Tutorials in Chemoinformatics

 
 
Standards Information Network (Verlag)
  • erschienen am 14. Juni 2017
  • |
  • 488 Seiten
 
E-Book | PDF mit Adobe-DRM | Systemvoraussetzungen
978-1-119-13797-9 (ISBN)
 
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.
weitere Ausgaben werden ermittelt
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 compoundsand chemical reactions.
1 - Title Page [Seite 5]
2 - Copyright Page [Seite 6]
3 - Contents [Seite 7]
4 - List of Contributors [Seite 17]
5 - Preface [Seite 19]
6 - About the Companion Website [Seite 21]
7 - Part 1 Chemical Databases [Seite 23]
7.1 - Chapter 1 Data Curation [Seite 25]
7.1.1 - Theoretical Background [Seite 25]
7.1.1.1 - Software [Seite 27]
7.1.2 - Step-by-Step Instructions [Seite 29]
7.1.3 - Conclusion [Seite 56]
7.1.4 - References [Seite 58]
7.2 - Chapter 2 Relational Chemical Databases: Creation, Management, and Usage [Seite 59]
7.2.1 - Theoretical Background [Seite 59]
7.2.2 - Step-by-Step Instructions [Seite 63]
7.2.3 - Conclusion [Seite 87]
7.2.4 - References [Seite 87]
7.3 - Chapter 3 Handling of Markush Structures [Seite 89]
7.3.1 - Theoretical Background [Seite 89]
7.3.2 - Step-by-Step Instructions [Seite 90]
7.3.3 - Conclusion [Seite 95]
7.3.4 - References [Seite 95]
7.4 - Chapter 4 Processing of SMILES, InChI, and Hashed Fingerprints [Seite 97]
7.4.1 - Theoretical Background [Seite 97]
7.4.2 - Algorithms [Seite 98]
7.4.3 - Step-by-Step Instructions [Seite 100]
7.4.4 - Conclusion [Seite 102]
7.4.5 - References [Seite 103]
8 - Part 2 Library Design [Seite 105]
8.1 - Chapter 5 Design of Diverse and Focused Compound Libraries [Seite 107]
8.1.1 - Introduction [Seite 107]
8.1.1.1 - Data Acquisition [Seite 108]
8.1.1.2 - Implementation [Seite 108]
8.1.2 - Compound Library Creation [Seite 109]
8.1.3 - Compound Library Analysis [Seite 112]
8.1.3.1 - Normalization of Descriptor Values [Seite 113]
8.1.3.2 - Visualizing Descriptor Distributions [Seite 114]
8.1.3.3 - Decorrelation and Dimension Reduction [Seite 116]
8.1.4 - Partitioning and Diverse Subset Calculation [Seite 117]
8.1.4.1 - Partitioning [Seite 117]
8.1.4.2 - Diverse Subset Selection [Seite 119]
8.1.5 - Combinatorial Libraries [Seite 120]
8.1.5.1 - Combinatorial Enumeration of Compounds [Seite 120]
8.1.5.2 - Retrosynthetic Approaches to Library Design [Seite 121]
8.1.6 - References [Seite 123]
9 - Part 3 Data Analysis and Visualization [Seite 125]
9.1 - Chapter 6 Hierarchical Clustering in R [Seite 127]
9.1.1 - Theoretical Background [Seite 127]
9.1.2 - Algorithms [Seite 128]
9.1.3 - Instructions [Seite 129]
9.1.4 - Hierarchical Clustering Using Fingerprints [Seite 130]
9.1.5 - Hierarchical Clustering Using Descriptors [Seite 133]
9.1.6 - Visualization of the Data Sets [Seite 135]
9.1.7 - Alternative Clustering Methods [Seite 138]
9.1.8 - Conclusion [Seite 139]
9.1.9 - References [Seite 140]
9.2 - Chapter 7 Data Visualization and Analysis Using Kohonen Self-Organizing Maps [Seite 141]
9.2.1 - Theoretical Background [Seite 141]
9.2.2 - Algorithms [Seite 142]
9.2.3 - Instructions [Seite 143]
9.2.4 - Conclusion [Seite 148]
9.2.5 - References [Seite 148]
10 - Part 4 Obtaining and Validation QSAR/QSPR Models [Seite 149]
10.1 - Chapter 8 Descriptors Generation Using the CDK Toolkit and Web Services [Seite 151]
10.1.1 - Theoretical Background [Seite 151]
10.1.2 - Algorithms [Seite 152]
10.1.2.1 - Step-by-Step Instructions [Seite 153]
10.1.3 - Conclusion [Seite 155]
10.1.4 - References [Seite 156]
