Christoph Sotriffer is Professor for Pharmaceutical Chemistry at the University ofWürzburg, Germany. He graduated as a chemist from the University of Innsbruck, Austria, where he obtained his PhD in 1999. After conducting postdoctoral research at the University of California, San Diego, USA, and the University of Marburg, Germany, he moved to the University ofWürzburg in 2006, where he has built a research group for computational medicinal chemistry. Besides structure-based drug design and virtual screening, his prime scientific interest is the computational analysis and prediction of protein-ligand interactions. His work was awarded by the Austrian Chemical Society GÖCH in 2005and the German Chemical and Pharmaceutical Societies GDCh and DPhG in 2007.

"Although mediocre quality and inconsistency of some of the figures and chemical structure drawings curbed my enthusiasm, I emphatically recommend this book to anyone who is avid of learning (more) about the current and future "state-of-the-science" in virtual screening." (Molecular Informatics, 2011) "The book is well suited both for all practitioners in medicinal chemistry and for graduate students who want to learn how to apply virtual screening methodology." (International Journal Bioautomation, 2011)"All scientists interested in the field will have an interest in reading it, whether for the bibliographic contents,examples cited or principles broached. Students will find out "how to do it", whatever their intent is, which willmake this volume a useful handbook. No need to be an expert in the field or a computer specialist to give it a try." (ChemMedChem, 2011)"This comprehensive and up-to-date review of the basic concepts and tools for virtual screening applications in drug discovery is part of the Methods and Principles in Medicinal Chemistry series, which has been a crucial source of information for medicinal chemists from both academia and pharmaceutical companies since 1993." (Doody's, 30 September 2011)"Virtual Screening is a comprehensive and up-to-date overview, this is both a desktop reference and practical guide for virtual screening applications in drug discovery". (Laboratory Journal, 18 January 2011)

• List of Contributors XVIIPreface XXIIIA Personal Foreword XXVPart One Principles 11 Virtual Screening of Chemical Space: From Generic Compound Collections to Tailored Screening Libraries 3Markus Boehm1.1 Introduction 31.2 Concepts of Chemical Space 41.3 Concepts of Druglikeness and Leadlikeness 61.4 Diversity-Based Libraries 81.4.1 Concepts of Molecular Diversity 81.4.2 Descriptor-Based Diversity Selection 91.4.3 Scaffold-Based Diversity Selection 121.4.4 Sources of Diversity 131.5 Focused Libraries 151.5.1 Concepts of Focused Design 151.5.2 Ligand-Based Focused Design 161.5.3 Structure-Based Focused Design 171.5.4 Chemogenomics Approaches 181.6 Virtual Combinatorial Libraries and Fragment Spaces 201.7 Databases of Chemical and Biological Information 211.8 Conclusions and Outlook 241.9 Glossary 25References 262 Preparing and Filtering Compound Databases for Virtual and Experimental Screening 35Maxwell D. Cummings, Éric Arnoult, Christophe Buyck, Gary Tresadern, Ann M. Vos, and Jörg K. Wegner2.1 Introduction 352.2 Ligand Databases 362.2.1 Chemical Data Structures 362.2.2 3D Conformations 382.2.3 Data Storage 392.2.4 Workflow Tools 392.2.5 Past Reviews and Recent Papers 402.3 Considering Physicochemical Properties 422.3.1 Druglikeness 422.3.2 Leadlikeness and Beyond 432.4 Undesirables 432.4.1 Screening Artifacts 442.4.2 Pharmacologically Promiscuous Compounds 452.5 Property-Based Filtering for Selected Targets 462.5.1 Antibacterials 472.5.2 CNS 492.5.3 Protein–Protein Interactions 512.6 Summary 52References 533 Ligand-Based Virtual Screening 61Herbert Koeppen, Jan Kriegl, Uta Lessel, Christofer S. Tautermann, and Bernd Wellenzohn3.1 Introduction 613.2 Descriptors 623.3 Search Databases and Queries 673.3.1 Selection of Reference Ligands 673.3.2 Preparation of the Search Database 683.4 Virtual Screening Techniques 683.4.1 Similarity Searches 693.4.2 Similarity Searches in Very Large Chemical Spaces 723.4.3 Machine Learning in Virtual Screening 743.4.4 Validation of Methods and Prediction of Success 783.5 Conclusions 79References 804 The Basis for Target-Based Virtual Screening: Protein Structures 87Jason C. Cole, Oliver Korb, Tjelvar S.G. Olsson, and John Liebeschuetz4.1 Introduction 874.2 Selecting a Protein Structure for Virtual Screening 874.2.1 Why Are There Errors in Crystal Structures? 874.2.2 Possible Problems That May Occur in a Crystal Structure 914.2.3 Structural Relevance 954.2.4 Critical Evaluation of Models: Recognizing Issues in Structures 984.3 Setting Up a Protein Model for vHTS 1014.3.1 Binding Site Definition 1014.3.2 Protonation 1044.3.3 Treatment of Solvent in Docking 1044.3.4 Use of Protein-Based Constraints in Docking 1054.3.5 Protein Flexibility 1064.4 Summary 1094.5 Glossary of Crystallographic Terms 1104.5.1 R-Factor 1104.5.2 Resolution 1104.5.3 2mFo-DFc Map 110References 1105 Pharmacophore Models for Virtual Screening 115Patrick Markt, Daniela Schuster, and Thierry Langer5.1 Introduction 1155.2 Compilation of Compounds 1165.2.1 Chemical Structure Generation 1165.2.2 Conformational Analysis 1165.3 Pharmacophore Model Generation 1175.3.1 State of the Art 1175.3.2 Structure-Based Methods 