Tutorials in Chemoinformatics

Edited by Alexandre Varnek, PhD, is a professor of theoretical chemistry at The University of Strasbourg, France where he heads the Laboratory of Chemoinformatics, and is Director of two MSc programs: Chemoinformatics and In Silico Drug Design. Professor Varnek's research focuses on developing new approaches and tools for virtual screening and "in silico" design of new compounds and chemical reactions.

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