Virtual screening is commonly associated with methodologies that rely on the explicit knowledge of 3D structure of the target protein to identify potential bioactive compounds. This book is the first monograph to summarize the innovative applications of efficient chemoinformatics approaches towards screening large chemical libraries. The focus on virtual screening expands chemoinformatics beyond its traditional boundaries as a synthetic and data-analytical area of research towards its recognition as a predictive and decision-support scientific discipline. The monograph covers chemoinformatics…mehr
Virtual screening is commonly associated with methodologies that rely on the explicit knowledge of 3D structure of the target protein to identify potential bioactive compounds. This book is the first monograph to summarize the innovative applications of efficient chemoinformatics approaches towards screening large chemical libraries. The focus on virtual screening expands chemoinformatics beyond its traditional boundaries as a synthetic and data-analytical area of research towards its recognition as a predictive and decision-support scientific discipline. The monograph covers chemoinformatics approaches applicable to virtual screening of very large collections of chemical compounds to identify novel biologically active molecules. The discussed approaches rely on chemoinformatics concepts such as the representation of molecules using multiple descriptors of chemical structures, advanced chemical similarity calculations in multidimensional descriptor spaces, and machine learning and data mining approaches. Ligand-based approaches, which are in the focus of this work, are more computationally efficient compared to structure-based virtual screening. Chemoinformatics: An Approach to Virtual Screening opens with the overview of modern methods of compounds library design, followed by a chapter devoted to molecular similarity analysis. Four sections describe virtual screening based on the using of molecular fragments, 2D pharmacophores and 3D pharmacophores. Application of fuzzy pharmacophores for libraries design is the subject of the next chapter, followed by a chapter dealing with QSAR studies based on local molecular parameters. Probabilistic approaches based on 2D descriptors in the assessment of biological activities are also described with an overview of the modern methods and software for ADME prediction. The final chapter describes the new approach of coding the receptor binding sites and their respective ligands in multidimensional chemical descriptor space which affords an interesting and efficient alternative to traditional docking and screening techniques. The focus on extending the experiences accumulated in traditional areas of chemoinformatics research such as Quantitative Structure Activity Relationships (QSAR) or chemical similarity searching towards virtual screening make the theme of this monograph essential reading for researchers in the area of computer-aided drug discovery. Due to its generic data-analytical focus there will be a growing application of chemoinformatics approaches in multiple areas of chemical and biological research such as synthesis planning, nanotechnology, proteomics, physical and analytical chemistry and chemical genomics.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Alexandre Varnek is Professor in Theoretical Chemistry at the Louis Pasteur University (ULP) France, and Head of the Laboratory of Chemoinformatics, Director of Master Courses on Chemoinformatics at the Faculty of Chemistry, ULP. He has 30 years experience in the fields of molecular modelling and chemoinformatics and more than 80 publications including a monograph. His current research projects include the development of new approaches and software tools for in silico design of new compounds. Alexander Tropsha is Head of the Laboratory for Molecular Modeling, School of Pharmacy at the University of North Carolina, Chapel Hill, USA as well as Professor and Chair, Division of Medicinal Chemistry and Natural Products at the School of Pharmacy. His research interests include Computer-Aided Drug Design, Chemoinformatics, and Structural Bioinformatics. He has authored or co-authored over 110 peer-reviewed research papers and book chapters.