1175.3.3 Ligand-Based Methods 1185.3.4 Limitations of Ligand-Based Methods 1195.4 Validation of Pharmacophore Models 1195.4.1 Chemical Databases for Validation 1195.4.2 Enrichment Assessment 1215.4.3 Enrichment Metrics 1225.4.4 Receiver Operating Characteristic Curve Analysis 1245.4.5 Area Under the ROC Curve 1255.5 Pharmacophore-Based Screening 1275.5.1 DS CATALYST 1285.5.2 UNITY (GALAHAD/GASP) 1285.5.3 LIGANDSCOUT 1295.5.4 MOE 1305.5.5 PHASE 1305.6 Postprocessing of Pharmacophore-Based Screening Hits 1315.6.1 Lead- and Druglikeness 1315.6.2 Structural Similarity Analysis 1315.7 Pharmacophore-Based Parallel Screening 1325.8 Application Examples for Synthetic Compound Screening 1335.8.1 17b-Hydroxysteroid Dehydrogenase 1 Inhibitors 1335.8.2 Cannabinoid Receptor 2 (CB2) Ligands 1345.8.3 Further Application Examples 1365.9 Application Examples for Natural Product Screening 1365.9.1 Cyclooxygenase (COX) Inhibitors 1395.9.2 Sigma-1 (s1) Receptor Ligands 1395.9.3 Acetylcholinesterase Inhibitors 1405.9.4 Human Rhinovirus Coat Protein Inhibitors 1415.9.5 Quorum-Sensing Inhibitors 1415.9.6 Peroxisome Proliferator-Activated Receptor c Ligands 1415.9.7 b-Ketoacyl-Acyl Carrier Protein Synthase III Inhibitors 1425.9.8 5-Lipoxygenase Inhibitors 1425.9.9 11b-Hydroxysteroid Dehydrogenase Type 1 Inhibitors 1425.9.10 Pharmacophore-Based Parallel Screening of Natural Products 1435.10 Conclusions 143References 1446 Docking Methods for Virtual Screening: Principles and Recent Advances 153Didier Rognan6.1 Principles of Molecular Docking 1536.1.1 Sampling Degrees of Freedom of the Ligand 1546.1.2 Scoring Ligand Poses 1566.2 Docking-Based Virtual Screening Flowchart 1586.2.1 Ligand Setup 1586.2.2 Protein Setup 1596.2.3 Docking 1606.2.4 Postdocking Analysis 1616.3 Recent Advances in Docking-Based VS Methods 1626.3.1 Novel Docking Algorithms 1626.3.2 Fragment Docking 1646.3.3 Postdocking Refinement 1646.3.4 Addressing Protein Flexibility 1666.3.5 Solvated or Dry? 1686.4 Future Trends in Docking 168References 169Part Two Challenges 1777 The Challenge of Affinity Prediction: Scoring Functions for Structure-Based Virtual Screening 179Christoph Sotriffer and Hans Matter7.1 Introduction 1797.2 Physicochemical Basis of Protein–Ligand Recognition 1807.3 Classes of Scoring Functions 1857.3.1 Force Field-Based Methods 1857.3.2 Empirical Scoring Functions 1897.3.3 Knowledge-Based Scoring Functions 1917.4 Interesting New Approaches to Scoring Functions 1927.4.1 Improved Treatment of Hydrophobicity and Dehydration 1927.4.2 Development and Validation of SFCscore 1947.4.3 Consensus Scoring 1957.4.4 Tailored Scoring Functions 1967.4.5 Structural Interaction Fingerprints 1997.5 Comparative Assessment of Scoring Functions 2007.6 Tailoring Scoring Strategies in Virtual Screening 2037.6.1 Toward a Strategy for Applying Scoring Functions 2037.6.2 Retrospective Validation Prior to Prospective Virtual Screening 2047.6.3 Lessons Learned: Improvements in Scoring Evaluations 2057.6.4 Postfiltering Results of Virtual Screenings 2057.7 Caveats for Development of Scoring Functions 2067.7.1 General Points 2067.7.2 Biological Data 2077.7.3 Structural Data on Protein–Ligand Complexes and Decoy Data Sets 2077.7.4 Cooperativity and Other Model Deficiencies 2087.8 Conclusions 209References 2108 Protein Flexibility in Structure-Based Virtual Screening: From Models to Algorithms 223Angela M. Henzler and Matthias Rarey8.1 How Flexible Are Proteins? – A Historical Perspective 2238.1.1 Ligand Binding Is Coupled with Protein Conformational Change 2238.1.2 Types of Flexibility 2248.2 Flexible Protein Handling in Protein–Ligand Docking 2258.2.1 Docking Following Conformational Selection 2278.2.2 Induced Fit Docking: Single-Structure-Based Docking Techniques 2318.2.3 Integrated Docking Approaches 2358.3 Flexible Protein Handling in Docking-Based Virtual Screening 2368.3.1 Efficiency of Fully Flexible Docking Approaches in Retrospective 2378.3.2 Discrimination of Binders and Nonbinders 2388.4 Summary 238References 2399 Handling Protein Flexibility in Docking and High-Throughput Docking: From Algorithms to Applications 245Claudio N. Cavasotto9.1 Introduction: Docking and High-Throughput Docking in Drug Discovery 2459.2 The Challenge of Accounting for Protein Flexibility in Docking 2469.2.1 Theoretical Understanding of the Problem 2469.2.2 Docking Failures Due to Protein Flexibility 2479.3 Accounting for Protein Flexibility in Docking-Based Drug Discovery and Design 2509.3.1 Receptor Ensemble-Based Docking Methods 2529.3.2 Single-Structure-Based Docking Methods 2539.3.3 Multilevel Methods 2569.3.4 Homology Modeling 2579.4 Conclusions 257References 25810 Consideration of Water and Solvation Effects in Virtual Screening 263Johannes Kirchmair, Gudrun M. Spitzer, and Klaus R. Liedl10.1 Introduction 26310.2 Experimental Approaches for Analyzing Water Molecules 26610.3 Computational Approaches for Analyzing Water Molecules 27110.3.1 Molecular Dynamics Simulations 27110.3.2 Empirical and Implicit Considerations of Solvation Effects 27410.4 Water-Sensitive Virtual Screening: Approaches and Applications 27510.4.1 Protein–Ligand Docking 27510.4.2 Pharmacophore Modeling 27810.5 Conclusions and Recommendations 281References 282Part Three Applications and Practical Guidelines 29111 Applied Virtual Screening: Strategies, Recommendations, and Caveats 293Dagmar Stumpfe and Jürgen Bajorath11.1 Introduction 29311.2 What Is Virtual Screening? 