In silico Lead Discovery


by

Maria A. Miteva

DOI: 10.2174/97816080514271110101
eISBN: 978-1-60805-142-7, 2011
ISBN: 978-1-60805-679-8

  
  


Indexed in: Scopus, Chemical Abstracts

Computer-aided drug design and in silico screening have contributed to the discovery of several compounds that have either reached the...[view complete introduction]
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Table of Contents

Foreword , Pp. i

Martin G. Grigorov
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Preface , Pp. ii

Maria A. Miteva
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Contributors , Pp. iii-iv (2)

Maria A. Miteva
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Chemical Libraries for Virtual Screening , Pp. 1-19 (19)

David Lagorce, Olivier Sperandio, Maria A. Miteva and Bruno O. Villoutreix
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Structure-Based Virtual Screening , Pp. 20-46 (27)

Olivier Sperandio, Bruno O. Villoutreix and Maria A. Miteva
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3D Similarity Search for Lead Compound Identification , Pp. 47-59 (13)

Frederic Guyon and Pierre Tuffery
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Fragment-Based Methods for Lead Discovery , Pp. 60-83 (24)

Will R. Pitt, Alicia P. Higueruelo and Nikolay P. Todorov
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Analyzing and Predicting Protein Binding Pockets , Pp. 84-98 (15)

Rooplekha C. Mitra and Emil Alexov
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Receptor Flexibility in Ligand Docking and Virtual Screening , Pp. 99-117 (19)

Maria A. Miteva, Charles H. Robert, Jean Didier Marechal and David Perahia
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Protein-Protein Interaction Inhibition (2P2I): Mixed Methodologies for the Acceleration of Lead Discovery , Pp. 118-143 (26)

Philippe Roche and Xavier Morelli
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Application of In Silico Methods to Study ABC Transporters Involved in Multidrug Resistance , Pp. 144-162 (19)

Ilza Pajeva and Michael Wiese
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Successful Applications of In Silico Approaches for Lead/Drug Discovery , Pp. 163-175 (13)

Andrea Bortolato, Francesca Perruccio and Stefano Moro
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Visualisation and Efficient Communication in Structure-Based Lead Discovery , Pp. 176-189 (14)

Andrea Bortolato, Francesca Perruccio and Stefano Moro
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Index , Pp. 190-193 (4)

Maria A. Miteva
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Foreword

The development of quantitative mathematical models of real phenomena which are consequently evolved on computational devices has become a common practice in science and engineering in the last 50 years. The major advantage of such simulation technologies is the possibility to process large amounts of data in relatively short times and thus to accelerate the discovery rate and to improve the quality of innovation. The application of such technologies in the pharmaceutical industry holds the promise to remediate to the continuously decreasing number of new chemical entities approved by the regulatory authorities in the last decade.

The present book, edited by a leading scientist in the field of computational molecular science, Dr. Maria Miteva from INSERM, France, compiles several state-of-the art reviews of the major techniques of early stage in silico lead discovery as they are currently applied in life science industries. Chapter 1 entitled “Chemical libraries for virtual screening” shows to the reader that the drug-likeness of the constituents of virtual chemical libraries is a property which determines the hit rate of virtual screening experiments as well as the potential of the identified leads to reach late stages of development. In an effort to make a bridge between chemistry and biology, the editor invited contributions which review both bioinformatic and cheminformatic structure-based and ligand-based computational methodologies. Chapter 2 is highlighting the role of molecular docking in “Structure-based virtual screening”, while Chapter 3 describes “3D similarity search for lead compound identification” as the principal technique applied in ligand-based approaches to in silico lead discovery. Chapter 4 brings an account of how the emerging “Fragment-based methods for lead discovery” improved significantly the performance of ligand-based approaches. Further on Chapter 5 and Chapter 6 review two major challenges of structure-based approaches. The first consists in “Analyzing and predicting of protein binding pockets” as these remain unknown for a number of newly solved protein structures, while the second is related to the “Receptor flexibility in ligand docking and virtual screening” caused by atomic thermal fluctuations. Chapter 7 and Chapter 8 describe, respectively, the specific challenges of “Protein-Protein Interaction Inhibition (2P2I)” and of “Application of in silico methods to study ABC transporters involved in multidrug resistance” and in the uptake and excretion of drugs.

