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Chemoinformatics in Drug Discovery, Volume 23

Tudor I. Oprea (Editor), Raimund Mannhold (Series Editor), Hugo Kubinyi (Series Editor), Gerd Folkers (Series Editor)
ISBN: 978-3-527-30753-1
515 pages
March 2005
Chemoinformatics in Drug Discovery, Volume 23 (3527307532) cover image
This handbook provides the first-ever inside view of today's integrated approach to rational drug design. Chemoinformatics experts from large pharmaceutical companies, as well as from chemoinformatics service providers and from academia demonstrate what can be achieved today by harnessing the power of computational methods for the drug discovery process.
With the user rather than the developer of chemoinformatics software in mind, this book describes the successful application of computational tools to real-life problems and presents solution strategies to commonly encountered problems. It shows how almost every step of the drug discovery pipeline can be optimized and accelerated by using chemoinformatics tools -- from the management of compound databases to targeted combinatorial synthesis, virtual screening and efficient hit-to-lead transition.
An invaluable resource for drug developers and medicinal chemists in academia and industry.
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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 Predict.

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)-Based 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, Magdalena 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).

14.1 Introduction.

14.2 The Origin of Lead Structures.

14.3 Optimization for Affinity and Selectivity.

14.4 Addressing Pharmacokinetic Problems.

14.5 ADME/Antitarget Models for Lead Optimization.

14.6 Integrated Approach.

14.7 Conclusions.

References.

15 Chemoinformatic Tools for Library Design and the Hit-to-Lead Process: A User’s Perspective (Robert Alan Goodnow, Jr., Paul Gillespie, and Konrad Bleicher).

15.1 The Need for Leads: The Sources of Leads and the Challenge to Find Them.

15.2 Property Predictions.

15.3 Prediction of Solubility.

15.4 Druglikeness.

15.5 Frequent Hitters.

15.6 Identification of a Lead Series.

15.7 The Hit-to-lead Process.

15.8 Leads from Libraries: General Principles, Practical Considerations.

15.9 Druglikeness in Small-molecule Libraries.

15.10 Data Reduction and Viewing for Virtual Library Design.

15.11 Druglikeness.

15.12 Complexity and Andrews’ Binding Energy.

15.13 Solubility.

15.14 Polar Surface Area.

15.15 Number of Rotatable Bonds.

15.16 hERG Channel Binding.

15.17 Chemoinformatic Analysis of the Predicted Hansch Substituent Constants of the Diversity Reagents for Design of Vector Exploration Libraries.

15.18 Targeting Libraries by Virtual Screening.

15.19 Combinatorial Design Based on Biostructural Information.

15.20 Ligand-based Combinatorial Design: The RADDAR Approach.

15.21 Virtual Screening of Small-molecule Library with Peptide-derived Pharmacophores.

15.22 Chemoinformatic Tools and Strategies to Visualize Active Libraries.

15.23 Visualization of Library Designs during Hit-to-lead Efforts.

15.24 Summary and Outlook for Chemoinformatically Driven Lead Generation.

References.

16 Application of Predictive QSAR Models to Database Mining (Alexander Tropsha).

16.1 Introduction.

16.2 Building Predictive QSAR Models: The Importance of Validation.

16.3 Defining Model Applicability Domain.

16.4 Validated QSAR Modeling as an Empirical Data-modeling Approach: Combinatorial QSAR.

16.5 Validated QSAR Models as Virtual Screening Tools.

16.6 Conclusions and Outlook.

References.

17 Drug Discovery in Academia – A Case Study (Donald J. Abraham).

17.1 Introduction.

17.2 Linking the University with Business and Drug Discovery.

17.3 Research Parks.

17.4 Conflict of Interest Issues for Academicians.

17.5 Drug Discovery in Academia.

17.6 Case Study: The Discovery and Development of Allosteric Effectors of Hemoglobin.

References.

Subject Index.

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Tudor I. Oprea is Professor of Biochemistry and Molecular Biology and Chief, Division of Biocomputing at the University of New Mexico School of Medicine, Albuquerque (USA). He was born in Timisoara (Romania) where he did all his studies including his Ph.D. thesis under the supervision of Francisc Schneider. He was a post-doctoral fellow at Washington University with Garland Marshall, and Los Alamos National Laboratory with Angel Garcia. He worked six years at AstraZeneca in Sweden, before moving to New Mexico as full Professor in 2002. He received the Hansch Award from the QSAR and Modeling Society in 2002. He is interested in chemoinformatics, virtual screening, QSAR, and lead and drug discovery.
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"…truly a delightful beginning and ending to a thoroughly superb reading that is worth the price of admission." (Journal of the American Chemical Society, October 26, 2005)

"…a unique and richly erudite text premised on a sub-layer of the scientific process…" (Electric Review, November/December 2005)

“…a well-written, up-to-date, and practical book for medicinal chemists and computational chemists working in drug discovery.” (Journal of Medical Chemistry, Vol. 48 (19), 2005)

"This book will be of interest to professionals in the pharmaceutical industry as well as students of pharmacy, medicine, or life sciences and others interested in drug discovery." (Journal of Natural Products, August 2005)

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