29311.3 Spectrum of Virtual Screening Approaches 29411.4 Molecular Similarity as a Foundation and Caveat of Virtual Screening 29511.5 Goals of Virtual Screening 29611.6 Applicability Domain 29711.7 Reference and Database Compounds 29911.8 Biological Activity versus Compound Potency 30011.9 Methodological Complexity and Compound Class Dependence 30111.10 Search Strategies and Compound Selection 30211.11 Virtual and High-Throughput Screening 30411.12 Practical Applications: An Overview 30611.13 LFA-1 Antagonist 30711.14 Selectivity Searching 31011.15 Concluding Remarks 314References 31512 Applications and Success Stories in Virtual Screening 319Hans Matter and Christoph Sotriffer12.1 Introduction 31912.2 Practical Considerations 32012.3 Successful Applications of Virtual Screening 32112.3.1 Structure-Based Virtual Screening 32212.3.2 Structure-Based Library Design 33612.3.3 Ligand-Based Virtual Screening 33812.4 Conclusion 347References 348Part Four Scenarios and Case Studies: Routes to Success 35913 Scenarios and Case Studies: Examples for Ligand-Based Virtual Screening 361Trevor Howe, Daniele Bemporad, and Gary Tresadern13.1 Introduction 36113.2 1D Ligand-Based Virtual Screening 36213.3 2D Ligand-Based Virtual Screening 36313.3.1 Examples from the Literature 36313.3.2 Applications at J&JPRD Europe 36613.4 3D Ligand-Based Virtual Screening 36813.4.1 Methods 37013.4.2 3DLBVS Examples 37213.5 Summary 376References 37714 Virtual Screening on Homology Models 381Róbert Kiss and György M. Keseru"14.1 Introduction 38114.2 Homology Models versus Crystal Structures: Comparative Evaluation of Screening Performance 38214.2.1 Soluble Proteins 38214.2.2 Membrane Proteins 39214.3 Challenges of Homology Model-Based Virtual Screening 39414.3.1 Level of Sequence Identity 39514.3.2 Main-Chain Flexibility 39614.3.3 Side-Chain Conformation: Induced Fit Effects of Ligands 39614.3.4 Loop Modeling 39714.4 Case Studies 39914.4.1 Virtual Screening on the Homology Model of Histamine H4 Receptor 39914.4.2 Virtual Screening on the Homology Model of Janus Kinase 2 402References 40415 Target-Based Virtual Screening on Small-Molecule Protein Binding Sites 411Ralf Heinke, Urszula Uciechowska, Manfred Jung, and Wolfgang Sippl15.1 Introduction 41115.1.1 Pharmacophore-Based Methods 41215.1.2 Ligand Docking 41215.1.3 Virtual Screening 41315.1.4 Binding Free Energy Calculations 41415.2 Structure-Based VS for Histone Arginine Methyltransferase PRMT1 Inhibitors 41415.2.1 Structure-Based VS of the NCI Diversity Set 41515.2.2 Pharmacophore-Based VS 41715.3 Identification of Nanomolar Histamine H3 Receptor Antagonists by Structure- and Pharmacophore-Based VS 42215.3.1 Generation of Homology Model of the hH3R and hH3R Antagonist Complexes 42315.3.2 Validation of the Homology Model by Docking Known Antagonists into the hH3R Binding Site 42415.3.3 Pharmacophore-Based VS 42515.3.4 Experimental Testing of the Identified Hits 42915.3.5 Discussion of the Applied VS Strategies 42915.4 Summary 431References 43216 Target-Based Virtual Screening to Address Protein–Protein Interfaces 435Olivier Sperandio, Maria A. Miteva, and Bruno O. Villoutreix16.1 Introduction 43516.2 Some Recent PPIM Success Stories 43716.3 Protein–Protein Interfaces 43816.3.1 Interface Pockets, Flexibility, and Hot Spots 44016.3.2 Databases and Tools to Analyze Interfaces 44216.4 PPIMs. Chemical Space and ADME/Tox Properties 44216.5 Drug Discovery, Chemical Biology, and In Silico Screening Methods: Overview and Suggestions for PPIM Search 44716.6 Case Studies 45016.6.1 PPI Stabilizers: Superoxide Dismutase Type 1 45016.6.2 PPI Inhibitors: Lck 45216.6.3 Allosteric Inhibitors: Antitrypsin Polymerization 45516.7 Conclusions and Future Directions 457References 45817 Fragment-Based Approaches in Virtual Screening 467Danzhi Huang and Amedeo Caflisch17.1 Introduction 46717.2 In Silico Fragment-Based Approaches 46817.3 Our Approach to High-Throughput Fragment-Based Docking 47017.3.1 Decomposition of Compounds into Fragments 47117.3.2 Docking of Anchor Fragments 47117.3.3 Flexible Docking of Library Compounds 47217.3.4 LIECE Binding Energy Evaluation 47217.3.5 Consensus Scoring 47517.3.6 In Silico Screening Campaigns 47517.3.7 West Nile Virus NS3 Protease (Flaviviral Infections) 47517.3.8 EphB4 Tyrosine Kinase (Cancer) 47717.4 Lessons Learned from Our Fragment-Based Docking 47917.5 Challenges of Fragment-Based Approaches 481References 482Appendix A: Software Overview 491Appendix B: Virtual Screening Application Studies 501Index 511

Christoph Sotriffer is Professor for Pharmaceutical Chemistry at the University ofWürzburg, Germany. He graduated as a chemist from the University of Innsbruck, Austria, where he obtained his PhD in 1999. After conducting postdoctoral research at the University of California, San Diego, USA, and the University of Marburg, Germany, he moved to the University ofWürzburg in 2006, where he has built a research group for computational medicinal chemistry. Besides structure-based drug design and virtual screening, his prime scientific interest is the computational analysis and prediction of protein-ligand interactions. His work was awarded by the Austrian Chemical Society GÖCH in 2005and the German Chemical and Pharmaceutical Societies GDCh and DPhG in 2007.