I would like to finally cite Chapter 9 dedicated to the “Successful applications of in silico approaches for lead/drug discovery”. The Chapter documents the impact of computational molecular science technologies on the research and development workflow, with real illustrative examples taken from the pharmaceutical industry. Chapter 10 reminds to the reader how computerized molecular graphics and visualization techniques brought to computational molecular science an unprecedented impact by allowing for direct “Visualisation and efficient communication in structure-based lead discovery”. These two last Chapters allow me to conclude that computational molecular science has now come of age. When deployed knowledgably the related technologies are capable to generate visual and intelligent scientific hypotheses about the molecular mechanisms of action of drug molecules. These models were proven to accelerate the rate of discovery, to lower attrition, to shorten the time-to-market cycle and ultimately to reduce the costs of research and development.

The documented successes of computational molecular science are however episodic and therefore in the future all our efforts should be directed towards the development of standard reproducible protocols. I believe that with the richness of the material exposed in the book, the reader will find the necessary knowledge and inspiration to bring computational molecular science to the next frontier and to transform it from an esoteric science into a reliable discovery tool at the service of every chemist and biologist.

Dr. Martin G. Grigorov
Head of Bioinformatics
Nestlé Research Center
Vers-chez-les-Blanc
CH-1000 Lausanne 26
Switzerland


Preface

The number of promising protein targets that could enter drug discovery programs has significantly increased because of the recent advances in human genomics, proteomics and because new technologies able to address difficult targets are being developed. Today drug discovery campaigns are still time consuming and expensive and in this respect in silico methods are attractive since they facilitate efficient handling of large compound collections, mining of the data and improving “hit-rates”, thereby contributing to each step of the pre-clinical work. In fact, in silico methods, and particularly in silico screening, have been used for lead identification and can complement high-throughput and NMR-based screenings. Numerous successful studies employing in silico screening suggest that such methods can be very helpful to find novel molecules. While in silico approaches have been used for many years to assist hit identification, recent studies also report the discovery of several compounds that have either reached the market or entered clinical trials as an evidence of the contribution of these technologies to pharmaceutical research.

This eBook presents important aspects of in silico methodology for discovering new lead compounds. It contains ten individual chapters addressing different topics used in modern in silico lead discovery. An overview of the most widely employed methods and the latest advances are provided. The eBook outlines the recent progress made in docking-scoring and ligand-based techniques, as well as fragment-based screening. Freely available tools to assist in silico lead discovery are also described. Many examples of successful applications of these methods for lead/drug discovery are given. Leading experts on bioinformatics and drug design address in the eBook challenging issues, such as chemical libraries design, druggable pocket prediction, protein receptor flexibility, targeting complex systems such as protein-protein complexes or ABC transport proteins, as well as mixed methodologies for acceleration of lead discovery. I hope that this eBook contains much valuable information and will help to enhance the understanding of these technologies for scientists engaged in drug discovery projects.

The successful completion of this eBook was made possible by the assistance of many people to whom I am very grateful. I express my gratitude to all individual chapter contributors and the reviewers for their suggestions. I would like to thank Dr. Bruno O. Villoutreix (Inserm, Paris, France), Dr. Emil Alexov (Clemson University, USA), Jivko Mitev (Paris, France) and Dr. Martin Grigorov (Nestle Research Center, Lausanne, Switzerland) for comments about this eBook. I also want to thank the publisher and in particular Asma Ahmed (Bentham Science Publishers Ltd.) for the support.