Inhaltsangabe
Preface 1 - Fragment Descriptors in SAR/QSAR/QSPR studies, molecular similarity analysis and in virtual screening Introduction Historical survey Main characteristics of Fragment Descriptors Types of Fragments Simple Fixed Types WLN and SMILES Fragments Atom-Centered Fragments Bond-Centered Fragments Maximum Common Substructures Atom Pairs and Topological Multiplets Substituents and Molecular Frameworks Basic Subgraphs Mined Subgraphs Random Subgraphs Library Subgraphs Fragments describing supramolecular systems and chemical reactions Storage of fragments' information Fragment's Connectivity Generic Graphs Labeling Atoms Application in Virtual Screening and In Silico Design Filtering Similarity Search SAR Classification (Probabilistic) Models QSAR/QSPR Regression Models In Silico Design Limitations of Fragment Descriptors Conclusion 2 - Topological Pharmacophores Introduction 3D pharmacophore models and descriptors Topological pharmacophores Topological pharmacophores from 2D-aligments Topological pharmacophores from 2D pharmacophore fingerprints Topological index-based 'pharmacophores'? Topological pharmacophores from 2D-aligments Topological pharmacophores from pharmacophore fingerprints Topological pharmacophore pair fingerprints Topological pharmacophore triplets Similarity searching with pharmacophore fingerprints - Technical Issues Similarity searching with pharmacophore fingerprints - Some Examples Machine-learning of Topological Pharmacophores from Fingerprints Topological index-based 'pharmacophores'? Conclusions 3 - Pharmacophore-based Virtual Screening in Drug Discovery Introduction Virtual Screening Methods Chemical Feature-based Pharmacophores The Term "3D Pharmacophore" Feature Definitions and Pharmacophore Representation Hydrogen bonding interactions Lipophilic areas Aromatic interactions Charge-transfer interactions Customization and definition of new features Current super-positioning techniques for aligning 3D pharmacophores and molecules Generation and Use of Pharmacophore Models Ligand-based Pharmacophore Modeling Structure-based Pharmacophore Modeling Inclusion of Shape Information Qualitative vs. Quantitative Pharmacophore Models Validation of Models for Virtual Screening Application of Pharmacophore Models in Virtual Screening Pharmacophore Models as Part of a Multi-Step Screening Approach Antitarget and ADME(T) Screening Using Pharmacophores Pharmacophore Models for Activity Profiling and Parallel Virtual Screening Pharmacophore Method Extensions and Comparisons to Other Virtual Screening Methods Topological Fingerprints Shape-based Virtual Screening Docking Methods Pharmacophore Constraints Used in Docking Further Reading Summary and Conclusion 4 - Molecular Similarity Analysis in Virtual Screening Ligand-Based Virtual Screening Foundations of Molecular Similarity Analysis Molecular Similarity and Chemical Spaces Similarity Measures Activity Landscapes Analyzing the Nature of Structure-Activity Relationships Relationships between different SARs SARs and target-ligand interactions Qualitative SAR characterization Quantitative SAR characterization Implications for molecular similarity analysis and virtual screening Strengths and Limitations of Similarity Methods Conclusion and Future Perspectives 5 - Molecular Field Topology Analysis in drug design and virtual screening Introduction: local molecular parameters in QSAR, drug design and virtual screening Supergraph-based QSAR models Rationale and history Molecular Field Topology Analysis (MFTA) General principles Local molecular descriptors: facets of ligand-biotarget interaction Construction of molecular supergraph Formation of descriptor matrix Statistical analysis Applicability control From MFTA model to drug design and virtual screening MFTA models in biotarget and drug action analysis MFTA models in virtual screening MFTA-based virtual screening of compound databases MFTA-based virtual screening of generated structure libraries Conclusion 6 - Probabilistic approaches in activity prediction Introduction Biological Activity Dose-Effect Relationships Experimental Data Probabilistic Ligand-Based Virtual Screening Methods Preparation of Training Sets Creation of Evaluation Sets Mathematical Approaches Evaluation of Prediction Accuracy Single-Targeted vs. Multi-Targeted Virtual Screening PASS Approach Biological Activities Predicted by PASS Chemical Structure Description in PASS SAR Base Algorithm of Activity Spectrum Estimation Interpretation of Prediction Results Selection of the Most Prospective Compounds Conclusions 7 - Fragment-based de novo design of druglike molecules Introduction From Molecules to Fragments From Fragments to Molecules Scoring the Design Conclusions and Outlook 8 - Early ADME/T predictions: a toy or a tool? Introduction Which properties are important for early drug