Tutorials in Chemoinformatics

List of Contributors xv Preface xvii About the Companion Website xix            Part 1 Chemical Databases 1 1 Data Curation 3 Gilles Marcou and Alexandre Varnek Theoretical Background 3 Software 5 Step‐by‐Step Instructions 7 Conclusion 34 References 36 2 Relational Chemical Databases: Creation, Management, and Usage 37 Gilles Marcou and Alexandre Varnek Theoretical Background 37 Step‐by‐Step Instructions 41 Conclusion 65 References 65 3 Handling of Markush Structures 67 Timur Madzhidov, Ramil Nugmanov, and Alexandre Varnek Theoretical Background 67 Step‐by‐Step Instructions 68 Conclusion 73 References 73 4 Processing of SMILES, InChI, and Hashed Fingerprints 75 João Montargil Aires de Sousa Theoretical Background 75 Algorithms 76 Step‐by‐Step Instructions 78 Conclusion 80 References 81 Part 2 Library Design 83 5 Design of Diverse and Focused Compound Libraries 85 Antonio de la Vega de Leon, Eugen Lounkine, Martin Vogt, and Jürgen Bajorath Introduction 85 Data Acquisition 86 Implementation 86 Compound Library Creation 87 Compound Library Analysis 90 Normalization of Descriptor Values 91 Visualizing Descriptor Distributions 92 Decorrelation and Dimension Reduction 94 Partitioning and Diverse Subset Calculation 95 Partitioning 95 Diverse Subset Selection 97 Combinatorial Libraries 98 Combinatorial Enumeration of Compounds 98 Retrosynthetic Approaches to Library Design 99 References 101 Part 3 Data Analysis and Visualization 103 6 Hierarchical Clustering in R 105 Martin Vogt and Jürgen Bajorath Theoretical Background 105 Algorithms 106 Instructions 107 Hierarchical Clustering Using Fingerprints 108 Hierarchical Clustering Using Descriptors 111 Visualization of the Data Sets 113 Alternative Clustering Methods 116 Conclusion 117 References 118 7 Data Visualization and Analysis Using Kohonen Self‐Organizing Maps 119 João Montargil Aires de Sousa Theoretical Background 119 Algorithms 120 Instructions 121 Conclusion 126 References 126 Part 4 Obtaining and Validation QSAR/QSPR Models 127 8 Descriptors Generation Using the CDK Toolkit and Web Services 129 João Montargil Aires de Sousa Theoretical Background 129 Algorithms 130 Step‐by‐Step Instructions 131 Conclusion 133 References 134 9 QSPR Models on Fragment Descriptors 135 Vitaly Solov’ev and Alexandre Varnek Abbreviations 135 Data 136 ISIDA_QSPR Input 137 Data Split Into Training and Test Sets 139 Substructure Molecular Fragment (SMF) Descriptors 139 Regression Equations 142 Forward and Backward Stepwise Variable Selection 142 Parameters of Internal Model Validation 143 Applicability Domain (AD) of the Model 143 Storage and Retrieval Modeling Results 144 Analysis of Modeling Results 144 Root‐Mean Squared Error (RMSE) Estimation 148 Setting the Parameters 151 Analysis of n‐Fold Cross‐Validation Results 151 Loading Structure‐Data File 153 Descriptors and Fitting Equation 154 Variables Selection 155 Consensus Model 155 Model Applicability Domain 155 n‐Fold External Cross‐Validation 155 Saving and Loading of the Consensus Modeling Results 155 Statistical Parameters of the Consensus Model 156 Consensus Model Performance as a Function of Individual Models Acceptance Threshold 157 Building Consensus Model on the Entire Data Set 158 Loading Input Data 159 Loading Selected Models and Choosing their Applicability Domain 160 Reporting Predicted Values 160 Analysis of the Fragments Contributions 161 References 161 10 Cross‐Validation and the Variable Selection Bias 163 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 163 Step‐by‐Step Instructions 165 Conclusion 172 References 173 11 Classification Models 175 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 176 Algorithms 178 Step‐by‐Step Instructions 180 Conclusion 191 References 192 12 Regression Models 193 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 194 Step‐by‐Step Instructions 197 Conclusion 207 References 208 13 Benchmarking Machine‐Learning Methods 209 