"Although mediocre quality and inconsistency of some of the figures and chemical structure drawings curbed my enthusiasm, I emphatically recommend this book to anyone who is avid of learning (more) about the current and future "state-of-the-science" in virtual screening." (Molecular Informatics, 2011) "The book is well suited both for all practitioners in medicinal chemistry and for graduate students who want to learn how to apply virtual screening methodology." (International Journal Bioautomation, 2011)"All scientists interested in the field will have an interest in reading it, whether for the bibliographic contents,examples cited or principles broached. Students will find out "how to do it", whatever their intent is, which willmake this volume a useful handbook. No need to be an expert in the field or a computer specialist to give it a try." (ChemMedChem, 2011)"This comprehensive and up-to-date review of the basic concepts and tools for virtual screening applications in drug discovery is part of the Methods and Principles in Medicinal Chemistry series, which has been a crucial source of information for medicinal chemists from both academia and pharmaceutical companies since 1993." (Doody's, 30 September 2011)"Virtual Screening is a comprehensive and up-to-date overview, this is both a desktop reference and practical guide for virtual screening applications in drug discovery". (Laboratory Journal, 18 January 2011)

• List of Contributors XVIIPreface XXIIIA Personal Foreword XXVPart One Principles 11 Virtual Screening of Chemical Space: From Generic Compound Collections to Tailored Screening Libraries 3Markus Boehm1.1 Introduction 31.2 Concepts of Chemical Space 41.3 Concepts of Druglikeness and Leadlikeness 61.4 Diversity-Based Libraries 81.4.1 Concepts of Molecular Diversity 81.4.2 Descriptor-Based Diversity Selection 91.4.3 Scaffold-Based Diversity Selection 121.4.4 Sources of Diversity 131.5 Focused Libraries 151.5.1 Concepts of Focused Design 151.5.2 Ligand-Based Focused Design 161.5.3 Structure-Based Focused Design 171.5.4 Chemogenomics Approaches 181.6 Virtual Combinatorial Libraries and Fragment Spaces 201.7 Databases of Chemical and Biological Information 211.8 Conclusions and Outlook 241.9 Glossary 25References 262 Preparing and Filtering Compound Databases for Virtual and Experimental Screening 35Maxwell D. Cummings, Éric Arnoult, Christophe Buyck, Gary Tresadern, Ann M. Vos, and Jörg K. Wegner2.1 Introduction 352.2 Ligand Databases 362.2.1 Chemical Data Structures 362.2.2 3D Conformations 382.2.3 Data Storage 392.2.4 Workflow Tools 392.2.5 Past Reviews and Recent Papers 402.3 Considering Physicochemical Properties 422.3.1 Druglikeness 422.3.2 Leadlikeness and Beyond 432.4 Undesirables 432.4.1 Screening Artifacts 442.4.2 Pharmacologically Promiscuous Compounds 452.5 Property-Based Filtering for Selected Targets 462.5.1 Antibacterials 472.5.2 CNS 492.5.3 Protein–Protein Interactions 512.6 Summary 52References 533 Ligand-Based Virtual Screening 61Herbert Koeppen, Jan Kriegl, Uta Lessel, Christofer S. Tautermann, and Bernd Wellenzohn3.1 Introduction 613.2 Descriptors 623.3 Search Databases and Queries 673.3.1 Selection of Reference Ligands 673.3.2 Preparation of the Search Database 683.4 Virtual Screening Techniques 683.4.1 Similarity Searches 693.4.2 Similarity Searches in Very Large Chemical Spaces 723.4.3 Machine Learning in Virtual Screening 743.4.4 Validation of Methods and Prediction of Success 783.5 Conclusions 79References 804 The Basis for Target-Based Virtual Screening: Protein Structures 87Jason C. Cole, Oliver Korb, Tjelvar S.G. Olsson, and John Liebeschuetz4.1 Introduction 874.2 Selecting a Protein Structure for Virtual Screening 874.2.1 Why Are There Errors in Crystal Structures? 874.2.2 Possible Problems That May Occur in a Crystal Structure 914.2.3 Structural Relevance 954.2.4 Critical Evaluation of Models: Recognizing Issues in Structures 984.3 Setting Up a Protein Model for vHTS 1014.3.1 Binding Site Definition 1014.3.2 Protonation 1044.3.3 Treatment of Solvent in Docking 1044.3.4 Use of Protein-Based Constraints in Docking 1054.3.5 Protein Flexibility 1064.4 Summary 1094.5 Glossary of Crystallographic Terms 1104.5.1 R-Factor 1104.5.2 Resolution 1104.5.3 2mFo-DFc Map 110References 1105 Pharmacophore Models for Virtual Screening 115Patrick Markt, Daniela Schuster, and Thierry Langer5.1 Introduction 1155.2 Compilation of Compounds 1165.2.1 Chemical Structure Generation 1165.2.2 Conformational Analysis 1165.3 Pharmacophore Model Generation 1175.3.1 State of the Art 1175.3.2 Structure-Based Methods 1175.3.3 Ligand-Based Methods 1185.3.4 Limitations of Ligand-Based Methods 1195.4 Validation of Pharmacophore Models 1195.4.1 Chemical Databases for Validation 1195.4.2 Enrichment Assessment 1215.4.3 Enrichment Metrics 1225.4.4 Receiver Operating Characteristic Curve Analysis 1245.4.5 Area Under the ROC Curve 1255.5 Pharmacophore-Based Screening 1275.5.1 DS CATALYST 1285.5.2 UNITY (GALAHAD/GASP) 1285.5.3 LIGANDSCOUT 1295.5.4 MOE 1305.5.5 PHASE 1305.6 Postprocessing of Pharmacophore-Based Screening Hits 1315.6.1 Lead- and Druglikeness 1315.6.2 Structural Similarity Analysis 1315.7 Pharmacophore-Based Parallel Screening 1325.8 Application Examples for Synthetic Compound Screening 1335.8.1 17b-Hydroxysteroid Dehydrogenase 1 Inhibitors 1335.8.2 Cannabinoid Receptor 2 (CB2) Ligands 1345.8.3 Further Application Examples 1365.9 Application Examples for Natural Product Screening 1365.9.1 Cyclooxygenase (COX) Inhibitors 1395.9.2 Sigma-1 (s1) Receptor Ligands 1395.9.3 Acetylcholinesterase Inhibitors 1405.9.4 Human Rhinovirus Coat Protein Inhibitors 1415.9.5 Quorum-Sensing Inhibitors 1415.9.6 Peroxisome Proliferator-Activated Receptor c Ligands 1415.9.7 b-Ketoacyl-Acyl Carrier Protein Synthase III Inhibitors 1425.9.8 5-Lipoxygenase Inhibitors 1425.9.9 11b-Hydroxysteroid Dehydrogenase Type 1 Inhibitors 1425.9.10 Pharmacophore-Based Parallel Screening of Natural Products 