discovery? Physico-chemical profiling Lipophilicity Solubility Data availability and accuracy Models Why models don't work: the challenge of the Applicability Domain AD based on similarity in the descriptor space AD based on similarity in the property-based space How reliable are predictions of physico-chemical properties? Available Data for ADME/T biological properties Absorption Data Models Distribution Data Models The usefulness of ADME/T models is limited by available data Conclusions 9 - Compound Library Design - Principles and Applications Introduction to Compound Library Design Methods for Compound Library Design Design for Specific Biological Activities Similarity Guided Design of Targeted Libraries Diversity Based Design of General Screening Libraries Pharmacophore Guided Design of Focused Compound Libraries QSAR Based Targeted Library Design Protein Structure Based Methods for Compound Library Design Design for Developability or Drug-likeness Rule & Alert Based Approaches QSAR Based ADMET Models Undesirable Functionality Filters Design for Multiple Objectives and Targets Simultaneously Concluding Remarks 10 - Integrated Chemo- and Bioinformatics Approaches to Virtual Screening Introduction Availability of large compound collections for virtual screening NIH Molecular Libraries Roadmap Initiative and the PubChem database Other chemical databases in public domain Structure based virtual screening Major methodologies Challenges and limitations of current approaches The implementation of cheminformatics concepts in structure based virtual screening Predictive QSAR models as virtual screening tools Critical Importance of model validation Applicability domains and QSAR model acceptability criteria Predictive QSAR modeling workflow Examples of application Structure based chemical descriptors of protein ligand interface: the EnTESS method Derivation of the EnTESS descriptors Validation of the EnTESS descriptors for binding affinity prediction Structure based cheminformatics approach to virtual screening: the CoLiBRI method The representation of three-dimensional active sites in multidimensional chemistry space The mapping between chemistry spaces of active sites and ligands Summary and Conclusions
Preface 1 - Fragment Descriptors in SAR/QSAR/QSPR studies, molecular similarity analysis and in virtual screening Introduction Historical survey Main characteristics of Fragment Descriptors Types of Fragments Simple Fixed Types WLN and SMILES Fragments Atom-Centered Fragments Bond-Centered Fragments Maximum Common Substructures Atom Pairs and Topological Multiplets Substituents and Molecular Frameworks Basic Subgraphs Mined Subgraphs Random Subgraphs Library Subgraphs Fragments describing supramolecular systems and chemical reactions Storage of fragments' information Fragment's Connectivity Generic Graphs Labeling Atoms Application in Virtual Screening and In Silico Design Filtering Similarity Search SAR Classification (Probabilistic) Models QSAR/QSPR Regression Models In Silico Design Limitations of Fragment Descriptors Conclusion 2 - Topological Pharmacophores Introduction 3D pharmacophore models and descriptors Topological pharmacophores Topological pharmacophores from 2D-aligments Topological pharmacophores from 2D pharmacophore fingerprints Topological index-based 'pharmacophores'? Topological pharmacophores from 2D-aligments Topological pharmacophores from pharmacophore fingerprints Topological pharmacophore pair fingerprints Topological pharmacophore triplets Similarity searching with pharmacophore fingerprints - Technical Issues Similarity searching with pharmacophore fingerprints - Some Examples Machine-learning of Topological Pharmacophores from Fingerprints Topological index-based 'pharmacophores'? Conclusions 3 - Pharmacophore-based Virtual Screening in Drug Discovery Introduction Virtual Screening Methods Chemical Feature-based Pharmacophores The Term "3D Pharmacophore" Feature Definitions and Pharmacophore Representation Hydrogen bonding interactions Lipophilic areas Aromatic interactions Charge-transfer interactions Customization and definition of new features Current super-positioning techniques for aligning 3D pharmacophores and molecules Generation and Use of Pharmacophore Models Ligand-based Pharmacophore Modeling Structure-based Pharmacophore Modeling Inclusion of Shape Information Qualitative vs. Quantitative Pharmacophore Models Validation of Models for Virtual Screening Application of Pharmacophore Models in Virtual Screening Pharmacophore Models as Part of a Multi-Step Screening Approach Antitarget and ADME(T) Screening Using Pharmacophores Pharmacophore Models for Activity Profiling and Parallel Virtual Screening Pharmacophore Method Extensions and Comparisons to Other Virtual Screening Methods Topological Fingerprints Shape-based Virtual Screening Docking Methods