10.2 - Chapter 9 QSPR Models on Fragment Descriptors [Seite 157]
10.2.1 - Abbreviations [Seite 157]
10.2.2 - DATA [Seite 158]
10.2.2.1 - ISIDA_QSPR input [Seite 159]
10.2.2.2 - Data Split Into Training and Test Sets [Seite 161]
10.2.2.3 - Substructure Molecular Fragment (SMF) Descriptors [Seite 161]
10.2.2.4 - Regression Equations [Seite 164]
10.2.2.5 - Forward and Backward Stepwise Variable Selection [Seite 164]
10.2.2.6 - Parameters of Internal Model Validation [Seite 165]
10.2.2.7 - Applicability Domain (AD) of the Model [Seite 165]
10.2.2.8 - Storage and Retrieval Modeling Results [Seite 166]
10.2.2.9 - Analysis of Modeling Results [Seite 166]
10.2.2.10 - Root-Mean Squared Error (RMSE) Estimation [Seite 170]
10.2.2.11 - Setting the Parameters [Seite 173]
10.2.2.12 - Analysis of n-Fold Cross-Validation Results [Seite 173]
10.2.2.13 - Loading Structure-Data File [Seite 175]
10.2.2.14 - Descriptors and Fitting Equation [Seite 176]
10.2.2.15 - Variables Selection [Seite 177]
10.2.2.16 - Consensus Model [Seite 177]
10.2.2.17 - Model Applicability Domain [Seite 177]
10.2.2.18 - n-Fold External Cross-Validation [Seite 177]
10.2.2.19 - Saving and Loading of the Consensus Modeling Results [Seite 177]
10.2.2.20 - Statistical Parameters of the Consensus Model [Seite 178]
10.2.2.21 - Consensus Model Performance as a Function of Individual Models Acceptance Threshold [Seite 179]
10.2.2.22 - Building Consensus Model on the Entire Data Set [Seite 180]
10.2.2.23 - Loading Input Data [Seite 181]
10.2.2.24 - Loading Selected Models and Choosing their Applicability Domain [Seite 182]
10.2.2.25 - Reporting Predicted Values [Seite 182]
10.2.2.26 - Analysis of the Fragments Contributions [Seite 183]
10.2.3 - References [Seite 183]
10.3 - Chapter 10 Cross-Validation and the Variable Selection Bias [Seite 185]
10.3.1 - Theoretical Background [Seite 185]
10.3.2 - Step-by-Step Instructions [Seite 187]
10.3.3 - Conclusion [Seite 194]
10.3.4 - References [Seite 195]
10.4 - Chapter 11 Classification Models [Seite 197]
10.4.1 - Theoretical Background [Seite 198]
10.4.2 - Algorithms [Seite 200]
10.4.3 - Step-by-Step Instructions [Seite 202]
10.4.4 - Conclusion [Seite 213]
10.4.5 - References [Seite 214]
10.5 - Chapter 12 Regression Models [Seite 215]
10.5.1 - Theoretical Background [Seite 216]
10.5.2 - Step-by-Step Instructions [Seite 219]
10.5.3 - Conclusion [Seite 229]
10.5.4 - References [Seite 230]
10.6 - Chapter 13 Benchmarking Machine-Learning Methods [Seite 231]
10.6.1 - Theoretical Background [Seite 231]
10.6.2 - Step-by-Step Instructions [Seite 232]
10.6.3 - Conclusion [Seite 244]
10.6.4 - References [Seite 244]
10.7 - Chapter 14 Compound Classification Using the scikit-learn Library [Seite 245]
10.7.1 - Theoretical Background [Seite 246]
10.7.2 - Algorithms [Seite 247]
10.7.3 - Step-by-Step Instructions [Seite 252]
10.7.3.1 - Naïve Bayes [Seite 252]
10.7.3.2 - Decision Tree [Seite 253]
10.7.3.3 - Support Vector Machine [Seite 256]
10.7.4 - Notes on Provided Code [Seite 259]
10.7.5 - Conclusion [Seite 260]
10.7.6 - References [Seite 261]
11 - Part 5 Ensemble Modeling [Seite 263]
11.1 - Chapter 15 Bagging and Boosting of Classification Models [Seite 265]
11.1.1 - Theoretical Background [Seite 265]
11.1.2 - Algorithm [Seite 266]
11.1.3 - Conclusion [Seite 269]
11.1.4 - References [Seite 269]
11.2 - Chapter 16 Bagging and Boosting of Regression Models [Seite 271]
11.2.1 - Theoretical Background [Seite 271]
