Chemoinformatics in Drug Discovery

Cheminformatics experts from large pharmaceutical companies, as well as from cheminformatics service providers and from academia demonstrate what can be achieved today by harnessing the power of computational methods for the drug discovery process.
From the contents:Cheminformatics in Lead DiscoveryMolecular Complexity and Screening Set DesignAlgorithmic Engines in Virtual ScreeningPharmacophore–Based Virtual ScreeningEnhancing Hit Quality and DiversityMolecular Diversity in Lead DiscoveryIn Silico Lead OptimizationUsing Databases and LibrariesCombinational Libraries Based on Privileged SubstructuresStrategies for Directed Compound AcquisitionPredictive QSAR Models in Database MiningDrug Discovery in Academia – a Case StudyWith the user rather than the developer of cheminformatics software in mind, the successful application of computational tools for commonly encountered tasks is described in detail, and numerous real life examples are given. An invaluable resource for drug developers and medicinal chemists in academia and industry.

Spis treści:
A Personal Foreword.
Preface.
List of Contributors.
1 Introduction to Chemoinformatics in Drug Discovery – A Personal View (Garland R.Marshall).
1.1 Introduction.
1.2 Historical Evolution.
1.3 Known versus Unknown Targets.
1.4 Graph Theory and Molecular Numerology.
1.5 Pharmacophore.
1.6 Active–Analog Approach.
1.7 Active–Site Modeling.
1.8 Validation of the Active–Analog Approach and Active–Site Modeling.
1.9 PLS/CoMFA.
1.10 Prediction of Affinity.
1.11 Protein Structure Prediction.
1.12 Structure–Based Drug Design.
1.13 Real World Pharmaceutical Issues.
1.14 Combinatorial Chemistry and High–throughput Screens.
1.15 Diversity and Similarity.
1.16 Prediction of ADME.
1.17 Failures to Accurately P redict.
1.18 Summary.
Part I: Virtual Screening.
2 Chemoinformatics in Lead Discovery (Tudor I. Oprea).
2.1 Chemoinformatics in the Context of Pharmaceutical Research.
2.2 Leads in the Drug Discovery Paradigm.
2.3 Is There a Trend for High Activity Molecules?
2.4 The Concept of Leadlikeness.
2.5 Conclusions.
References.
3 Computational Chemistry, Molecular Complexity and Screening Set Design (MichaelM. Hann, Andrew R. Leach, and Darren V.S. Green).
3.1 Introduction.
3.2 Background Concepts: the Virtual, Tangible and Real Worlds of Compounds, the ‘‘Knowledge Plot’’ and Target Tractability.
3.3 The Construction of High Throughput Screening Sets.
3.4 Compound Filters.
3.5 ‘‘Leadlike’’ Screening Sets.
3.6 Focused and Biased Set Design.
3.7 Conclusion.
References.
4 Algorithmic Engines in Virtual Screening (Matthias Rarey, Christian Lemmen, and HansMatter).
4.1 Introduction.
4.2 Software Tools for Virtual Screening.
4.3 Physicochemical Models in Virtual Screening.
4.4 Algorithmic Engines in Virtual Screening.
4.5 Entering the Real World: Virtual Screening Applications.
4.5.1 Practical Considerations on Virtual Screening.
4.5.2 Successful Applications of Virtual Screening.
4.6 Practical Virtual Screening: Some Final Remarks.
References.
5 Strengths and Limitations of Pharmacophore–Based Virtual Screening (Dragos Horvath, BoryeuMao, Rafael Gozalbes, Fr´ed´erique Barbosa, and Sherry L. Rogalski).
5.1 Introduction.
5.2 The ‘‘Pharmacophore’’ Concept: Pharmacophore Features.
5.3 Pharmacophore Models:Managing Pharmacophore–related Information.
5.4 The Main Topic of This Paper.
5.5 The Cox2 Data Set.
5.6 Pharmacophore Fingerprints and Similarity Searches.
5.7 Molecular Field Analysis (MFA)–B ased Pharmacophore Information.
5.8 QSAR Models.
5.9 Hypothesis Models.
5.10 TheMinimalist Overlay–Independent QSAR Model.
5.11 Minimalist and Consensus Overlay–Based QSAR Models.
5.12 Diversity Analysis of the Cox2 Compound Set.
5.13 Do Hypothesis Models Actually Tell Us More Than Similarity Models About the Structural Reasons of Activity?
5.14 Why Did Hypothesis Models Fail to Unveil the Key Cox2 Site–Ligand Interactions?
5.15 Conclusions.
References.