1435.10 Conclusions 143References 1446 Docking Methods for Virtual Screening: Principles and Recent Advances 153Didier Rognan6.1 Principles of Molecular Docking 1536.1.1 Sampling Degrees of Freedom of the Ligand 1546.1.2 Scoring Ligand Poses 1566.2 Docking-Based Virtual Screening Flowchart 1586.2.1 Ligand Setup 1586.2.2 Protein Setup 1596.2.3 Docking 1606.2.4 Postdocking Analysis 1616.3 Recent Advances in Docking-Based VS Methods 1626.3.1 Novel Docking Algorithms 1626.3.2 Fragment Docking 1646.3.3 Postdocking Refinement 1646.3.4 Addressing Protein Flexibility 1666.3.5 Solvated or Dry? 1686.4 Future Trends in Docking 168References 169Part Two Challenges 1777 The Challenge of Affinity Prediction: Scoring Functions for Structure-Based Virtual Screening 179Christoph Sotriffer and Hans Matter7.1 Introduction 1797.2 Physicochemical Basis of Protein–Ligand Recognition 1807.3 Classes of Scoring Functions 1857.3.1 Force Field-Based Methods 1857.3.2 Empirical Scoring Functions 1897.3.3 Knowledge-Based Scoring Functions 1917.4 Interesting New Approaches to Scoring Functions 1927.4.1 Improved Treatment of Hydrophobicity and Dehydration 1927.4.2 Development and Validation of SFCscore 1947.4.3 Consensus Scoring 1957.4.4 Tailored Scoring Functions 1967.4.5 Structural Interaction Fingerprints 1997.5 Comparative Assessment of Scoring Functions 2007.6 Tailoring Scoring Strategies in Virtual Screening 2037.6.1 Toward a Strategy for Applying Scoring Functions 2037.6.2 Retrospective Validation Prior to Prospective Virtual Screening 2047.6.3 Lessons Learned: Improvements in Scoring Evaluations 2057.6.4 Postfiltering Results of Virtual Screenings 2057.7 Caveats for Development of Scoring Functions 2067.7.1 General Points 2067.7.2 Biological Data 2077.7.3 Structural Data on Protein–Ligand Complexes and Decoy Data Sets 2077.7.4 Cooperativity and Other Model Deficiencies 2087.8 Conclusions 209References 2108 Protein Flexibility in Structure-Based Virtual Screening: From Models to Algorithms 223Angela M. Henzler and Matthias Rarey8.1 How Flexible Are Proteins? – A Historical Perspective 2238.1.1 Ligand Binding Is Coupled with Protein Conformational Change 2238.1.2 Types of Flexibility 2248.2 Flexible Protein Handling in Protein–Ligand Docking 2258.2.1 Docking Following Conformational Selection 2278.2.2 Induced Fit Docking: Single-Structure-Based Docking Techniques 2318.2.3 Integrated Docking Approaches 2358.3 Flexible Protein Handling in Docking-Based Virtual Screening 2368.3.1 Efficiency of Fully Flexible Docking Approaches in Retrospective 2378.3.2 Discrimination of Binders and Nonbinders 2388.4 Summary 238References 2399 Handling Protein Flexibility in Docking and High-Throughput Docking: From Algorithms to Applications 245Claudio N. Cavasotto9.1 Introduction: Docking and High-Throughput Docking in Drug Discovery 2459.2 The Challenge of Accounting for Protein Flexibility in Docking 2469.2.1 Theoretical Understanding of the Problem 2469.2.2 Docking Failures Due to Protein Flexibility 2479.3 Accounting for Protein Flexibility in Docking-Based Drug Discovery and Design 2509.3.1 Receptor Ensemble-Based Docking Methods 2529.3.2 Single-Structure-Based Docking Methods 2539.3.3 Multilevel Methods 2569.3.4 Homology Modeling 2579.4 Conclusions 257References 25810 Consideration of Water and Solvation Effects in Virtual Screening 263Johannes Kirchmair, Gudrun M. Spitzer, and Klaus R. Liedl10.1 Introduction 26310.2 Experimental Approaches for Analyzing Water Molecules 26610.3 Computational Approaches for Analyzing Water Molecules 27110.3.1 Molecular Dynamics Simulations 27110.3.2 Empirical and Implicit Considerations of Solvation Effects 27410.4 Water-Sensitive Virtual Screening: Approaches and Applications 27510.4.1 Protein–Ligand Docking 27510.4.2 Pharmacophore Modeling 27810.5 Conclusions and Recommendations 281References 282Part Three Applications and Practical Guidelines 29111 Applied Virtual Screening: Strategies, Recommendations, and Caveats 293Dagmar Stumpfe and Jürgen Bajorath11.1 Introduction 29311.2 What Is Virtual Screening? 29311.3 Spectrum of Virtual Screening Approaches 29411.4 Molecular Similarity as a Foundation and Caveat of Virtual Screening 29511.5 Goals of Virtual Screening 29611.6 Applicability Domain 29711.7 Reference and Database Compounds 29911.8 Biological Activity versus Compound Potency 30011.9 Methodological Complexity and Compound Class Dependence 30111.10 Search Strategies and Compound Selection 30211.11 Virtual and High-Throughput Screening 30411.12 Practical Applications: An Overview 30611.13 LFA-1 Antagonist 30711.14 Selectivity Searching 31011.15 Concluding Remarks 314References 31512 Applications and Success Stories in Virtual Screening 319Hans Matter and Christoph Sotriffer12.1 Introduction 31912.2 Practical Considerations 32012.3 Successful Applications of Virtual Screening 32112.3.1 Structure-Based Virtual Screening 32212.3.2 Structure-Based Library Design 33612.3.3 Ligand-Based Virtual Screening 33812.4 Conclusion 347References 348Part Four Scenarios and Case Studies: Routes to Success 35913 Scenarios and Case Studies: Examples for Ligand-Based Virtual Screening 361Trevor Howe, Daniele Bemporad, and Gary Tresadern13.1 Introduction 36113.2 1D Ligand-Based Virtual Screening 36213.3 2D Ligand-Based Virtual Screening 36313.3.1 Examples from the Literature 36313.3.2 Applications at J&JPRD Europe 36613.4 3D Ligand-Based Virtual Screening 36813.4.1 Methods 37013.4.2 3DLBVS Examples 37213.5 Summary 376References 37714 Virtual Screening on Homology Models 381Róbert Kiss and György M. Keseru"14.1 Introduction 38114.2 Homology Models versus Crystal Structures: Comparative Evaluation of Screening Performance 38214.2.1 Soluble Proteins 38214.2.2 Membrane Proteins 39214.3 Challenges of Homology Model-Based Virtual Screening 39414.3.1 Level of Sequence Identity 39514.3.2 Main-Chain Flexibility 39614.3.3 Side-Chain Conformation: Induced Fit Effects of Ligands 39614.3.4 Loop Modeling 39714.4 