11.2.2 - Algorithm [Seite 271]
11.2.3 - Step-by-Step Instructions [Seite 272]
11.2.4 - Conclusion [Seite 277]
11.2.5 - References [Seite 277]
11.3 - Chapter 17 Instability of Interpretable Rules [Seite 279]
11.3.1 - Theoretical Background [Seite 279]
11.3.2 - Algorithm [Seite 280]
11.3.3 - Step-by-Step Instructions [Seite 280]
11.3.4 - Conclusion [Seite 283]
11.3.5 - References [Seite 283]
11.4 - Chapter 18 Random Subspaces and Random Forest [Seite 285]
11.4.1 - Theoretical Background [Seite 286]
11.4.2 - Algorithm [Seite 286]
11.4.3 - Step-by-Step Instructions [Seite 287]
11.4.4 - Conclusion [Seite 291]
11.4.5 - References [Seite 291]
11.5 - Chapter 19 Stacking [Seite 293]
11.5.1 - Theoretical Background [Seite 293]
11.5.2 - Algorithm [Seite 294]
11.5.3 - Step-by-Step Instructions [Seite 295]
11.5.4 - Conclusion [Seite 299]
11.5.5 - References [Seite 300]
12 - Part 6 3D Pharmacophore Modeling [Seite 301]
12.1 - Chapter 20 3D Pharmacophore Modeling Techniques in Computer-Aided Molecular Design Using LigandScout [Seite 303]
12.1.1 - Introduction [Seite 303]
12.1.2 - Theory: 3D Pharmacophores [Seite 305]
12.1.3 - Representation of Pharmacophore Models [Seite 305]
12.1.3.1 - Hydrogen-Bonding Interactions [Seite 307]
12.1.3.2 - Hydrophobic Interactions [Seite 307]
12.1.3.3 - Aromatic and Cation?? Interactions [Seite 308]
12.1.3.4 - Ionic Interactions [Seite 308]
12.1.3.5 - Metal Complexation [Seite 308]
12.1.3.6 - Ligand Shape Constraints [Seite 309]
12.1.4 - Pharmacophore Modeling [Seite 310]
12.1.5 - Manual Pharmacophore Construction [Seite 310]
12.1.6 - Structure-Based Pharmacophore Models [Seite 311]
12.1.7 - Ligand-Based Pharmacophore Models [Seite 311]
12.1.8 - 3D Pharmacophore-Based Virtual Screening [Seite 313]
12.1.8.1 - 3D Pharmacophore Creation [Seite 313]
12.1.8.2 - Annotated Database Creation [Seite 313]
12.1.8.3 - Virtual Screening-Database Searching [Seite 314]
12.1.8.4 - Hit-List Analysis [Seite 314]
12.1.9 - Tutorial: Creating 3D-Pharmacophore Models Using LigandScout [Seite 316]
12.1.10 - Creating Structure-Based Pharmacophores From a Ligand-Protein Complex [Seite 316]
12.1.11 - Description: Create a Structure-Based Pharmacophore Model [Seite 318]
12.1.12 - Create a Shared Feature Pharmacophore Model From Multiple Ligand-Protein Complexes [Seite 318]
12.1.13 - Description: Create a Shared Feature Pharmacophore and Align it to Ligands [Seite 319]
12.1.14 - Create Ligand-Based Pharmacophore Models [Seite 320]
12.1.15 - Description: Ligand-Based Pharmacophore Model Creation [Seite 322]
12.1.16 - Tutorial: Pharmacophore-Based Virtual Screening Using LigandScout [Seite 323]
12.1.17 - Virtual Screening, Model Editing, and Viewing Hits in the Target Active Site [Seite 323]
12.1.18 - Description: Virtual Screening and Pharmacophore Model Editing [Seite 324]
12.1.19 - Analyzing Screening Results with Respect to the Binding Site [Seite 325]
12.1.20 - Description: Analyzing Hits in the Active Site Using LigandScout [Seite 327]
12.1.21 - Parallel Virtual Screening of Multiple Databases Using LigandScout [Seite 327]
12.1.22 - Virtual Screening in the Screening Perspective of LigandScout [Seite 328]
12.1.23 - Description: Virtual Screening Using LigandScout [Seite 328]
12.1.24 - Conclusions [Seite 329]
12.1.25 - Acknowledgments [Seite 329]
12.1.26 - References [Seite 329]
13 - Part 7 The Protein 3D-Structures in Virtual Screening [Seite 333]
13.1 - Chapter 21 The Protein 3D-Structures in Virtual Screening [Seite 335]
