 Pharmacophores and Pharmacophore Searches
ISBN: 978-3-527-31250-4
Hardcover
395 pages
October 2006
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A Personal Foreword.
List of Contributors.
Part I Introduction.
1 Pharmacophores: Historical Perspective and Viewpoint from a Medicinal Chemist (Camille G. Wermuth).
1.1 Definitions.
1.2 Historical Perspective.
1.3 Pharmacophores: the Viewpoint of a Medicinal Chemist.
1.4 Conclusion.
References.
Part II Pharmacophore Approaches.
2 Pharmacophore Model Generation Software Tools (Konstantin Poptodorov, Tien Luu, and Rémy D. Hoffmann).
2.1 Introduction.
2.2 Molecular Alignments.
2.3 Pharmacophore Modeling.
2.4 Automated Pharmacophore Generation Methods.
2.5 Other Methods.
2.6 Conclusions.
References.
3 Alignment-free Pharmacophore Patterns – A Correlation-vector Approach (Steffen Renner, Uli Fechner, and Gisbert Schneider).
3.1 Introduction.
3.2 The Correlation-vector Approach.
3.3 Applications.
3.4 New Methods Influenced by the Correlation-vector Approach.
3.5 Conclusions.
Acknowledgments.
Abbreviations.
References.
4 Feature Trees: Theory and Applications from Large-scale Virtual Screening to Data Analysis (Matthias Rarey, Patrick Fricker, Sally Hindle, Günther Metz, Christian Rummey, and Marc Zimmermann).
4.1 Introduction: from Linear to Non-linear Molecular Descriptors.
4.2 Creating Feature Trees from Molecules.
4.3 Algorithms for Pairwise Comparison of Feature Trees.
4.4 Feature Trees in Similarity Searching and Virtual Screening.
4.5 Searching Combinatorial Fragment Spaces with Feature Trees.
4.6 Multiple Feature Tree Models: Applications in HTS Data Analysis.
4.7 Drawing Similar Compounds in 2D Using Feature TreeMappings.
4.8 Conclusion.
Acknowledgments.
References.
5 Concept and Applications of Pseudoreceptors (Klaus-Jürgen Schleifer).
5.1 Introduction.
5.2 Methodology.
5.3 Application of Pseudoreceptors.
5.4 Conclusion.
References.
6 Pharmacophores from Macromolecular Complexes with LigandScout (Gerhard Wolber and Robert Kosara).
6.1 Introduction.
6.2 The Data Source: Clean-up and Interpretation of PDB Ligand Molecules.
6.3 Chemical Feature-based Pharmacophores Used by LigandScout.
6.4 Overlaying Chemical Features.
6.5 3D Visualization and Interaction.
6.6 Application Examples: Pharmacophore Generation and Screening.
6.7 Conclusion.
Acknowledgments.
References.
7 GRID-based Pharmacophore Models: Concept and Application Examples (Francesco Ortuso, Stefano Alcaro, and Thierry Langer).
7.1 Introduction.
7.2 Theoretical Basis of the GBPM Method.
7.3 Application Examples.
7.4 Conclusions.
References.
8 “Hot Spot” Analysis of Protein-binding Sites as a Prerequisite for Structure-based Virtual Screening and Lead Optimization (Ruth Brenk and Gerhard Klebe).
8.1 Introduction.
8.2 Calculating “Hot Spots”.
8.3 From “Hot Spots” to Molecules.
8.4 Real-life Examples.
8.5 Replacement of Active-site Water Molecules.
8.6 Conclusions.
Acknowledgments.
References.
9 Application of Pharmacophore Fingerprints to Structure-based Design and Data Mining (Prabha Karnachi and Amit Kulkarni).
9.1 Introduction.
9.2 Applications of 3D Pharmacophore Fingerprints.
9.3 Conclusion.
Acknowledgments.
References.
10 SIFt: Analysis, Organization and Database Mining for Protein-Inhibitor Complexes. Application to Protein Kinase Inhibitors (Juswinder Singh, Zhan Deng, and Claudio Chuaqui).
10.1 Introduction.
10.2 How to Generate a SIFt Fingerprint.
10.3 Profile-based SIFts.
10.4 SIFt and the Analysis of Protein Kinase – Inhibitor Complexes.
10.5 Canonical Protein – Small Molecule Interactions in the Kinase Family.
10.6 Clustering of Kinase Inhibitors Based on Interaction Fingerprints.
10.7 Profile Analysis of ATP, p38 and CDK2 Complexes.
10.8 Virtual Screening.
10.9 Use of p-SIFT to Enrich Selectively p38, CDK2 and ATP Complexes.
10.10 Conclusion.
Acknowledgments.
References.
11 Application of Structure-based Alignment Methods for 3D QSAR Analyses (Wolfgang Sippl).
11.1 Introduction.
11.2 Why is 3D QSAR So Attractive?
11.3 CoMFA and Related Methods.
11.3.1 CoMFA.
11.3.2 CoMSIA.
11.3.3 GRID/GOLPE.
11.4 Reliability of 3D QSAR Models.
11.5 Structure-based Alignments Within 3D QSAR.
11.6 Conclusion.
Acknowledgments.
References.
Part III Pharmacophores for Hit Identification and Lead Profiling: Applications and Validation.
12 Application of Pharmacophore Models in Medicinal Chemistry (Fabrizio Manetti, Maurizio Botta, and Andrea Tafi).
12.1 Introduction.
12.2 Building Pharmacophore Models Able to Account for the Molecular Features Required to Target the a<sub>1</sub> Adrenergic Receptor (a<sub>1</sub>-AR) and its Subtypes.
12.3 Use of Excluded Volume Features in the Rationalization of the Activity Data of Azole Antifungal Agents.
12.4 Conclusion.
References.
13 GPCR Anti-target Modeling: Pharmacophore Models to Avoid GPCR-mediated Side-effects (Thomas Klabunde).
13.1 Introduction: GPCRs as Anti-targets.
13.2 In Silico Tools for GPCR Anti-target Modeling.
13.3 GPCR Anti-target Pharmacophore Modeling: the a<sub>1a</sub> Adrenergic Receptor.
13.4 Conclusion.
References.
14 Pharmacophores for Human ADME/Tox-related Proteins (Cheng Chang and Sean Ekins).
14.1 Introduction.
14.2 Cytochrome P450.
14.3 UDP-glucuronosyltransferase.
14.4 P-glycoprotein (P-gp).
14.5 Human Peptide Transporter 1.
14.6 Apical Sodium-dependent Bile Acid Transporter (ASBT)).
14.7 Sodium Taurocholate-transporting Polypeptide (NTCP).
14.8 Nucleoside Transporters.
14.9 Organic Cation Transporter 1 and 2.
14.10 Organic Anion-transporting Polypeptides (OATPs).
14.11 Breast Cancer Resistance Protein (BRCP).
14.12 The Nuclear Hormone Receptors.
14.13 Human Ether-a-go-go Related Gene.
14.14 Conclusion.
Acknowledgments.
References.
15 Are You Sure You Have a Good Model? (Nicolas Triballeau, Hugues-Olivier Bertrand, and Francine Acher).
15.1 Introduction.
15.2 Validation Methods: Different Answers Brought to Different Questions.
15.3 A Successful Application: the Ultimate Validation Proof.
15.4 Case Study: a New Pharmacophore Model for mGlu4R Agonists.
15.5 Conclusion.
Acknowledgments.
References.
Subject Index.
