Pharmacophores and Pharmacophore Searches Book

Preface     XIII
A Personal Foreword     XV
List of Contributors     XVII
Introduction
Pharmacophores: Historical Perspective and Viewpoint from a Medicinal Chemist   Camille G. Wermuth     3
Definitions     3
Functional Groups Considered as Pharmacophores: the Privileged Structure Concept     4
Historical Perspective     4
Early Considerations About Structure-Activity Relationships     4
Early Considerations About the Concept of Receptors     5
Ehrlich's "Magic Bullet"     5
Fischer's "Lock and Key"     6
Pharmacophores: the Viewpoint of a Medicinal Chemist     6
Two-dimensional Pharmacophores     6
Sulfonamides and PABA     6
Estrogens     7
An Early Three-dimensional Approach: the Three-point Contact Model     7
Clonidine and Its Interaction with the a-Adrenergic Receptor     8
Criteria for a Satisfactory Pharmacophore Model     9
Combination of Pharmacophores     10
Conclusion     11
References     11
Pharmacophore Approaches
Pharmacophore Model Generation Software Tools   Konstantin Poptodorov   Tien Luu   Remy D. Hoffmann     17
Introduction     17
Molecular Alignments     18
Handling Flexibility     18
Alignment Techniques     19
Scoring and Optimization     20
Pharmacophore Modeling     21
Compound Structures and Conformations     21
Representation of Interactions in the Pharmacophore Models     22
Conformational Expansion     22
Comparison     23
Pharmacophores, Validation and Usage     23
Automated Pharmacophore Generation Methods     23
Methods Using Pharmacophore Features and Geometric Constraints     24
DISCO, GASP and GALAHAD     24
Catalyst     27
Phase     32
Pharmacophores in MOE     34
Field-based Methods     36
CoMFA     36
XED     37
Pharmacophore Fingerprints     38
ChemX/ChemDiverse, PharmPrint, OSPPREYS, 3D Keys, Tuplets     39
Other Methods     40
SCAMPI     40
Think     41
Feature Trees     43
ILP     43
Conclusions     43
References     44
Alignment-free Pharmacophore Patterns - A Correlation-vector Approach   Steffen Renner   Uli Fechner   Gisbert Schneider 49     49
Introduction     49
The Correlation-vector Approach     51
The Concept     51
Comparison of Molecular: Topology CATS     52
Comparison of Molecular Conformation: CATS3D     56
Comparison of Molecular Surfaces: SURFCATS     57
Applications     58
Retrospective Screening Studies     58
Scaffold-hopping Potential     64
Prospective Virtual Screening     69
New Methods Influenced by the Correlation-vector Approach     72
"Fuzzy" Pharmacophores: SQUID     72
Feature Point Pharmacophores: FEPOPS     76
Conclusions     76
Acknowledgments     77
Abbreviations     77
References     78
Feature Trees: Theory and Applications from Large-scale Virtual Screening to Data Analysis   Matthias Rarey   Patrick Fricker   Sally Hindle   Gunther Metz   Christian Rummey   Marc Zimmermann     81
Introduction: from Linear to Non-linear Molecular Descriptors     81
Creating Feature Trees from Molecules     82
Algorithms for Pairwise Comparison of Feature Trees     85
Recursive Division: the Split-search Algorithm     86
Subsequently Growing Matchings: the Match-search Algorithm     87
Match-Search with Gaps: the Dynamic Match-search Algorithm     89
Building Multiple Feature Tree Models     91
Feature Trees in Similarity Searching and Virtual Screening     92
Virtual Screening     92
Virtual Screening Based on Multiple Query Compounds     95
Tagged Feature Trees     97
Searching Combinatorial Fragment Spaces with Feature Trees     99
Search Algorithm     100
Set-up of Fragment Spaces     102
Searching in Fragment Spaces     105
Multiple Feature Tree Models: Applications in HTS Data Analysis     108
Drawing Similar Compounds in 2D Using Feature Tree Mappings     111
Conclusion     113
Acknowledgments     113
References     114
Concept and Applications of Pseudoreceptors   Klaus-Jurgen Schleifer     117
Introduction     117
Methodology     118
Application of Pseudoreceptors     123
Conclusion     129
References     130
Pharmacophores from Macromolecular Complexes with LigandScout   Gerhard Wolber   Robert Kosara     131
