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E-raamat: Tutorials in Chemoinformatics

Edited by Alexandre Varnek

Formaat: EPUB+DRM
Ilmumisaeg: 22-Jun-2017
Kirjastus: John Wiley & Sons Inc
Keel: eng
ISBN-13: 9781119137986

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Formaat: EPUB+DRM
Ilmumisaeg: 22-Jun-2017
Kirjastus: John Wiley & Sons Inc
Keel: eng
ISBN-13: 9781119137986

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30 tutorials and more than 100 exercises in chemoinformatics, supported by online software and data sets

Chemoinformatics is widely used in both academic and industrial chemical and biochemical research worldwide. Yet, until this unique guide, there were no books offering practical exercises in chemoinformatics methods. Tutorials in Chemoinformatics contains more than 100 exercises in 30 tutorials exploring key topics and methods in the field. It takes an applied approach to the subject with a strong emphasis on problem-solving and computational methodologies.

Each tutorial is self-contained and contains exercises for students to work through using a variety of software packages. The majority of the tutorials are divided into three sections devoted to theoretical background, algorithm description and software applications, respectively, with the latter section providing step-by-step software instructions. Throughout, three types of software tools are used: in-house programs developed by the authors, open-source programs and commercial programs which are available for free or at a modest cost to academics. The in-house software and data sets are available on a dedicated companion website.

Key topics and methods covered in Tutorials in Chemoinformatics include:

Data curation and standardization
Development and use of chemical databases
Structure encoding by molecular descriptors, text strings and binary fingerprints
The design of diverse and focused libraries
Chemical data analysis and visualization
Structure-property/activity modeling (QSAR/QSPR)
Ensemble modeling approaches, including bagging, boosting, stacking and random subspaces
3D pharmacophores modeling and pharmacological profiling using shape analysis
Protein-ligand docking
Implementation of algorithms in a high-level programming language

Tutorials in Chemoinformatics is an ideal supplementary text for advanced undergraduate and graduate courses in chemoinformatics, bioinformatics, computational chemistry, computational biology, medicinal chemistry and biochemistry. It is also a valuable working resource for medicinal chemists, academic researchers and industrial chemists looking to enhance their chemoinformatics skills.

List of Contributors

Preface

xvii

About the Companion Website

xix

Part 1 Chemical Databases

(82)

1 Data Curation

(34)

Gilles Marcou and Alexandre Varnek

Theoretical Background

(4)

Software

(2)

Step-by-Step Instructions

(27)

Conclusion

(2)

References

(1)

2 Relational Chemical Databases: Creation, Management, and Usage

(30)

Gilles Marcou

Alexandre Varnek

Theoretical Background

(4)

Step-by-Step Instructions

(24)

Conclusion

(1)

References

(2)

3 Handling of Markush Structures

(8)

Timur Madzhidov

Ramil Nugmanov

Alexandre Varnek

Theoretical Background

(1)

Step-by-Step Instructions

(5)

Conclusion

(1)

References

(2)

4 Processing of SMILES, InChl, and Hashed Fingerprints

(8)

Joao Montargil Aires de Sousa

Theoretical Background

(1)

Algorithms

(2)

Step-by-Step Instructions

(2)

Conclusion

(1)

References

(2)

Part 2 Library Design

(20)

5 Design of Diverse and Focused Compound Libraries

(18)

Antonio de la Vega de Leon

Eugen Lounkine

Martin Vogt

Jurgen Bajorath

Introduction

(2)

Data Acquisition

(1)

Implementation

(1)

Compound Library Creation

(3)

Compound Library Analysis

(5)

Normalization of Descriptor Values

(1)

Visualizing Descriptor Distributions

(2)

Decorrelation and Dimension Reduction

(1)

Partitioning and Diverse Subset Calculation

(3)

Partitioning

(2)

Diverse Subset Selection

(1)

Combinatorial Libraries

(3)

Combinatorial Enumeration of Compounds

(1)

Retrosynthetic Approaches to Library Design

(2)

References

101

(2)

Part 3 Data Analysis and Visualization

103

(24)

6 Hierarchical Clustering in R

105

(14)

Martin Vogt

Jurgen Bajorath

Theoretical Background

105

(1)

Algorithms

106

(1)

Instructions

107

(1)

Hierarchical Clustering Using Fingerprints

108

(3)

Hierarchical Clustering Using Descriptors

111

(2)

Visualization of the Data Sets

113

(3)

Alternative Clustering Methods

116

(1)

Conclusion

117

(1)

References

118

(1)

7 Data Visualization and Analysis Using Kohonen Self-Organizing Maps

119

(8)

Jodo Montargil Aires de Sousa

Theoretical Background

119

(1)

Algorithms

120

(1)

Instructions

121

(5)