13.1.1 - Introduction [Seite 335]
13.1.2 - Description of the Example Case [Seite 336]
13.1.2.1 - Thrombin and Blood Coagulation [Seite 336]
13.1.2.2 - Active Thrombin and Inactive Prothrombin [Seite 336]
13.1.2.3 - Thrombin as a Drug Target [Seite 336]
13.1.2.4 - Thrombin Three-Dimensional Structure: The 1OYT PDB File [Seite 337]
13.1.3 - Modeling Suite [Seite 337]
13.1.4 - Overall Description of the Input Data Available on the Editor Website [Seite 337]
13.1.5 - Exercise 1: Protein Analysis and Preparation [Seite 338]
13.1.5.1 - Step 1: Identification of Molecules Described in the 1OYT PDB File [Seite 338]
13.1.5.2 - Step 2: Protein Quality Analysis of the Thrombin/Inhibitor PDB Complex Using MOE Geometry Utility [Seite 342]
13.1.5.3 - Step 3: Preparation of the Protein for Drug Design Applications [Seite 343]
13.1.5.4 - Step 4: Description of the Protein?Ligand Binding Mode [Seite 347]
13.1.5.5 - Step 5: Detection of Protein Cavities [Seite 350]
13.1.6 - Exercise 2: Retrospective Virtual Screening Using the Pharmacophore Approach [Seite 352]
13.1.6.1 - Step 1: Description of the Test Library [Seite 354]
13.1.6.2 - Step 2.1: Pharmacophore Design, Overview [Seite 355]
13.1.6.3 - Step 2.2: Pharmacophore Design, Flexible Alignment of Three Thrombin Inhibitors [Seite 356]
13.1.6.4 - Step 2.3: Pharmacophore Design, Query Generation [Seite 357]
13.1.6.5 - Step 3: Pharmacophore Search [Seite 359]
13.1.7 - Exercise 3: Retrospective Virtual Screening Using the Docking Approach [Seite 363]
13.1.7.1 - Step 1: Description of the Test Library [Seite 363]
13.1.7.2 - Step 2: Preparation of the Input [Seite 363]
13.1.7.3 - Step 3: Re-Docking of the Crystallographic Ligand [Seite 363]
13.1.7.4 - Step 4: Virtual Screening of a Database [Seite 367]
13.1.7.5 - Conclusion [Seite 370]
13.1.8 - General Conclusion [Seite 372]
13.1.9 - References [Seite 373]
14 - Part 8 Protein-Ligand Docking [Seite 375]
14.1 - Chapter 22 Protein-Ligand Docking [Seite 377]
14.1.1 - Introduction [Seite 377]
14.1.2 - Description of the Example Case [Seite 378]
14.1.3 - Methods [Seite 378]
14.1.3.1 - Ligand Preparation [Seite 381]
14.1.3.2 - Protein Preparation [Seite 381]
14.1.3.3 - Docking Parameters [Seite 382]
14.1.4 - Description of Input Data Available on the Editor Website [Seite 382]
14.1.5 - Exercises [Seite 384]
14.1.5.1 - A Quick Start with LeadIT [Seite 384]
14.1.5.2 - Re-Docking of Tacrine into AChE [Seite 384]
14.1.5.3 - Preparation of AChE From 1ACJ PDB File [Seite 384]
14.1.5.4 - Docking of Neutral Tacrine, then of Positively Charged Tacrine [Seite 385]
14.1.5.5 - Docking of Positively Charged Tacrine in AChE in Presence of Water [Seite 387]
14.1.6 - Conclusions [Seite 387]
14.1.6.1 - Cross-Docking of Tacrine?Pyridone and Donepezil Into AChE [Seite 388]
14.1.6.2 - Preparation of AChE From 1ACJ PDB File [Seite 388]
14.1.6.3 - Cross-Docking of Tacrine-Pyridone Inhibitor and Donepezil in AChE in Presence of Water [Seite 389]
14.1.6.4 - Re-Docking of Donepezil in AChE in Presence of Water [Seite 392]
14.1.7 - Conclusions [Seite 392]
14.1.8 - General Conclusions [Seite 394]
14.1.9 - Annex: Screen Captures of LeadIT Graphical Interface [Seite 394]
14.1.10 - References [Seite 397]
15 - Part 9 Pharmacophorical Profiling Using Shape Analysis [Seite 399]
15.1 - Chapter 23 Pharmacophorical Profiling Using Shape Analysis [Seite 401]
15.1.1 - Introduction [Seite 401]
15.1.2 - Description of the Example Case [Seite 402]