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 209 Step‐by‐Step Instructions 210 Conclusion 222 References 222 14 Compound Classification Using the scikit‐learn Library 223 Jenny Balfer, Jürgen Bajorath, and Martin Vogt Theoretical Background 224 Algorithms 225 Step‐by‐Step Instructions 230 Naïve Bayes 230 Decision Tree 231 Support Vector Machine 234 Notes on Provided Code 237 Conclusion 238 References 239 Part 5 Ensemble Modeling 241 15 Bagging and Boosting of Classification Models 243 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 243 Algorithm 244 Step by Step Instructions 245 Conclusion 247 References 247 16 Bagging and Boosting of Regression Models 249 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 249 Algorithm 249 Step‐by‐Step Instructions 250 Conclusion 255 References 255 17 Instability of Interpretable Rules 257 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 257 Algorithm 258 Step‐by‐Step Instructions 258 Conclusion 261 References 261 18 Random Subspaces and Random Forest 263 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 264 Algorithm 264 Step‐by‐Step Instructions 265 Conclusion 269 References 269 19 Stacking 271 Igor I. Baskin, Gilles Marcou, Dragos Horvath, and Alexandre Varnek Theoretical Background 271 Algorithm 272 Step‐by‐Step Instructions 273 Conclusion 277 References 278 Part 6 3D Pharmacophore Modeling 279 20 3D Pharmacophore Modeling Techniques in Computer‐Aided Molecular Design Using LigandScout 281 Thomas Seidel, Sharon D. Bryant, Gökhan Ibis, Giulio Poli, and Thierry Langer Introduction 281 Theory: 3D Pharmacophores 283 Representation of Pharmacophore Models 283 Hydrogen‐Bonding Interactions 285 Hydrophobic Interactions 285 Aromatic and Cation‐π Interactions 286 Ionic Interactions 286 Metal Complexation 286 Ligand Shape Constraints 287 Pharmacophore Modeling 288 Manual Pharmacophore Construction 288 Structure‐Based Pharmacophore Models 289 Ligand‐Based Pharmacophore Models 289 3D Pharmacophore‐Based Virtual Screening 291 3D Pharmacophore Creation 291 Annotated Database Creation 291 Virtual Screening‐Database Searching 292 Hit‐List Analysis 292 Tutorial: Creating 3D‐Pharmacophore Models Using LigandScout 294 Creating Structure‐Based Pharmacophores From a Ligand‐Protein Complex 294 Description: Create a Structure‐Based Pharmacophore Model 296 Create a Shared Feature Pharmacophore Model From Multiple Ligand‐Protein Complexes 296 Description: Create a Shared Feature Pharmacophore and Align it to Ligands 297 Create Ligand‐Based Pharmacophore Models 298 Description: Ligand‐Based Pharmacophore Model Creation 300 Tutorial: Pharmacophore‐Based Virtual Screening Using LigandScout 301 Virtual Screening, Model Editing, and Viewing Hits in the Target Active Site 301 Description: Virtual Screening and Pharmacophore Model Editing 302 Analyzing Screening Results with Respect to the Binding Site 303 Description: Analyzing Hits in the Active Site Using LigandScout 305 Parallel Virtual Screening of Multiple Databases Using LigandScout 305 Virtual Screening in the Screening Perspective of LigandScout 306 Description: Virtual Screening Using LigandScout 306 Conclusions 307 Acknowledgments 307 References 307 Part 7 The Protein 3D‐Structures in Virtual Screening 311 21 The Protein 3D‐Structures in Virtual Screening 313 Inna Slynko and Esther Kellenberger Introduction 313 Description of the Example Case 314 Thrombin and Blood Coagulation 314 Active Thrombin and Inactive Prothrombin 314 Thrombin as a Drug Target 314 Thrombin Three‐Dimensional Structure: The 1OYT PDB File 315 Modeling Suite 315 Overall Description of the Input Data Available on the Editor Website 315 Exercise 1: Protein Analysis and Preparation 316 Step 1: Identification of Molecules Described in the 1OYT PDB File 316 Step 2: Protein Quality Analysis of the Thrombin/Inhibitor PDB Complex Using MOE Geometry Utility 320 Step 3: Preparation of the Protein for Drug Design Applications 321 Step 4: Description of the