Conclusion

126

(1)

References

126

(1)

Part 4 Obtaining and Validation QSAR/QSPR Models

127

(114)

8 Descriptors Generation Using the CDK Toolkit and Web Services

129

(6)

Joao Montargil Aires de Sousa

Theoretical Background

129

(1)

Algorithms

130

(1)

Step-by-Step Instructions

131

(2)

Conclusion

133

(1)

References

134

(1)

9 QSPR Models on Fragment Descriptors

135

(28)

Vitaly Solov'ev

Alexandre Varnek

Abbreviations

135

(1)

DATA

136

(25)

ISIDA_QSPR Input

137

(2)

Data Split Into Training and Test Sets

139

(1)

Substructure Molecular Fragment (SMF) Descriptors

139

(3)

Regression Equations

142

(1)

Forward and Backward Stepwise Variable Selection

142

(1)

Parameters of Internal Model Validation

143

(1)

Applicability Domain (AD) of the Model

143

(1)

Storage and Retrieval Modeling Results

144

(1)

Analysis of Modeling Results

144

(4)

Root-Mean Squared Error (RMSE) Estimation

148

(3)

Setting the Parameters

151

(1)

Analysis of n-Fold Cross-Validation Results

151

(2)

Loading Structure-Data File

153

(1)

Descriptors and Fitting Equation

154

(1)

Variables Selection

155

(1)

Consensus Model

155

(1)

Model Applicability Domain

155

(1)

n-Fold External Cross-Validation

155

(1)

Saving and Loading of the Consensus Modeling Results

155

(1)

Statistical Parameters of the Consensus Model

156

(1)

Consensus Model Performance as a Function of Individual Models Acceptance Threshold

157

(1)

Building Consensus Model on the Entire Data Set

158

(1)

Loading Input Data

159

(1)

Loading Selected Models and Choosing their Applicability Domain

160

(1)

Reporting Predicted Values

160

(1)

Analysis of the Fragments Contributions

161

(1)

References

161

(2)

10 Cross-Validation and the Variable Selection Bias

163

(12)

Igor I. Baskin

Gilles Marcou

Dragos Horvath

Alexandre Varnek

Theoretical Background

163

(2)

Step-by-Step Instructions

165

(7)

Conclusion

172

(1)

References

173

(2)

11 Classification Models

175

(18)

Igor I. Baskin

Gilles Marcou

Dragos Horvath

Alexandre Varnek

Theoretical Background

176

(2)

Algorithms

178

(2)

Step-by-Step Instructions

180

(11)

Conclusion

191

(1)

References

192

(1)

12 Regression Models

193

(16)

Igor I. Baskin

Gilles Marcou

Dragos Horvath

Alexandre Varnek

Theoretical Background

194

(3)

Step-by-Step Instructions

197

(10)

Conclusion

207

(1)

References

208

(1)

13 Benchmarking Machine-Learning Methods

209

(14)

Igor I. Baskin

Gilles Marcou

Dragos Horvath

Alexandre Varnek

Theoretical Background

209

(1)

Step-by-Step Instructions

210

(12)

Conclusion

222

(1)

References

222

(1)

14 Compound Classification Using the scikit-learn Library

223

(18)

Jenny Balfer

Jurgen Bajorath

Martin Vogt

Theoretical Background

224

(1)

Algorithms

225

(5)

Step-by-Step Instructions

230

(8)

Naive Bayes

230

(1)

Decision Tree

231

(3)

Support Vector Machine

234

(3)

Notes on Provided Code

237

(1)

Conclusion

238

(1)

References

239

(2)

Part 5 Ensemble Modeling

241

(38)

15 Bagging and Boosting of Classification Models

243

(6)

Igor I. Baskin

Gilles Marcou

Dragos Horvath

Alexandre Varnek

Theoretical Background

243

(1)

Algorithm

244

(1)

Step by Step Instructions

245

(2)

Conclusion

247

(1)

References

247

(2)

16 Bagging and Boosting of Regression Models

249

(8)

Igor I. Baskin

Gilles Marcou

Dragos Horvath

Alexandre Varnek

Theoretical Background

249

(1)

Algorithm

249

(1)

Step-by-Step Instructions

250

(5)

Conclusion

255

(1)

References

255

(2)

17 Instability of Interpretable Rules

257

(6)

Igor I. Baskin

Gilles Marcou

Dragos Horvath

Alexandre Varnek

Theoretical Background

257

(1)

Algorithm

258

(1)

Step-by-Step Instructions

258

(3)

Conclusion

261

(1)

References

261

(2)

18 Random Subspaces and Random Forest

263

(8)

Igor I. Baskin

Gilles Marcou

Dragos Horvath

Alexandre Varnek

Theoretical Background

264

(1)