15.1.2.1 - Aim and Context [Seite 402]
15.1.2.2 - Description of the Searched Data Set [Seite 403]
15.1.2.3 - Description of the Query [Seite 403]
15.1.3 - Methods [Seite 403]
15.1.3.1 - ROCS [Seite 403]
15.1.3.2 - VolSite and Shaper [Seite 406]
15.1.3.3 - Other Programs for Shape Comparison [Seite 406]
15.1.4 - Description of Input Data Available on the Editor Website [Seite 407]
15.1.5 - Exercises [Seite 409]
15.1.5.1 - Preamble: Practical Considerations [Seite 409]
15.1.5.2 - Ligand Shape Analysis [Seite 409]
15.1.5.3 - What are ROCS Output Files? [Seite 409]
15.1.5.4 - Binding Site Comparison [Seite 410]
15.1.6 - Conclusions [Seite 412]
15.1.7 - References [Seite 413]
16 - Part 10 Algorithmic Chemoinformatics [Seite 415]
16.1 - Chapter 24 Algorithmic Chemoinformatics [Seite 417]
16.1.1 - Introduction [Seite 417]
16.1.2 - Similarity Searching Using Data Fusion Techniques [Seite 418]
16.1.3 - Introduction to Virtual Screening [Seite 418]
16.1.4 - The Three Pillars of Virtual Screening [Seite 419]
16.1.4.1 - Molecular Representation [Seite 419]
16.1.4.2 - Similarity Function [Seite 419]
16.1.4.3 - Search Strategy (Data Fusion) [Seite 419]
16.1.5 - Fingerprints [Seite 419]
16.1.5.1 - Count Fingerprints [Seite 419]
16.1.5.2 - Fingerprint Representations [Seite 421]
16.1.5.3 - Bit Strings [Seite 421]
16.1.5.4 - Feature Lists [Seite 421]
16.1.5.5 - Generation of Fingerprints [Seite 421]
16.1.6 - Similarity Metrics [Seite 424]
16.1.7 - Search Strategy [Seite 426]
16.1.8 - Completed Virtual Screening Program [Seite 427]
16.1.9 - Benchmarking VS Performance [Seite 428]
16.1.9.1 - Scoring the Scorers [Seite 429]
16.1.9.2 - How to Score [Seite 429]
16.1.9.3 - Multiple Runs and Reproducibility [Seite 430]
16.1.10 - Adjusting the VS Program for Benchmarking [Seite 430]
16.1.10.1 - Analyzing Benchmark Results [Seite 432]
16.1.11 - Conclusion [Seite 436]
16.1.12 - Introduction to Chemoinformatics Toolkits [Seite 437]
16.1.13 - Theoretical Background [Seite 437]
16.1.14 - A Note on Graph Theory [Seite 438]
16.1.15 - Basic Usage: Creating and Manipulating Molecules in RDKit [Seite 439]
16.1.15.1 - Creation of Molecule Objects [Seite 439]
16.1.15.2 - Molecule Methods [Seite 440]
16.1.15.3 - Atom Methods [Seite 440]
16.1.15.4 - Bond Methods [Seite 441]
16.1.16 - An Example: Hill Notation for Molecules [Seite 441]
16.1.17 - Canonical SMILES: The Canon Algorithm [Seite 442]
16.1.18 - Theoretical Background [Seite 442]
16.1.18.1 - Recap of SMILES Notation [Seite 442]
16.1.18.2 - Canonical SMILES [Seite 443]
16.1.19 - Building a SMILES String [Seite 444]
16.1.20 - Canonicalization of SMILES [Seite 447]
16.1.20.1 - The Initial Invariant [Seite 449]
16.1.21 - The Iteration Step [Seite 450]
16.1.22 - Summary [Seite 453]
16.1.23 - Substructure Searching: The Ullmann Algorithm [Seite 454]
16.1.24 - Theoretical Background [Seite 454]
16.1.25 - Backtracking [Seite 455]
16.1.25.1 - A Note on Atom Order [Seite 458]
16.1.26 - The Ullmann Algorithm [Seite 458]
16.1.26.1 - Sample Runs [Seite 462]
16.1.27 - Summary [Seite 463]
16.1.28 - Atom Environment Fingerprints [Seite 463]
16.1.29 - Theoretical Background [Seite 463]
16.1.30 - Implementation [Seite 465]
16.1.30.1 - The Hashing Function [Seite 465]
16.1.30.2 - The Initial Atom Invariant [Seite 466]
16.1.30.3 - The Algorithm [Seite 466]
16.1.31 - Summary [Seite 469]
16.1.32 - References [Seite 469]
17 - Index [Seite 471]
18 - EULA [Seite 485]

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