Introduction     131
Structure-based Drug Design Methods     131
Why Structure-based Pharmacophores?     132
The Data Source: Clean-up and Interpretation of PDB Ligand Molecules     132
Topological Analysis     133
Geometric and Semantic Analysis     135
Double Bond Distribution     136
Chemical Feature-based Pharmacophores Used by LigandScout     136
Characteristics of Chemical Features: Specific or Comparable?     137
Fully Automated Perception of Chemical Features     138
Vectors: Hydrogen Bonding     139
Points: Lipophilic Contacts and Charge-transfer Interactions     139
Hydrophobic Contacts     139
Positive and Negative Ionizable Areas     140
Overlaying Chemical Features     140
3D Visualization and Interaction     141
Core and Environment Visualization     141
Pharmacophore Visualization     143
Interaction     144
Application Examples: Pharmacophore Generation and Screening     145
HRV Coat Protein Inhibitor     146
ABL Tyrosine Kinase Inhibitor      146
Conclusion     147
Acknowledgments     148
References     148
GRID-based Pharmacophore Models: Concept and Application Examples   Francesco Ortuso   Stefano Alcaro   Thierry Langer     151
Introduction     151
Theoretical Basis of the GBPM Method     152
Application Examples     155
Protein-Protein Interaction: XIAP     155
Protein-Protein Interaction: the Interleukin 8 Dimer     159
DNA-Ligand Interaction     162
Conclusions     168
References     168
"Hot Spot" Analysis of Protein-binding Sites as a Prerequisite for Structure-based Virtual Screening and Lead Optimization   Ruth Brenk   Gerhard Klebe     171
Introduction     171
Calculating "Hot Spots"     171
From "Hot Spots" to Molecules     174
Real-life Examples     177
Replacement of Active-site Water Molecules     185
Conclusions     190
Acknowledgments     190
References     191
Application of Pharmacophore Fingerprints to Structure-based Design and Data Mining   Prabha Karnachi   Amit Kulkarni     193
Introduction      193
Applications of 3D Pharmacophore Fingerprints     194
Focused/Diverse Library Design Using Pharmacophore Fingerprints     194
Analyzing Protein-Ligand Interactions Using Pharmacophore Fingerprints     195
Virtual High-throughput Screen (vHTS) and Protein Selectivity     196
Application of FLIP Technology     199
Conclusion     203
Acknowledgments     204
References     204
SIFt: Analysis, Organization and Database Mining for Protein-Inhibitor Complexes. Application to Protein Kinase Inhibitors   Juswinder Singh   Zhan Deng   Claudio Chuaqui     207
Introduction     207
How to Generate a SIFt Fingerprint     208
Profile-based SIFts     210
SIFt and the Analysis of Protein Kinase - Inhibitor Complexes     211
Canonical Protein - Small Molecule Interactions in the Kinase Family     212
Clustering of Kinase Inhibitors Based on Interaction Fingerprints     212
Profile Analysis of ATP, p38 and CDK2 Complexes     215
Virtual Screening     218
Use of p-SIFT to Enrich Selectively p38, CDK2 and ATP Complexes     239
Conclusion     220
Acknowledgments     222
References      222
Application of Structure-based Alignment Methods for 3D QSAR Analyses   Wolfgang Sippl     223
Introduction     223
Why is 3D QSAR So Attractive?     225
CoMFA and Related Methods     226
CoMFA     226
CoMSIA     227
GRID/GOLPE     227
Reliability of 3D QSAR Models     228
Structure-based Alignments Within 3D QSAR     230
Conclusion     241
Acknowledgments     243
References     244
Pharmacophores for Hit Identification and Lead Profiling: Applications and Validation
Application of Pharmacophore Models in Medicinal Chemistry   Fabrizio Manetti   Maurizio Botta   Andrea Tafi     253
Introduction     253
Building Pharmacophore Models Able to Account for the Molecular Features Required to Target the a[subscript 1] Adrenergic Receptor (a[subscript 1]-AR) and its Subtypes     254
A Pharmacophore Model for a[subscript 1]-AR Antagonists     254
Pharmacophore Building     254
Pharmacophore Analysis     257
Validation of the Pharmacophore Model     259
Hit Search Through Database Mining     260