Algorithm

264

(1)

Step-by-Step Instructions

265

(4)

Conclusion

269

(1)

References

269

(2)

19 Stacking

271

(8)

Igor I. Baskin

Gilles Marcou

Dragos Horvath

Alexandre Varnek

Theoretical Background

271

(1)

Algorithm

272

(1)

Step-by-Step Instructions

273

(4)

Conclusion

277

(1)

References

278

(1)

Part 6 3D Pharmacophore Modeling

279

(32)

20 3D Pharmacophore Modeling Techniques in Computer-Aided Molecular Design Using LigandScout

281

(30)

Thomas Seidel

Sharon D. Bryant

Gokhan Ibis

Giulio Poli

Thierry Langer

Introduction

281

(2)

Theory: 3D Pharmacophores

283

(1)

Representation of Pharmacophore Models

283

(5)

Hydrogen-Bonding Interactions

285

(1)

Hydrophobic Interactions

285

(1)

Aromatic and Cation-pi Interactions

286

(1)

Ionic Interactions

286

(1)

Metal Complexation

286

(1)

Ligand Shape Constraints

287

(1)

Pharmacophore Modeling

288

(1)

Manual Pharmacophore Construction

288

(1)

Structure-Based Pharmacophore Models

289

(1)

Ligand-Based Pharmacophore Models

289

(2)

3D Pharmacophore-Based Virtual Screening

291

(16)

3D Pharmacophore Creation

291

(1)

Annotated Database Creation

291

(1)

Virtual Screening-Database Searching

292

(1)

Hit-List Analysis

292

(2)

Tutorial: Creating 3D-Pharmacophore Models Using LigandScout

294

(1)

Creating Structure-Based Pharmacophores From a Ligand-Protein Complex

294

(2)

Description: Create a Structure-Based Pharmacophore Model

296

(1)

Create a Shared Feature Pharmacophore Model From Multiple Ligand-Protein Complexes

296

(1)

Description: Create a Shared Feature Pharmacophore and Align it to Ligands

297

(1)

Create Ligand-Based Pharmacophore Models

298

(2)

Description: Ligand-Based Pharmacophore Model Creation

300

(1)

Tutorial: Pharmacophore-Based Virtual Screening Using LigandScout

301

(1)

Virtual Screening, Model Editing, and Viewing Hits in the Target Active Site

301

(1)

Description: Virtual Screening and Pharmacophore Model Editing

302

(1)

Analyzing Screening Results with Respect to the Binding Site

303

(2)

Description: Analyzing Hits in the Active Site Using LigandScout

305

(1)

Parallel Virtual Screening of Multiple Databases Using LigandScout

305

(1)

Virtual Screening in the Screening Perspective of LigandScout

306

(1)

Description: Virtual Screening Using LigandScout

306

(1)

Conclusions

307

(1)

Acknowledgments

307

(1)

References

307

(4)

Part 7 The Protein 3D-Structures in Virtual Screening

311

(42)

21 The Protein 3D-Structures in Virtual Screening

313

(40)

Inna Slynko

Esther Kellenberger

Introduction

313

(1)

Description of the Example Case

314

(1)

Thrombin and Blood Coagulation

314

(1)

Active Thrombin and Inactive Prothrombin

314

(1)

Thrombin as a Drug Target

314

(1)

Thrombin Three-Dimensional Structure: The 1OYT PDB File

315

(1)

Modeling Suite

315

(1)

Overall Description of the Input Data Available on the Editor Website

315

(1)

Exercise 1: Protein Analysis and Preparation

316

(14)

Step 1: Identification of Molecules Described in the 1OYT PDB File

316

(4)

Step 2: Protein Quality Analysis of the Thrombin/Inhibitor PDB Complex Using MOE Geometry Utility

320

(1)

Step 3: Preparation of the Protein for Drug Design Applications

321

(4)

Step 4: Description of the Protein-Ligand Binding Mode

325

(3)

Step 5: Detection of Protein Cavities

328

(2)

Exercise 2: Retrospective Virtual Screening Using the Pharmacophore Approach

330

(11)

Step 1: Description of the Test Library

332

(1)

Step 2.1: Pharmacophore Design, Overview

333

(1)

Step 2.2: Pharmacophore Design, Flexible Alignment of Three Thrombin Inhibitors

334

(1)

Step 2.3: Pharmacophore Design, Query Generation

335

(2)

Step 3: Pharmacophore Search

337

(4)

Exercise 3: Retrospective Virtual Screening Using the Docking Approach

341

(9)

Step 1: Description of the Test Library

341

(1)

Step 2: Preparation of the Input

341

(1)

Step 3: Re-Docking of the Crystallographic Ligand

341

(4)

Step 4: Virtual Screening of a Database

345

(5)