Case Studies 39914.4.1 Virtual Screening on the Homology Model of Histamine H4 Receptor 39914.4.2 Virtual Screening on the Homology Model of Janus Kinase 2 402References 40415 Target-Based Virtual Screening on Small-Molecule Protein Binding Sites 411Ralf Heinke, Urszula Uciechowska, Manfred Jung, and Wolfgang Sippl15.1 Introduction 41115.1.1 Pharmacophore-Based Methods 41215.1.2 Ligand Docking 41215.1.3 Virtual Screening 41315.1.4 Binding Free Energy Calculations 41415.2 Structure-Based VS for Histone Arginine Methyltransferase PRMT1 Inhibitors 41415.2.1 Structure-Based VS of the NCI Diversity Set 41515.2.2 Pharmacophore-Based VS 41715.3 Identification of Nanomolar Histamine H3 Receptor Antagonists by Structure- and Pharmacophore-Based VS 42215.3.1 Generation of Homology Model of the hH3R and hH3R Antagonist Complexes 42315.3.2 Validation of the Homology Model by Docking Known Antagonists into the hH3R Binding Site 42415.3.3 Pharmacophore-Based VS 42515.3.4 Experimental Testing of the Identified Hits 42915.3.5 Discussion of the Applied VS Strategies 42915.4 Summary 431References 43216 Target-Based Virtual Screening to Address Protein–Protein Interfaces 435Olivier Sperandio, Maria A. Miteva, and Bruno O. Villoutreix16.1 Introduction 43516.2 Some Recent PPIM Success Stories 43716.3 Protein–Protein Interfaces 43816.3.1 Interface Pockets, Flexibility, and Hot Spots 44016.3.2 Databases and Tools to Analyze Interfaces 44216.4 PPIMs. Chemical Space and ADME/Tox Properties 44216.5 Drug Discovery, Chemical Biology, and In Silico Screening Methods: Overview and Suggestions for PPIM Search 44716.6 Case Studies 45016.6.1 PPI Stabilizers: Superoxide Dismutase Type 1 45016.6.2 PPI Inhibitors: Lck 45216.6.3 Allosteric Inhibitors: Antitrypsin Polymerization 45516.7 Conclusions and Future Directions 457References 45817 Fragment-Based Approaches in Virtual Screening 467Danzhi Huang and Amedeo Caflisch17.1 Introduction 46717.2 In Silico Fragment-Based Approaches 46817.3 Our Approach to High-Throughput Fragment-Based Docking 47017.3.1 Decomposition of Compounds into Fragments 47117.3.2 Docking of Anchor Fragments 47117.3.3 Flexible Docking of Library Compounds 47217.3.4 LIECE Binding Energy Evaluation 47217.3.5 Consensus Scoring 47517.3.6 In Silico Screening Campaigns 47517.3.7 West Nile Virus NS3 Protease (Flaviviral Infections) 47517.3.8 EphB4 Tyrosine Kinase (Cancer) 47717.4 Lessons Learned from Our Fragment-Based Docking 47917.5 Challenges of Fragment-Based Approaches 481References 482Appendix A: Software Overview 491Appendix B: Virtual Screening Application Studies 501Index 511

Christoph Sotriffer is Professor for Pharmaceutical Chemistry at the University ofWürzburg, Germany. He graduated as a chemist from the University of Innsbruck, Austria, where he obtained his PhD in 1999. After conducting postdoctoral research at the University of California, San Diego, USA, and the University of Marburg, Germany, he moved to the University ofWürzburg in 2006, where he has built a research group for computational medicinal chemistry. Besides structure-based drug design and virtual screening, his prime scientific interest is the computational analysis and prediction of protein-ligand interactions. His work was awarded by the Austrian Chemical Society GÖCH in 2005and the German Chemical and Pharmaceutical Societies GDCh and DPhG in 2007.

"Although mediocre quality and inconsistency of some of the figures and chemical structure drawings curbed my enthusiasm, I emphatically recommend this book to anyone who is avid of learning (more) about the current and future "state-of-the-science" in virtual screening." (Molecular Informatics, 2011) "The book is well suited both for all practitioners in medicinal chemistry and for graduate students who want to learn how to apply virtual screening methodology." (International Journal Bioautomation, 2011)"All scientists interested in the field will have an interest in reading it, whether for the bibliographic contents,examples cited or principles broached. Students will find out "how to do it", whatever their intent is, which willmake this volume a useful handbook. No need to be an expert in the field or a computer specialist to give it a try." (ChemMedChem, 2011)"This comprehensive and up-to-date review of the basic concepts and tools for virtual screening applications in drug discovery is part of the Methods and Principles in Medicinal Chemistry series, which has been a crucial source of information for medicinal chemists from both academia and pharmaceutical companies since 1993." (Doody's, 30 September 2011)"Virtual Screening is a comprehensive and up-to-date overview, this is both a desktop reference and practical guide for virtual screening applications in drug discovery". (Laboratory Journal, 18 January 2011)

• List of Contributors XVIIPreface XXIIIA Personal Foreword XXVPart One Principles 11 Virtual Screening of Chemical Space: From Generic Compound Collections to Tailored Screening Libraries 3Markus Boehm1.1 Introduction 31.2 Concepts of Chemical Space 41.3 Concepts of Druglikeness and Leadlikeness 61.4 Diversity-Based Libraries 81.4.1 Concepts of Molecular Diversity 81.4.2 Descriptor-Based Diversity Selection 91.4.3 Scaffold-Based Diversity Selection 121.4.4 Sources of Diversity 131.5 Focused Libraries 151.5.1 Concepts of Focused Design 151.5.2 Ligand-Based Focused Design 161.5.3 Structure-Based Focused Design 171.5.4 Chemogenomics Approaches 181.6 Virtual Combinatorial Libraries and Fragment Spaces 201.7 Databases of Chemical and Biological Information 211.8 Conclusions and Outlook 241.9 Glossary 25References 262 Preparing and Filtering Compound Databases for Virtual and Experimental Screening 35Maxwell D. Cummings, Éric Arnoult, Christophe Buyck, Gary Tresadern, Ann M. Vos, and Jörg K. Wegner2.1 Introduction 352.2 Ligand Databases 362.2.1 Chemical Data Structures 362.2.2 3D Conformations 382.2.3 Data Storage 392.2.4 Workflow Tools 392.2.5 Past Reviews