Part II: Hit and Lead Discovery.
6 Enhancing Hit Quality and Diversity Within Assay Throughput Constraints (Iain McFadyen, GaryWalker, and Juan Alvarez).
6.1 Introduction.
6.2 Methods.
6.3 Results.
6.4 Discussion and Conclusion.
References.
7 Molecular Diversity in Lead Discovery: From Quantity to Quality (Cullen L. Cavallaro, DoraM. Schnur, and Andrew J. Tebben).
7.1 Introduction.
7.2 Large Libraries and Collections.
7.3 Medium–sized/Target–class Libraries and Collections.
7.4 Small Focused Libraries.
7.5 Summary/Conclusion.
References.
8 In Silico Lead Optimization (ChrisM.W. Ho).
8.1 Introduction.
8.2 The Rise of Computer–aided Drug Refinement.
8.3 RACHEL Software Package.
8.4 Extraction of Building Blocks from Corporate Databases.
8.5 Intelligent Component Selection System.
8.6 Development of a Component Specification Language.
8.7 Filtration of Components Using Constraints.
8.8 Template–driven Structure Generation.
8.9 Scoring Functions – Methods to Estimate Ligand–Receptor Binding.
8.10 Target Functions.
8.11 Ligand Optimization Example.
References.
Part III: Databases and Libraries.
9 WOMBAT: World of Molecular Bioactivity (Marius Olah, MariaMracec, Liliana Ostopovici, Ramona Rad, Alina Bora, Nicoleta Hadaruga, Ionela Olah, Magdale na Banda, Zeno Simon, Mircea Mracec, and Tudor I. Oprea).
9.1 Introduction – Brief History of theWOMBAT Project.
9.2 WOMBAT 2004.1 Overview.
9.3 WOMBAT Database Structure.
9.4 WOMBAT Quality Control.
9.5 Uncovering Errors from Literature.
9.6 Data Mining with WOMBAT.
9.7 Conclusions and Future Challenges.
References.
10 Cabinet – Chemical and Biological Informatics Network (Vera Povolna, Scott Dixon, and David Weininger).
10.1 Introduction.
10.2 Merits of Federation Rather than Unification.
10.3 HTTP is Appropriate Communication Technology.
10.4 Implementation.
10.5 Specific Examples of Federated Services.
10.6 Deployment and Refinement.
10.7 Conclusions.
References.
11 Structure Modification in Chemical Databases (PeterW. Kenny and Jens Sadowski).
11.1 Introduction.
11.2 Permute.
11.3 Leatherface.
11.4 Concluding Remarks.
References.
12 Rational Design of GPCR–specific Combinational Libraries Based on the Concept of Privileged Substructures (Nikolay P. Savchuk, Sergey E. Tkachenko, and Konstantin V. Balakin).
12.1 Introduction – Combinatorial Chemistry and Rational Drug Design.
12.2 Rational Selection of Building Blocks Based on Privileged Structural Motifs.
12.3 Conclusions.
References.
Part IV: Chemoinformatics Applications.
13 A Practical Strategy for Directed Compound Acquisition (Gerald M. Maggiora, Veerabahu Shanmugasundaram,Michael S. Lajiness, Tom N. Doman, and Martin W. Schultz).
13.1 Introduction.
13.2 A Historical Perspective.
13.3 Practical Issues.
13.4 Compound Acquisition Scheme.
13.5 Conclusions.
13.6 Methodologies.
References.
14 Efficient Strategies for Lead Optimization by Simultaneously Addressing Affinity, Selectivity and Pharmacokinetic Parameters (Karl–Heinz Baringhaus and Hans Matter).<