Protein‐Ligand Binding Mode 325 Step 5: Detection of Protein Cavities 328 Exercise 2: Retrospective Virtual Screening Using the Pharmacophore Approach 330 Step 1: Description of the Test Library 332 Step 2.1: Pharmacophore Design, Overview 333 Step 2.2: Pharmacophore Design, Flexible Alignment of Three Thrombin Inhibitors 334 Step 2.3: Pharmacophore Design, Query Generation 335 Step 3: Pharmacophore Search 337 Exercise 3: Retrospective Virtual Screening Using the Docking Approach 341 Step 1: Description of the Test Library 341 Step 2: Preparation of the Input 341 Step 3: Re‐Docking of the Crystallographic Ligand 341 Step 4: Virtual Screening of a Database 345 General Conclusion 350 References 351 Part 8 Protein‐Ligand Docking 353 22 Protein‐Ligand Docking 355 Inna Slynko, Didier Rognan, and Esther Kellenberger Introduction 355 Description of the Example Case 356 Methods 356 Ligand Preparation 359 Protein Preparation 359 Docking Parameters 360 Description of Input Data Available on the Editor Website 360 Exercises 362 A Quick Start with LeadIT 362 Re‐Docking of Tacrine into AChE 362 Preparation of AChE From 1ACJ PDB File 362 Docking of Neutral Tacrine, then of Positively Charged Tacrine 363 Docking of Positively Charged Tacrine in AChE in Presence of Water 365 Cross‐Docking of Tacrine‐Pyridone and Donepezil Into AChE 366 Preparation of AChE From 1ACJ PDB File 366 Cross‐Docking of Tacrine‐Pyridone Inhibitor and Donepezil in AChE in Presence of Water 367 Re‐Docking of Donepezil in AChE in Presence of Water 370 General Conclusions 372 Annex: Screen Captures of LeadIT Graphical Interface 372 References 375 Part 9 Pharmacophorical Profiling Using Shape Analysis 377 23 Pharmacophorical Profiling Using Shape Analysis 379 Jérémy Desaphy, Guillaume Bret, Inna Slynko, Didier Rognan, and Esther Kellenberger Introduction 379 Description of the Example Case 380 Aim and Context 380 Description of the Searched Data Set 381 Description of the Query 381 Methods 381 Rocs 381 VolSite and Shaper 384 Other Programs for Shape Comparison 384 Description of Input Data Available on the Editor Website 385 Exercises 387 Preamble: Practical Considerations 387 Ligand Shape Analysis 387 What are ROCS Output Files? 387 Binding Site Comparison 388 Conclusions 390 References 391 Part 10 Algorithmic Chemoinformatics 393 24 Algorithmic Chemoinformatics 395 Martin Vogt, Antonio de la Vega de Leon, and Jürgen Bajorath Introduction 395 Similarity Searching Using Data Fusion Techniques 396 Introduction to Virtual Screening 396 The Three Pillars of Virtual Screening 397 Molecular Representation 397 Similarity Function 397 Search Strategy (Data Fusion) 397 Fingerprints 397 Count Fingerprints 397 Fingerprint Representations 399 Bit Strings 399 Feature Lists 399 Generation of Fingerprints 399 Similarity Metrics 402 Search Strategy 404 Completed Virtual Screening Program 405 Benchmarking VS Performance 406 Scoring the Scorers 407 How to Score 407 Multiple Runs and Reproducibility 408 Adjusting the VS Program for Benchmarking 408 Analyzing Benchmark Results 410 Conclusion 414 Introduction to Chemoinformatics Toolkits 415 Theoretical Background 415 A Note on Graph Theory 416 Basic Usage: Creating and Manipulating Molecules in RDKit 417 Creation of Molecule Objects 417 Molecule Methods 418 Atom Methods 418 Bond Methods 419 An Example: Hill Notation for Molecules 419 Canonical SMILES: The Canon Algorithm 420 Theoretical Background 420 Recap of SMILES Notation 420 Canonical SMILES 421 Building a SMILES String 422 Canonicalization of SMILES 425 The Initial Invariant 427 The Iteration Step 428 Summary 431 Substructure Searching: The Ullmann Algorithm 432 Theoretical Background 432 Backtracking 433 A Note on Atom Order 436 The Ullmann Algorithm 436 Sample Runs 440 Summary 441 Atom Environment Fingerprints 441 Theoretical Background 441 Implementation 443 The Hashing Function 443 The Initial Atom Invariant 444 The Algorithm 444 Summary 447 References 447 Index 449