Molecular interaction fields : applications in drug discovery and ADME prediction

This unique reference source, edited by the world's most respected expert on molecular interaction field software, covers all relevant principles of the GRID force field and its applications in medicinal chemistry. Entire chapters on 3D-QSAR, pharmacophore searches, docking studies, metabolism predictions and protein selectivity studies, among others, offer a concise overview of this emerging field. As an added bonus, this handbook includes a CD-ROM with the latest commercial versions of the GRID program and related software.

Author Notes

Gabriele Cruciani received his PhD in Organic Chemistry in 1987 and after several positions in Italy and abroad has been appointed full professor at Perugia University in 2002 where he is regularly teaching courses in computational chemistry and chemoinformactics.
Professor Cruciani has published more than 120 papers and in 2001 has received the Hansch award from the Molecular Modeling Society.
During a stay with Peter Goodford in Oxford he became intimately familiar with the GRID force field developed there and has been endowed by Prof. Goodford with the task of further developing this highly successful software tool.
In addition to his research and teaching duties at Perugia, Professor Cruciani is the scientific director of the London-based scientific software company 'Molecular Discovery' that distributes and develops numerous chemoinformatics software tools for pharmaceutical research.

A Personal

Foreword

Preface

List of Contributors

I Introduction

1 The Basic Principles of GRIDPeter Goodford

1.1 Introduction

1.2 Philosophy and Objectives

1.3 Priorities

1.4 The GRID Method

1.5 The GRID Force Field

1.6 Nomenclature

1.7 Calibrating the GRID Force Field

1.8 The Output from GRID

1.9 Conclusions

2 Calculation and Application of Molecular Interaction FieldsRebecca C. Wade

2.1 Introduction

2.2 Calculation of MIFs

2.3 Selected Applications of MIFs

2.4 Concluding Remarks and Outlook

II Pharmacodynamics

3 Protein Selectivity Studies Using GRID-MIFsThomas Fox

3.1 Introduction

3.2 GRID Calculations and Chemometric Analysis

3.3 Applications

3.4 Discussion and Conclusion

4 FLAP: 4-Point Pharmacophore Fingerprints from GRIDFrancesca Perruccio and Jonathan S. Mason and Simone Sciabola and Massimo Baroni

4.1 Introduction

4.2 FLAP Theory

4.3 Docking

4.4 Structure Based Virtual ScreeningSBVS

4.5 Ligand Based Virtual ScreeningLBVS

4.6 Protein Similarity

4.7 TOPPTriplets of Pharmacophoric Points

4.8 Conclusions

5 The Complexity of Molecular Interaction: Molecular Shape Fingerprints by the PathFinder ApproachIain McLay and Mike Hann and Emanuele Carosati and Gabriele Cruciani and Massimo Baroni

5.1 Introduction

5.2 Background

5.3 The PathFinder Approach

5.4 Examples

5.5 Conclusions

6 Alignment-independent Descriptors from Molecular Interaction FieldsManuel Pastor

6.1 Introduction

6.2 GRIND

6.3 How to Interpret a GRIND-based 3D QSAR Model

6.4 GRIND Limitations and Problems

6.5 Recent and Future Developments

6.6 Conclusions

7 3D-QSAR Using the GRID/GOLPE ApproachWolfgang Sippl

7.1 Introduction

7.2 3D-QSAR Using the GRID/GOLPE Approach

7.3 GRID/GOLPE Application Examples

7.4 Conclusion. III Pharmacokinetics

8 Use of MIF-based VolSurf Descriptors in Physicochemical and Pharmacokinetic StudiesRaimund Mannhold and Giuliano Berellini and Emanuele Carosati and and Paolo Benedetti

8.1 ADME Properties and Their Prediction

8.2 VolSurf Descriptors

8.3 Application Examples

8.4 Conclusion

9 Molecular Interaction Fields in ADME and SafetyGiovanni Cianchetta and Yi Li and Robert Singleton and Meng Zhang and Marianne Wildgoose and David Rampe and Jiesheng Kang and and Roy J. Vaz

9.1 Introduction

9.2 GRID and MIFs

9.3 Role of Pgp Efflux in the Absorption

9.4 HERG Inhibition

9.5 CYP 3A

4 Inhibition

9.6 Conclusions

10 Progress in ADME Prediction Using GRID-Molecular Interaction FieldsIsmael Zamora and Marianne Ridderstr.m and Anna-Lena Ungell and Tommy Andersson and and Lovisa Afzelius

10.1 Introduction: ADME Field in the Drug Discovery Process

10.2 Absorption

10.3 Distribution

10.4 Metabolism

10.5 Conclusions

11 Rapid ADME Filters for Lead DiscoveryTudor I. Oprea and Paolo Benedetti and Giuliano Berellini and Marius Olah and Kim Fejgin and Scott Boyer

11.1 Introduction

11.2 The Rule of FiveRo5) as ADME Filter

11.3 Molecular Interaction Fields MIFs): VolSurf

11.4 MIF-based ADME Models

11.5 Clinical Pharmacokinetics PK) and Toxicological Tox) Datasets

11.6 VolSurf in Clinical PK Data Modeling

11.7 ChemGPS-VolSurf GPSVS) in Clinical PK Property Modeling

11.8 ADME Filters: GPSVS vs. Ro5

11.9 PENGUINS: Ultrafast ADME Filter

11.10 Integrated ADME and Binding Affinity Predictions

11.11 Conclusions

12 GRID-Derived Molecular Interaction Fields for Predicting the Site of Metabolism in Human CytochromesGabriele Cruciani and Yasmin Aristei and Riccardo Vianello and Massimo Baroni

12.1

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Summary

Summary

Author Notes

Table of Contents