Pharmacophore Constraints Used in Docking Further Reading Summary and Conclusion 4 - Molecular Similarity Analysis in Virtual Screening Ligand-Based Virtual Screening Foundations of Molecular Similarity Analysis Molecular Similarity and Chemical Spaces Similarity Measures Activity Landscapes Analyzing the Nature of Structure-Activity Relationships Relationships between different SARs SARs and target-ligand interactions Qualitative SAR characterization Quantitative SAR characterization Implications for molecular similarity analysis and virtual screening Strengths and Limitations of Similarity Methods Conclusion and Future Perspectives 5 - Molecular Field Topology Analysis in drug design and virtual screening Introduction: local molecular parameters in QSAR, drug design and virtual screening Supergraph-based QSAR models Rationale and history Molecular Field Topology Analysis (MFTA) General principles Local molecular descriptors: facets of ligand-biotarget interaction Construction of molecular supergraph Formation of descriptor matrix Statistical analysis Applicability control From MFTA model to drug design and virtual screening MFTA models in biotarget and drug action analysis MFTA models in virtual screening MFTA-based virtual screening of compound databases MFTA-based virtual screening of generated structure libraries Conclusion 6 - Probabilistic approaches in activity prediction Introduction Biological Activity Dose-Effect Relationships Experimental Data Probabilistic Ligand-Based Virtual Screening Methods Preparation of Training Sets Creation of Evaluation Sets Mathematical Approaches Evaluation of Prediction Accuracy Single-Targeted vs. Multi-Targeted Virtual Screening PASS Approach Biological Activities Predicted by PASS Chemical Structure Description in PASS SAR Base Algorithm of Activity Spectrum Estimation Interpretation of Prediction Results Selection of the Most Prospective Compounds Conclusions 7 - Fragment-based de novo design of druglike molecules Introduction From Molecules to Fragments From Fragments to Molecules Scoring the Design Conclusions and Outlook 8 - Early ADME/T predictions: a toy or a tool? Introduction Which properties are important for early drug discovery? Physico-chemical profiling Lipophilicity Solubility Data availability and accuracy Models Why models don't work: the challenge of the Applicability Domain AD based on similarity in the descriptor space AD based on similarity in the property-based space How reliable are predictions of physico-chemical properties? Available Data for ADME/T biological properties Absorption Data Models Distribution Data Models The usefulness of ADME/T models is limited by available data Conclusions 9 - Compound Library Design - Principles and Applications Introduction to Compound Library Design Methods for Compound Library Design Design for Specific Biological Activities Similarity Guided Design of Targeted Libraries Diversity Based Design of General Screening Libraries Pharmacophore Guided Design of Focused Compound Libraries QSAR Based Targeted Library Design Protein Structure Based Methods for Compound Library Design Design for Developability or Drug-likeness Rule & Alert Based Approaches QSAR Based ADMET Models Undesirable Functionality Filters Design for Multiple Objectives and Targets Simultaneously Concluding Remarks 10 - Integrated Chemo- and Bioinformatics Approaches to Virtual Screening Introduction Availability of large compound collections for virtual screening NIH Molecular Libraries Roadmap Initiative and the PubChem database Other chemical databases in public domain Structure based virtual screening Major methodologies Challenges and limitations of current approaches The implementation of cheminformatics concepts in structure based virtual screening Predictive QSAR models as virtual screening tools Critical Importance of model validation Applicability domains and QSAR model acceptability criteria Predictive QSAR modeling workflow Examples of application Structure based chemical descriptors of protein ligand interface: the EnTESS method Derivation of the EnTESS descriptors Validation of the EnTESS descriptors for binding affinity prediction Structure based cheminformatics approach to virtual screening: the CoLiBRI method The representation of three-dimensional active sites in multidimensional chemistry space The mapping between chemistry spaces of active sites and ligands Summary and Conclusions
Es gelten unsere Allgemeinen Geschäftsbedingungen: www.buecher.de/agb
Impressum
www.buecher.de ist ein Internetauftritt der buecher.de internetstores GmbH
Geschäftsführung: Monica Sawhney | Roland Kölbl | Günter Hilger
Sitz der Gesellschaft: Batheyer Straße 115 - 117, 58099 Hagen
Postanschrift: Bürgermeister-Wegele-Str. 12, 86167 Augsburg
Amtsgericht Hagen HRB 13257
Steuernummer: 321/5800/1497
USt-IdNr: DE450055826
