</p> <p>10.2.3 Field-Based Molecular Similarity 362</p> <p>10.2.3.1 Electron Density 362</p> <p>10.2.3.2 General Field-Based Similarity Indices 363</p> <p>10.3 Structure Descriptors for Quantitative Modeling 363</p> <p>10.3.1 0-D Molecular Descriptors 363</p> <p>10.3.2 1D Molecular Descriptors 363</p> <p>10.3.3 2D Molecular Descriptors (Topological Descriptors) 365</p> <p>10.3.3.1 Single-Valued Descriptors 365</p> <p>10.3.3.2 Topological Descriptors as Vectors 366</p> <p>10.3.4 3D Descriptors 369</p> <p>10.3.4.1 3D Structure Generation 369</p> <p>10.3.4.2 3D Autocorrelation Vector 370</p> <p>10.3.4.3 3D Molecule Representation of Structures Based on Electron Diffraction Code (3D MoRSE Code) 370</p> <p>10.3.4.4 Radial Distribution Function Code 371</p> <p>10.3.4.5 Other 3D Descriptors 375</p> <p>10.3.5 Chirality Descriptors 375</p> <p>10.3.5.1 Chirality Codes 376</p> <p>10.3.5.2 Conformation-Independent Chirality Code (CICC) 376</p> <p>10.3.5.3 Conformation-Dependent Chirality Code (CDCC) 377</p> <p>10.3.5.4 Descriptors of Molecular Shape and Molecular Surfaces 377</p> <p>10.3.5.5 Global Shape Descriptors 378</p> <p>10.3.5.6 Autocorrelation of Molecular Surface Properties 378</p> <p>10.3.5.7 2D Maps of Molecular Surfaces 379</p> <p>10.3.5.8 Charged Partial Surface Area 382</p> <p>10.3.6 Field-Based Methods 383</p> <p>10.3.6.1 Comparative Molecular Field Analysis (CoMFA) 383</p> <p>10.3.6.2 Comparative Molecular Similarity Analysis (CoMSIA) 384</p> <p>10.3.6.3 3D Molecular Interaction Fields 384</p> <p>10.3.7 Descriptors for an Ensemble of Conformations (4D Descriptors) 384</p> <p>10.3.7.1 4D-QSAR 384</p> <p>10.3.8 Quantum Chemical Descriptors 385</p> <p>10.4 Descriptors That Are Not Calculated from the Chemical Structure 385</p> <p>10.5 Summary and Outlook 387</p> <p>Selected Reading 390</p> <p>References 390</p> <p><b>11 Data Analysis and Data Handling (QSPR/QSAR) 397</b></p> <p><b>11.1 Methods for Multivariate Data Analysis 399<br /></b><i>Kurt Varmuza</i></p> <p>11.1.1 Introduction into Multivariate Data Analysis 399</p> <p>11.1.1.1 Aims 399</p> <p>11.1.1.2 Notation and Symbols 400</p> <p>11.1.2 Basics of Statistical Data Evaluation 401</p> <p>11.1.2.1 Data Distribution, Central Value, and Spread 401</p> <p>11.1.2.2 Correlation 404</p> <p>11.1.2.3 Discrimination 405</p> <p>11.1.3 Multivariate Data 406</p> <p>11.1.3.1 Overview 406</p> <p>11.1.3.2 Preprocessing 407</p> <p>11.1.3.3 Distances and Similarities 408</p> <p>11.1.3.4 Linear Latent Variables 410</p> <p>11.1.4 Evaluation of Empirical Models 412</p> <p>11.1.4.1 Overview 412</p> <p>11.1.4.2 Optimum Model Complexity 412</p> <p>11.1.4.3 Performance Criteria for Calibration Models 413</p> <p>11.1.4.4 Performance Criteria for Classification Models 414</p> <p>11.1.4.5 Cross-Validation 415</p> <p>11.1.4.6 Bootstrap 416</p> <p>11.1.5 Exploration: Analyzing the Independent Variables 417</p> <p>11.1.5.1 Overview 417</p> <p>11.1.5.2 Principal Component Analysis (PCA) 417</p> <p>11.1.5.3 Nonlinear Mapping 419</p> <p>11.1.5.4 Cluster Analysis 419</p> <p>11.1.5.5 Example: Exploratory Data Analysis of Mass Spectra from Meteorite Samples 421</p> <p>11.1.6 Calibration: Building a Quantitative Model 423</p> <p>11.1.6.1 Overview 423</p> <p>11.1.6.2 Ordinary Least Squares (OLS) Regression 424</p> <p>11.1.6.3 Principal Component Regression (PCR) 424</p> <p>11.1.6.4 Partial Least Squares (PLS) Regression 425</p> <p>11.1.6.5 Variable Selection 426</p> <p>11.1.6.6 Example: Prediction of Gas Chromatographic Retention Indices for Polycyclic Aromatic Hydrocarbons 427</p> <p>11.1.7 Classification: Discriminating Samples 428</p> <p>11.1.7.1 Overview 428</p> <p>11.1.7.2 Linear Discriminant Analysis (LDA) 430</p> <p>11.1.7.3 Discriminant Partial Least Squares (D-PLS) Analysis 430</p> <p>11.1.7.4 k-Nearest Neighbor (KNN) Classification 430</p> <p>11.1.7.5 Support Vector Machine (SVM) 431</p> <p>11.1.7.6 Classification Trees (CART) 432</p> <p>11.1.7.7 Example: Classification of Meteorite Samples Using Mass Spectral Data 432</p> <p>Acknowledgements 434</p> <p>Selected Reading 435</p> <p>References 435</p> <p><b>11.2 Artificial Neural Networks (ANNs) 438<br /></b><i>Jure Zupan</i></p> <p>11.2.1 How to Learn a New Method? 