Maria A. Miteva
MTI, INSERM U973
University Paris Diderot
Paris, France

List of Contributors

Editor(s):
Maria A. Miteva
University Paris Diderot
France




Contributor(s):
Ruben Abagyan
University of California, San Diego, Skaggs School of Pharmacy and Pharmaceutical Sciences
9500 Gilman Drive, MC 0657
La Jolla, Ca 92093-0657
USA


Emil Alexov
Computational Biophysics and Bioinformatics, Department of Physics
Clemson University
SC , 29634
USA


Andrea Bortolato
Syngenta Crop Protection Research
Schaffhauserstrasse, CH- 4332 Stein
Switzerland


Frédéric Guyon
MTi, University Paris Diderot - Inserm UMR-S 97
35 rue Helene Brion
Paris, 75013
France


Alicia P. Higueruelo
Department of Biochemistry, University of Cambridge
80 Tennist Court Road
Cambridge
CB2 1GA
UK


David Lagorce
MTi, University Paris Diderot - Inserm UMR-S 973
35 rue Helene Brion
Paris, 75013
France


Wen Hwa Lee
Structural Genomics Consortium, University of Oxford
Old Road Campus Research Building
Roosevelt Drive, Headington
Oxford
OX3 7DQ
UK


Jean-Didier Marechal
Departament de Química
Universitat Autònoma de Barcelona
Bellaterra, 08193
Spain


Brian D. Marsden
Structural Genomics Consortium, University of Oxford
Old Road Campus Research Building
Roosevelt Drive, Headington
Oxford
OX3 7DQ
UK


Maria A. Miteva
MTi, University Paris Diderot - Inserm UMR-S 973
35 rue Helene Brion
Paris, 75013
France


Rooplekha C. Mitra
Computational Biophysics and Bioinformatics, Department of Physics
Clemson University
SC , 29634
USA


Xavier Morelli
Laboratoire Interactions et Modulateurs de Réponses (FRE3083)
CNRS & Aix-Marseille Universités, Institut de Microbiologie de la Méditerranée
31 Chemin Joseph Aiguier
Marseille Cedex 20, 13402
France


Stefano Moro
Molecular Modeling Section, Dipartimento di Scienze Farmaceutiche
Università di Padova
Via Marzolo 5
Padova, 35131
Italy


Ilza Pajeva
Centre of Biomedical Engineering, Bulgarian Academy of Sciences
Acad. G. Bonchev Str. Bl. 105
BG-1113
Sofia
Bulgaria


David Perahia
Institut de Biochimie et Biophysique Moléculaire et Cellulaire
Université Paris-Sud, Bat 430
Orsay, 91405
France


Francesca Perruccio
Syngenta Crop Protection Research
Schaffhauserstrasse
Stein
CH, 4332
Switzerland


Will R. Pitt
UCB Celltech, branch of UCB Pharma S.A., Slough, UK; Department of Biochemistry
University of Cambridge
80 Tennist Court Road
CB2 1GA
Cambridge
UK


Charles H. Robert
CNRS Laboratoire de Biochimie Théorique, IBPC
13 rue Pierre et Marie Curie
Paris, 75005
France


Philippe Roche
CNRS & Aix-Marseille Universités, Institut de Microbiologie de la Méditerranée, Laboratoire Interactions et Modulateurs de Réponses (FRE3083)
31 Chemin Joseph Aiguier
Marseille Cedex 20, 13402
France


Olivier Sperandio
MTi, University Paris Diderot - Inserm UMR-S 973
CDithem Platform
35 rue Helene Brion
Paris, 75013
France


Nikolay P. Todorov
Molscape Therapeutics
1 Woodman Way
CB2 1GA
Cambridge
UK


Pierre Tuffery
MTi, University Paris Diderot - Inserm UMR-S 973, RPBS
35 rue Helene Brion
Paris
75013
France


Bruno O. Villoutreix
MTi, University Paris Diderot - Inserm UMR-S 973
35 rue Helene Brion
Paris
75013
France


Michael Wiese
Pharmaceutical Chemistry II, Pharmaceutical Institute
University of Bonn, An der Immenburg 4
Bonn
53121
Germany




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