Spis treści: Chemoinformatics: An Approach to Virtual Screening


Chapter 1    Fragment Descriptors in SAR/QSAR/QSPR Studies, Molecular Similarity Analysis and in Virtual Screening
Igor Baskin and Alexandre Varnek

1.1    Introduction, s. 1
1.2    Historical Survey, s. 2
1.3     Main Characteristics of Fragment Descriptors, s. 4
1.3.1    Types of Fragments, s. 4
1.3.2    Fragments Describing SupramolecularSystems and Chemical Reactions, s. 14
1.3.3    Storage of Fragment Information, s. 15
1.3.4    Fragment Connectivity, s. 17
1.3.5    Generic Graphs, s. 18
1.3.6    Labeling Atoms, s. 20
1.4    Application in Virtual Screening and In Silico Design, s. 20
1.4.1    Filtering, s. 22
1.4.2    Similarity Search, s. 22
1.4.3    SAR Classification (Probabilistic) Models, s. 25
1.4.4    QSAR/QSPR Regression Models, s. 26
1.4.5    In Silico Design, s. 28
1.5    Limitations of Fragment Descriptors, s. 29
1.6    Conclusion, s. 30
Acknowledgements, s. 30
References, s. 30

Chapter 2    Topological Pharmacophores Dragos Horvath

2.1    Introduction, s. 44
2.1.1    3D Pharmacophore Models and Descriptors, s. 45
2.1.2    Topological Pharmacophores, s. 47
2.2    Topological Pharmacophores from 2D-Aligments, s. 50
2.3    Topological Pharmacophores from Pharmacophore Fingerprints, s. 52
2.3.1    Topological Pharmacophore Pair Fingerprints, s. 52
2.3.2    Topological Pharmacophore Triplets                      53
2.3.3    Similarity Searching with Pharmacophore Fingerprints - Technical Issues, s. 54
2.3.4    Similarity Searching with Pharmacophore Fingerprints - Some Examples, s. 58
2.3.5    Machine-learning of Topological Pharmacophores from Fingerprints, s. 61
2.4    Topological Index-based "Pharmacophores"?, s. 64
2.5    Conclusions, s. 67
2.5.1    How Important is 3D Modeling for Pharmacophore Characterization?, s. 67
2.5.2    2D Pharmacophore Fingerprints are Mainstream Chemoinformatics Tools, whereas 2D Pharmacophore Elucidation has been Rarely Attempted, s. 68
2.5.3    Each QSAR Problem should be Allowed to Choose its Descriptors of Predilection, s. 69
Abbreviations, s. 72
References, s. 72

Chapter 3    Pharmacophore-based Virtual Screening in Drug Discovery
Christian Laggner, Gerhard Wolber, Johannes Kirchmair, Daniela Schuster and Thierry Langer

3.1     Introduction, s. 76
3.2    Virtual Screening Methods, s. 77
3.3    Chemical Feature-based Pharmacophores, s. 78
3.3.1    The Term "3D Pharmacophore", s. 79
3.3.2    Feature Definitions and Pharmacophore Representation, s. 79
3.4    Generation and Use of Pharmacophore Models, s. 86
3.4.1    Ligand-based Pharmacophore Modeling, s. 86
3.4.2    Structure-based Pharmacophore Modeling, s. 88
3.4.3    Inclusion of Shape Information, s. 92
3.4.4    Qualitative vs. Quantitative Pharmacophore Models, s. 93
3.4.5    Validation of Models for Virtual Screening, s. 95
3.5    Application of Pharmacophore Models in Virtual Screening, s. 99
3.5.1    Pharmacophore Models as Part of a Multi-step Screening Approach, s. 101
3.5.2    Antitarget and ADME(T) Screening Using Pharmacophores, s. 102
3.5.3    Pharmacophore Models for Activity Profiling and Parallel Virtual Screening, s. 103
3.6    Pharmacophore Method Extensions and Comparisons to Other Virtual Screening Methods, s. 104
3.6.1    Topological Fingerprints, s. 104
3.6.2    Shape-based Virtual Screening, s. 106
3.6.3    Docking Methods, s. 106
3.6.4    Pharmacophore Constraints Used in Docking, s. 107
3.7    Further Reading, s. 108
3.8    Summary and Conclusion, s. 108
References, s. 110

Chapter 4    Molecular Similarity Analysis in Virtual Screening
Lisa Peltason and Jtirgen Bajorath

4.1    Introduction, s. 120
4.2    Ligand-based Virtual Screening, s. 123
4.3    Foundations of Molecular Similarity Analysis, s. 125
4.3.1    Molecular Similarity and Chemical Spaces, s. 125
4.3.2    Similarity Measures, s. 126
4.3.3    Activity Landscapes, s. 127
4.3.4    Analyzing the Nature of Structure-Activity Relationships, s. 127
4.4    Strengths and Limitations of Similarity Methods, s. 144
4.5    Conclusion and Future Perspectives, s. 146
References147

Chapter 5    Molecular Field Topology Analysis in Drug Design and Virtual Screening
Eugene V. Radchenko, Vladimir A. Palyulin and Nikolay S. Zefirov