and Recent Papers 402.3 Considering Physicochemical Properties 422.3.1 Druglikeness 422.3.2 Leadlikeness and Beyond 432.4 Undesirables 432.4.1 Screening Artifacts 442.4.2 Pharmacologically Promiscuous Compounds 452.5 Property-Based Filtering for Selected Targets 462.5.1 Antibacterials 472.5.2 CNS 492.5.3 Protein–Protein Interactions 512.6 Summary 52References 533 Ligand-Based Virtual Screening 61Herbert Koeppen, Jan Kriegl, Uta Lessel, Christofer S. Tautermann, and Bernd Wellenzohn3.1 Introduction 613.2 Descriptors 623.3 Search Databases and Queries 673.3.1 Selection of Reference Ligands 673.3.2 Preparation of the Search Database 683.4 Virtual Screening Techniques 683.4.1 Similarity Searches 693.4.2 Similarity Searches in Very Large Chemical Spaces 723.4.3 Machine Learning in Virtual Screening 743.4.4 Validation of Methods and Prediction of Success 783.5 Conclusions 79References 804 The Basis for Target-Based Virtual Screening: Protein Structures 87Jason C. Cole, Oliver Korb, Tjelvar S.G. Olsson, and John Liebeschuetz4.1 Introduction 874.2 Selecting a Protein Structure for Virtual Screening 874.2.1 Why Are There Errors in Crystal Structures? 874.2.2 Possible Problems That May Occur in a Crystal Structure 914.2.3 Structural Relevance 954.2.4 Critical Evaluation of Models: Recognizing Issues in Structures 984.3 Setting Up a Protein Model for vHTS 1014.3.1 Binding Site Definition 1014.3.2 Protonation 1044.3.3 Treatment of Solvent in Docking 1044.3.4 Use of Protein-Based Constraints in Docking 1054.3.5 Protein Flexibility 1064.4 Summary 1094.5 Glossary of Crystallographic Terms 1104.5.1 R-Factor 1104.5.2 Resolution 1104.5.3 2mFo-DFc Map 110References 1105 Pharmacophore Models for Virtual Screening 115Patrick Markt, Daniela Schuster, and Thierry Langer5.1 Introduction 1155.2 Compilation of Compounds 1165.2.1 Chemical Structure Generation 1165.2.2 Conformational Analysis 1165.3 Pharmacophore Model Generation 1175.3.1 State of the Art 1175.3.2 Structure-Based Methods 1175.3.3 Ligand-Based Methods 1185.3.4 Limitations of Ligand-Based Methods 1195.4 Validation of Pharmacophore Models 1195.4.1 Chemical Databases for Validation 1195.4.2 Enrichment Assessment 1215.4.3 Enrichment Metrics 1225.4.4 Receiver Operating Characteristic Curve Analysis 1245.4.5 Area Under the ROC Curve 1255.5 Pharmacophore-Based Screening 1275.5.1 DS CATALYST 1285.5.2 UNITY (GALAHAD/GASP) 1285.5.3 LIGANDSCOUT 1295.5.4 MOE 1305.5.5 PHASE 1305.6 Postprocessing of Pharmacophore-Based Screening Hits 1315.6.1 Lead- and Druglikeness 1315.6.2 Structural Similarity Analysis 1315.7 Pharmacophore-Based Parallel Screening 1325.8 Application Examples for Synthetic Compound Screening 1335.8.1 17b-Hydroxysteroid Dehydrogenase 1 Inhibitors 1335.8.2 Cannabinoid Receptor 2 (CB2) Ligands 1345.8.3 Further Application Examples 1365.9 Application Examples for Natural Product Screening 1365.9.1 Cyclooxygenase (COX) Inhibitors 1395.9.2 Sigma-1 (s1) Receptor Ligands 1395.9.3 Acetylcholinesterase Inhibitors 1405.9.4 Human Rhinovirus Coat Protein Inhibitors 1415.9.5 Quorum-Sensing Inhibitors 1415.9.6 Peroxisome Proliferator-Activated Receptor c Ligands 1415.9.7 b-Ketoacyl-Acyl Carrier Protein Synthase III Inhibitors 1425.9.8 5-Lipoxygenase Inhibitors 1425.9.9 11b-Hydroxysteroid Dehydrogenase Type 1 Inhibitors 1425.9.10 Pharmacophore-Based Parallel Screening of Natural Products 1435.10 Conclusions 143References 1446 Docking Methods for Virtual Screening: Principles and Recent Advances 153Didier Rognan6.1 Principles of Molecular Docking 1536.1.1 Sampling Degrees of Freedom of the Ligand 1546.1.2 Scoring Ligand Poses 1566.2 Docking-Based Virtual Screening Flowchart 1586.2.1 Ligand Setup 1586.2.2 Protein Setup 1596.2.3 Docking 1606.2.4 Postdocking Analysis 1616.3 Recent Advances in Docking-Based VS Methods 1626.3.1 Novel Docking Algorithms 1626.3.2 Fragment Docking 1646.3.3 Postdocking Refinement 1646.3.4 Addressing Protein Flexibility 1666.3.5 Solvated or Dry? 1686.4 Future Trends in Docking 168References 169Part Two Challenges 1777 The Challenge of Affinity Prediction: Scoring Functions for Structure-Based Virtual Screening 179Christoph Sotriffer and Hans Matter7.1 Introduction 1797.2 Physicochemical Basis of Protein–Ligand Recognition 1807.3 Classes of Scoring Functions 1857.3.1 Force Field-Based Methods 1857.3.2 Empirical Scoring Functions 1897.3.3 Knowledge-Based Scoring Functions 1917.4 Interesting New Approaches to Scoring Functions 1927.4.1 Improved Treatment of Hydrophobicity and Dehydration 1927.4.2 Development and Validation of SFCscore 1947.4.3 Consensus Scoring 1957.4.4 Tailored Scoring Functions 1967.4.5 Structural Interaction Fingerprints 1997.5 Comparative Assessment of Scoring Functions 2007.6 Tailoring Scoring Strategies in Virtual Screening 2037.6.1 Toward a Strategy for Applying Scoring Functions 2037.6.2 Retrospective Validation Prior to Prospective Virtual Screening 2047.6.3 Lessons Learned: Improvements in Scoring Evaluations 2057.6.4 Postfiltering Results of Virtual Screenings 2057.7 Caveats for Development of Scoring Functions 2067.7.1 General Points 2067.7.2 Biological Data 2077.7.3 Structural Data on Protein–Ligand Complexes and Decoy Data Sets 2077.7.4 Cooperativity and Other Model Deficiencies 2087.8 Conclusions 209References 2108 Protein Flexibility in Structure-Based Virtual Screening: From Models to Algorithms 223Angela M. Henzler and Matthias Rarey8.1 How Flexible Are Proteins? – A Historical Perspective 2238.1.1 Ligand Binding Is Coupled with Protein Conformational Change 2238.1.2 Types of Flexibility 2248.2 Flexible Protein Handling in Protein–Ligand Docking 2258.2.1 Docking Following Conformational Selection 2278.2.2 Induced Fit Docking: Single-Structure-Based Docking Techniques 2318.2.3 Integrated Docking Approaches 2358.3 Flexible Protein Handling in Docking-Based Virtual Screening 2368.3.1 Efficiency of Fully Flexible Docking Approaches in Retrospective 2378.3.2 Discrimination of Binders and Nonbinders 