Towards a Pharmacophore Model for the a[subscript 1D]-AR Subtype      261
A Preliminary Model     261
An Improved (Simplified) Model     264
Use of Excluded Volume Features in the Rationalization of the Activity Data of Azole Antifungal Agents     268
Excluded Volume Spheres in Structure-based and Ligand-based Pharmacophore Studies     268
Issues Inherent in the Rational Design of Azole Antifungal Agents     270
Conclusion     277
References     279
GPCR Anti-target Modeling: Pharmacophore Models to Avoid GPCR-mediated Side-effects   Thomas Klabunde     283
Introduction: GPCRs as Anti-targets     283
In Silico Tools for GPCR Anti-target Modeling     285
GPCR Anti-target Pharmacophore Modeling: the a[subscript 1a] Adrenergic Receptor     285
Generation of Cross-chemotype Pharmacophore Models     286
Description of Cross-chemotype Pharmacophore Models     287
Validation of Anti-target Pharmacophore Models     289
Virtual Screening: Hit Rates and Yields     289
Virtual Screening: Fit Values and Enrichment Factors     290
Mapping of Pharmacophore Models into Receptor Site     292
Guidance of Chemical Optimization to Avoid GPCR-mediated Side-effects     294
Conclusion     295
References      296
Pharmacophores for Human ADME/Tox-related Proteins   Cheng Chang   Sean Ekins     299
Introduction     299
Cytochrome P450     301
UDP-glucuronosyltransferase     304
P-glycoprotein (P-gp)     304
Human Peptide Transporter 1     306
Apical Sodium-dependent Bile Acid Transporter (ASBT))     307
Sodium Taurocholate-transporting Polypeptide (NTCP)     307
Nucleoside Transporters     307
Organic Cation Transporter 1 and 2     308
Organic Anion-transporting Polypeptides (OATPs)     309
Breast Cancer Resistance Protein (BRCP)     311
The Nuclear Hormone Receptors     312
Human Ether-a-go-go Related Gene     314
Conclusion     315
Acknowledgments     316
References     316
Are You Sure You Have a Good Model?   Nicolas Triballeau   Hugues-Olivier Bertrand   Francine Acher     325
Introduction     325
Validation Methods: Different Answers Brought to Different Questions     326
Software-related Validation Methods     326
Ligand-based Pharmacophore Research     326
Protein Structure-based Pharmacophore Research      329
Critical Remarks Regarding Structure-based Pharmacophore Models     329
Visual Inspection     330
Consistency with Structure - Activity Relationships     331
Some Limitations of Computer Programs     331
Retained Chemical Features     332
Spatial Arrangement     332
3D-QSAR Pharmacophore Models     333
External Data to Back Up a Pharmacophore Model     335
Biophysical Data     335
Other Published Pharmacophore Models     335
The "Test Set" Approach and the Kubinyi Paradox     336
Database Mining     337
Some Metrics to Assess Screening Performances     338
The ROC Curve Approach     341
A Successful Application: the Ultimate Validation Proof     343
Validation of Pharmacophore Models for Virtual Screening     343
Which Validation Method Should One Insist On?     344
Validation of Pharmacophore Models to Guide Medicinal and Computational Chemistry     345
Validation of Pharmacophore Models for Activity Prediction     346
Which Validation Method Should One Insist On?     346
Case Study: a New Pharmacophore Model for mGlu4R Agonists     348
Metabotropic Glutamate Receptors as Potential Therapeutic Targets     348
Pharmacology of Metabotropic Glutamate Receptor Subtype 4 (mGlu4)     348
Training Set Elaboration     351
Strategy for Perceiving the Pharmacophore     352
Four Criteria to Validate our Pharmacophore Model     353
Results of Our Pharmacophore Model Research with Catalyst-Hypo-Gen and HypoRefine     354
Description of the Two Retained Pharmacophore Models     356
Hypothesis 1 (Catalyst-HypoRefine with Variable Weights)     356
Hypothesis 2 (Catalyst-HypoRefine with Variable Weights and Tolerances)     357
Comparison of the Two Retained Hypotheses     358
Further Validation: Virtual Screening of the CAP Database     360
Conclusion     361
Acknowledgments     362
References     362
Subject Index     365