5.1    Introduction: Local Molecular Parameters in QSAR, Drug Design and Virtual Screening, s. 150
5.2    Supergraph-based QSAR Models, s. 153
5.2.1    Rationale and History, s. 153
5.2.2    Molecular Field Topology Analysis (MFTA), s. 154
5.3     From MFTA Model to Drug Design and Virtual Screening, s. 162
5.3.1    MFTA Models in Biotarget and Drug Action Analysis, s. 162
5.3.2    MFTA Models in Virtual Screening, s. 163
5.4    Conclusion, s. 176
Acknowledgements, s. 177
References, s. 177

Chapter 6    Probabilistic Approaches in Activity Prediction
Dmitry Filimonov and Vladimir Poroikov

6.1    Introduction, s. 182
6.2    Biological Activity, s. 183
6.2.1    Dose-Effect Relationships, s. 185
6.2.2    Experimental Data, s. 188
6.3    Probabilistic Ligand-based Virtual Screening Methods, s. 189
6.3.1    Preparation of Training Sets, s. 189
6.3.2    Creation of Evaluation Sets, s. 190
6.3.3    Mathematical Approaches, s. 191
6.3.4    Evaluation of Prediction Accuracy, s. 194
6.3.5    Single-targeted vs. Multi-targeted Virtual Screening, s. 198
6.4    PASS Approach, s. 199
6.4.1    Biological Activities Predicted by PASS, s. 199
6.4.2    Chemical Structure Description in PASS, s. 200
6.4.3    SAR Base, s. 200
6.4.4    Algorithm of Activity Spectrum Estimation, s. 201
6.4.5    Interpretation of Prediction Results, s. 206
6.4.6    Selection of the Most Prospective Compounds, s. 207
6.5    Conclusions, s. 207
References, s. 210

Chapter 7    Fragment-based De Novo Design of Drug-like Molecules
Ewgenij Proschak, Yusuf Tanrikulu and Gisbert Schneider

7.1     Introduction, s. 217
7.2    From Molecules to Fragments, s. 221
7.2.1    Pseudo-retrosynthesis, s. 222
7.2.2    Shape-derived Fragment Definition, s. 223
7.3    From Fragments to Molecules, s. 225
7.4    Scoring the Design, s. 229
7.5    Conclusions and Outlook, s. 234
Acknowledgements, s. 236
References, s. 236

Chapter 8    Early ADME/T Predictions: Toy or Tool?
Igor V. Tetko and Tudor I. Oprea

8.1     Introduction, s. 240
8.2    Which Properties are Important for Early Drug Discovery?, s. 241
8.2.1    Pfizer, s. 241
8.2.2    Abbot, s. 242
8.2.3    Novartis, s. 242
8.2.4    Bayer, s. 242
8.2.5    Inpharmatica, s. 243
8.3    Physicochemical Profiling, s. 244
8.3.1    Lipophilicity, s. 244
8.3.2    Solubility, s. 246
8.4    Why Predictions Fail: The Applicability Domain Challenge, s. 247
8.4.1    AD Based on Similarity in the Descriptor Space, s. 248
8.4.2    AD Based on Similarity in the Property-based Space, s. 249
8.4.3    How Reliable are Physicochemical Property Predictions?, s. 250
8.5    Available Data for ADME/T Biological Properties, s. 251
8.5.1    Absorption, s. 251
8.5.2    Distribution, s. 255
8.6    The Usefulness of ADME/T Models is Limited by the Available Data, s. 258
8.7    Conclusions, s. 260
Acknowledgements, s. 260
References, s. 260

Chapter 9    Compound Library Design - Principles and Applications
Weifan Zheng and Stephen R. Johnson

9.1     Introduction, s. 268
9.1.1   Compound Library Design, s. 269
9.2    Methods for Compound Library Design, s. 271
9.2.1     Design for Specific Biological Activities, s. 271
9.2.2    Design for Developability or Drug-likeness, s. 281
9.2.3     Design for Multiple Objectives and Targets Simultaneously, s. 285
9.3     Concluding Remarks, s. 287
References, s. 288

Chapter 10 Integrated Chemo- and Bioinformatics Approaches to Virtual Screening
Alexander Tropsha

10.1 Introduction, s. 295
10.2 Availability of Large Compound Collections for Virtual Screening, s. 296
10.2.1    NIH Molecular Libraries Roadmap Initiative and the PubChem Database, s. 297
10.2.2    Other Chemical Databases in the Public, s. 297
10.3     Structure-based Virtual Screening, s. 297
10.3.1     Major Methodologies, s. 298
10.3.2    Challenges and Limitations of Current Approaches, s. 298
10.4    Implementation of Cheminformatics Concepts in Structure-based Virtual Screening, s. 299
10.4.1     Predictive QSAR Models as Virtual Screening Tools, s. 300
10.4.2    Structure-based Chemical Descriptors of Protein-Ligand Interface: The EnTESS Method, s. 306
10.4.3    Structure-based Cheminformatics Approach to Virtual Screening: The CoLiBRI Method, s. 311
10.5    Summary and Conclusions: Integration of Conventional and Cheminformatics Structure-based Virtual Screening Approaches, s. 316

Acknowledgements, s. 318
References, s. 318
Subject Index, s. 326

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