General Conclusion

350

(1)

References

351

(2)

Part 8 Protein-Ligand Docking

353

(24)

22 Protein-Ligand Docking

355

(22)

Inna Slynko

Didier Rognan

Esther Kellenberger

Introduction

355

(1)

Description of the Example Case

356

(1)

Methods

356

(4)

Ligand Preparation

359

(1)

Protein Preparation

359

(1)

Docking Parameters

360

(1)

Description of Input Data Available on the Editor Website

360

(2)

Exercises

362

(10)

A Quick Start with LeadIT

362

(1)

Re-Docking of Tacrine into AChE

362

(1)

Preparation of AChE From lACJ PDB File

362

(1)

Docking of Neutral Tacrine, then of Positively Charged Tacrine

363

(2)

Docking of Positively Charged Tacrine in AChE in Presence of Water

365

(1)

Cross-Docking of Tacrine-Pyridone and Donepezil Into AChE

366

(1)

Preparation of AChE From lACJ PDB File

366

(1)

Cross-Docking of Tacrine-Pyridone Inhibitor and Donepezil in AChE in Presence of Water

367

(3)

Re-Docking of Donepezil in AChE in Presence of Water

370

(2)

General Conclusions

372

(1)

Annex: Screen Captures of LeadIT Graphical Interface

372

(3)

References

375

(2)

Part 9 Pharmacophorical Profiling Using Shape Analysis

377

(16)

23 Pharmacophorical Profiling Using Shape Analysis

379

(14)

Jeremy Desaphy

Guillaume Bret

Inna Slynko

Didier Rognan

Esther Kellenberger

Introduction

379

(1)

Description of the Example Case

380

(1)

Aim and Context

380

(1)

Description of the Searched Data Set

381

(1)

Description of the Query

381

(1)

Methods

381

(4)

ROCS

381

(3)

VolSite and Shaper

384

(1)

Other Programs for Shape Comparison

384

(1)

Description of Input Data Available on the Editor Website

385

(2)

Exercises

387

(3)

Preamble: Practical Considerations

387

(1)

Ligand Shape Analysis

387

(1)

What are ROCS Output Files?

387

(1)

Binding Site Comparison

388

(2)

Conclusions

390

(1)

References

391

(2)

Part 10 Algorithmic Chemoinformatics

393

(56)

24 Algorithmic Chemoinformatics

395

(54)

Martin Vogt

Antonio de la Vega de Leon

Jurgen Bajorath

Introduction

395

(1)

Similarity Searching Using Data Fusion Techniques

396

(1)

Introduction to Virtual Screening

396

(1)

The Three Pillars of Virtual Screening

397

(1)

Molecular Representation

397

(1)

Similarity Function

397

(1)

Search Strategy (Data Fusion)

397

(1)

Fingerprints

397

(5)

Count Fingerprints

397

(2)

Fingerprint Representations

399

(1)

Bit Strings

399

(1)

Feature Lists

399

(1)

Generation of Fingerprints

399

(3)

Similarity Metrics

402

(2)

Search Strategy

404

(1)

Completed Virtual Screening Program

405

(1)

Benchmarking VS Performance

406

(2)

Scoring the Scorers

407

(1)

How to Score

407

(1)

Multiple Runs and Reproducibility

408

(1)

Adjusting the VS Program for Benchmarking

408

(6)

Analyzing Benchmark Results

410

(4)

Conclusion

414

(1)

Introduction to Chemoinformatics Toolkits

415

(1)

Theoretical Background

415

(1)

A Note on Graph Theory

416

(1)

Basic Usage: Creating and Manipulating Molecules in RDKit

417

(2)

Creation of Molecule Objects

417

(1)

Molecule Methods

418

(1)

Atom Methods

418

(1)

Bond Methods

419

(1)

An Example: Hill Notation for Molecules

419

(1)

Canonical SMILES: The CANON Algorithm

420

(1)

Theoretical Background

420

(2)

Recap of SMILES Notation

420

(1)

Canonical SMILES

421

(1)

Building a SMILES String

422

(3)

Canonicalization of SMILES

425

(3)

The Initial Invariant

427

(1)

The Iteration Step

428

(3)

Summary

431

(1)

Substructure Searching: The Ullmann Algorithm

432

(1)

Theoretical Background

432

(1)

Backtracking

433

(3)

A Note on Atom Order

436

(1)

The Ullmann Algorithm

436

(5)

Sample Runs

440

(1)

Summary

441

(1)

Atom Environment Fingerprints

441

(1)

Theoretical Background

441

(2)

Implementation

443

(4)

The Hashing Function

443

(1)

The Initial Atom Invariant

444

(1)

The Algorithm

444

(3)

Summary

447

(1)

References

447

(2)

Index

449

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.

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