2388.4 Summary 238References 2399 Handling Protein Flexibility in Docking and High-Throughput Docking: From Algorithms to Applications 245Claudio N. Cavasotto9.1 Introduction: Docking and High-Throughput Docking in Drug Discovery 2459.2 The Challenge of Accounting for Protein Flexibility in Docking 2469.2.1 Theoretical Understanding of the Problem 2469.2.2 Docking Failures Due to Protein Flexibility 2479.3 Accounting for Protein Flexibility in Docking-Based Drug Discovery and Design 2509.3.1 Receptor Ensemble-Based Docking Methods 2529.3.2 Single-Structure-Based Docking Methods 2539.3.3 Multilevel Methods 2569.3.4 Homology Modeling 2579.4 Conclusions 257References 25810 Consideration of Water and Solvation Effects in Virtual Screening 263Johannes Kirchmair, Gudrun M. Spitzer, and Klaus R. Liedl10.1 Introduction 26310.2 Experimental Approaches for Analyzing Water Molecules 26610.3 Computational Approaches for Analyzing Water Molecules 27110.3.1 Molecular Dynamics Simulations 27110.3.2 Empirical and Implicit Considerations of Solvation Effects 27410.4 Water-Sensitive Virtual Screening: Approaches and Applications 27510.4.1 Protein–Ligand Docking 27510.4.2 Pharmacophore Modeling 27810.5 Conclusions and Recommendations 281References 282Part Three Applications and Practical Guidelines 29111 Applied Virtual Screening: Strategies, Recommendations, and Caveats 293Dagmar Stumpfe and Jürgen Bajorath11.1 Introduction 29311.2 What Is Virtual Screening? 29311.3 Spectrum of Virtual Screening Approaches 29411.4 Molecular Similarity as a Foundation and Caveat of Virtual Screening 29511.5 Goals of Virtual Screening 29611.6 Applicability Domain 29711.7 Reference and Database Compounds 29911.8 Biological Activity versus Compound Potency 30011.9 Methodological Complexity and Compound Class Dependence 30111.10 Search Strategies and Compound Selection 30211.11 Virtual and High-Throughput Screening 30411.12 Practical Applications: An Overview 30611.13 LFA-1 Antagonist 30711.14 Selectivity Searching 31011.15 Concluding Remarks 314References 31512 Applications and Success Stories in Virtual Screening 319Hans Matter and Christoph Sotriffer12.1 Introduction 31912.2 Practical Considerations 32012.3 Successful Applications of Virtual Screening 32112.3.1 Structure-Based Virtual Screening 32212.3.2 Structure-Based Library Design 33612.3.3 Ligand-Based Virtual Screening 33812.4 Conclusion 347References 348Part Four Scenarios and Case Studies: Routes to Success 35913 Scenarios and Case Studies: Examples for Ligand-Based Virtual Screening 361Trevor Howe, Daniele Bemporad, and Gary Tresadern13.1 Introduction 36113.2 1D Ligand-Based Virtual Screening 36213.3 2D Ligand-Based Virtual Screening 36313.3.1 Examples from the Literature 36313.3.2 Applications at J&JPRD Europe 36613.4 3D Ligand-Based Virtual Screening 36813.4.1 Methods 37013.4.2 3DLBVS Examples 37213.5 Summary 376References 37714 Virtual Screening on Homology Models 381Róbert Kiss and György M. Keseru"14.1 Introduction 38114.2 Homology Models versus Crystal Structures: Comparative Evaluation of Screening Performance 38214.2.1 Soluble Proteins 38214.2.2 Membrane Proteins 39214.3 Challenges of Homology Model-Based Virtual Screening 39414.3.1 Level of Sequence Identity 39514.3.2 Main-Chain Flexibility 39614.3.3 Side-Chain Conformation: Induced Fit Effects of Ligands 39614.3.4 Loop Modeling 39714.4 Case Studies 39914.4.1 Virtual Screening on the Homology Model of Histamine H4 Receptor 39914.4.2 Virtual Screening on the Homology Model of Janus Kinase 2 402References 40415 Target-Based Virtual Screening on Small-Molecule Protein Binding Sites 411Ralf Heinke, Urszula Uciechowska, Manfred Jung, and Wolfgang Sippl15.1 Introduction 41115.1.1 Pharmacophore-Based Methods 41215.1.2 Ligand Docking 41215.1.3 Virtual Screening 41315.1.4 Binding Free Energy Calculations 41415.2 Structure-Based VS for Histone Arginine Methyltransferase PRMT1 Inhibitors 41415.2.1 Structure-Based VS of the NCI Diversity Set 41515.2.2 Pharmacophore-Based VS 41715.3 Identification of Nanomolar Histamine H3 Receptor Antagonists by Structure- and Pharmacophore-Based VS 42215.3.1 Generation of Homology Model of the hH3R and hH3R Antagonist Complexes 42315.3.2 Validation of the Homology Model by Docking Known Antagonists into the hH3R Binding Site 42415.3.3 Pharmacophore-Based VS 42515.3.4 Experimental Testing of the Identified Hits 42915.3.5 Discussion of the Applied VS Strategies 42915.4 Summary 431References 43216 Target-Based Virtual Screening to Address Protein–Protein Interfaces 435Olivier Sperandio, Maria A. Miteva, and Bruno O. Villoutreix16.1 Introduction 43516.2 Some Recent PPIM Success Stories 43716.3 Protein–Protein Interfaces 43816.3.1 Interface Pockets, Flexibility, and Hot Spots 44016.3.2 Databases and Tools to Analyze Interfaces 44216.4 PPIMs. Chemical Space and ADME/Tox Properties 44216.5 Drug Discovery, Chemical Biology, and In Silico Screening Methods: Overview and Suggestions for PPIM Search 44716.6 Case Studies 45016.6.1 PPI Stabilizers: Superoxide Dismutase Type 1 45016.6.2 PPI Inhibitors: Lck 45216.6.3 Allosteric Inhibitors: Antitrypsin Polymerization 45516.7 Conclusions and Future Directions 457References 45817 Fragment-Based Approaches in Virtual Screening 467Danzhi Huang and Amedeo Caflisch17.1 Introduction 46717.2 In Silico Fragment-Based Approaches 46817.3 Our Approach to High-Throughput Fragment-Based Docking 47017.3.1 Decomposition of Compounds into Fragments 47117.3.2 Docking of Anchor Fragments 47117.3.3 Flexible Docking of Library Compounds 47217.3.4 LIECE Binding Energy Evaluation 47217.3.5 Consensus Scoring 47517.3.6 In Silico Screening Campaigns 47517.3.7 West Nile Virus NS3 Protease (Flaviviral Infections) 47517.3.8 EphB4 Tyrosine Kinase (Cancer) 47717.4 Lessons Learned from Our Fragment-Based Docking 47917.5 Challenges of Fragment-Based Approaches 481References 482Appendix A: Software Overview 491Appendix B: Virtual Screening Application Studies 501Index 511