438</p> <p>11.2.2 Multivariate Representation of Data 439</p> <p>11.2.3 Overview of Artificial Neural Networks (ANNs) 442</p> <p>11.2.4 Error Back-Propagation ANNs 443</p> <p>11.2.5 Kohonen and Counter-Propagation ANN 445</p> <p>11.2.6 Training of the ANN: Adapting the Weights 448</p> <p>11.2.7 Controlling Model Complexity and Optimizing Predictivity 450</p> <p>11.2.8 Few General Remarks about ANNs 450</p> <p>Selected Reading 451</p> <p>References 451</p> <p><b>11.3 Deep and Shallow Neural Networks 453<br /></b><i>David A. Winkler</i></p> <p>11.3.1 Drug Design in the Era of Big Data and Artificial Intelligence (AI) 453</p> <p>11.3.2 Deep Learning 454</p> <p>11.3.3 Controlling Model Complexity and Optimizing Predictivity Using Regularization 455</p> <p>11.3.4 Universal Approximation Theorem 458</p> <p>11.3.5 Do QSAR Models Generated by Neural Networks Meet the Requirements of the Universal Approximation Theorem? 458</p> <p>11.3.6 Comparison of the Performance of Deep and Shallow Regularized Neural Networks on Drug Datasets 459</p> <p>11.3.7 A Few General Remarks about Neural Networks for Drug Discovery 460</p> <p>Selected Reading 462</p> <p>References 462</p> <p><b>12 QSAR/QSPR Revisited 465<br /></b><i>Alexander Golbraikh and Alexander Tropsha</i></p> <p>12.1 Best Practices of QSAR Modeling 466</p> <p>12.1.1 Introduction 466</p> <p>12.1.2 Key Concepts 467</p> <p>12.1.3 Predictive QSAR Modeling Workflow 468</p> <p>12.1.4 Dataset Curation 469</p> <p>12.1.5 Modelability Studies 470</p> <p>12.1.6 Development of QSAR Models: Internal and External Validation 471</p> <p>12.1.7 Prediction Accuracy Criteria for QSAR Models for a Continuous Response Variable 472</p> <p>12.1.8 Prediction Accuracy Criteria for Category QSAR Models 473</p> <p>12.1.9 Time-Split Validation 475</p> <p>12.1.10 Validation by Y-Randomization 475</p> <p>12.1.11 Applicability Domain of QSAR Models 475</p> <p>12.1.11.1 Leverage AD for Regression QSAR Models 476</p> <p>12.1.11.2 Residual Standard Deviation (RSD) as AD 476</p> <p>12.1.11.3 Other widely Used ADs 476</p> <p>12.1.12 Ensemble Modeling 478</p> <p>12.1.13 Model Interpretation: Structural Alerts 478</p> <p>12.1.14 Virtual Screening 479</p> <p>12.1.15 Conclusions 480</p> <p>12.2 The Data Science of QSAR Modeling 480</p> <p>12.2.1 Introduction 480</p> <p>12.2.2 Data Curation: Trust but Verify! 482</p> <p>12.2.3 Models as Decision Support Tools 487</p> <p>12.2.4 Conclusions 487</p> <p>Selected Reading 489</p> <p>References 489</p> <p><b>13 Bioinformatics 497<br /></b><i>Heinrich Sticht</i></p> <p>13.1 Introduction 497</p> <p>13.2 Sequence Databases 499</p> <p>13.2.1 GenBank 499</p> <p>13.2.2 UniProt 501</p> <p>13.3 Searching Sequence Databases 502</p> <p>13.3.1 Tools for Sequence Database Searches 503</p> <p>13.3.2 Scoring Matrices 503</p> <p>13.3.3 Interpretation of the Results of a Database Search 507</p> <p>13.4 Characterization of Protein Families 509</p> <p>13.4.1 Multiple Sequence Alignment 509</p> <p>13.4.2 Sequence Signatures 512</p> <p>13.5 Homology Modeling 515</p> <p>Selected Reading 520</p> <p>References 520</p> <p><b>14 Future Directions 525<br /></b><i>Johann Gasteiger</i></p> <p>14.1 Access to Chemical Information 525</p> <p>14.2 Representation of Chemical Compounds 527</p> <p>14.3 Representation of Chemical Reactions 527</p> <p>14.4 Learning from Chemical Information 528</p> <p>14.5 Training in Chemoinformatics 529</p> <p>Answers Section 531</p> <p>Index 555</p>
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.<br> <br> 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).<br>

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