Chemoinformatics approaches to virtual screening
This book is the first monograph that summarizes innovative applications of efficient chemoinformatics approaches towards the goal of screening large chemical libraries. The focus on virtual screening expands chemoinformatics beyond its traditional boundaries as a synthetic and data-analytical area...
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| Main Authors | , |
|---|---|
| Format | eBook Book |
| Language | English |
| Published |
Cambridge
RSC Pub
2008
NBN International Royal Society of Chemistry (RSC) Royal Society of Chemistry, The |
| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9780854041442 0854041443 9781847558879 1847558879 |
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| Abstract | This book is the first monograph that summarizes innovative applications of efficient chemoinformatics approaches towards the goal of 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 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. However, 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. |
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| AbstractList | This book is the first monograph that summarizes innovative applications of efficient chemoinformatics approaches towards the goal of 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 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. However, 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. Focuses on chemoinformatics approaches applicable to virtual screening of very large available collections of chemical compounds to identify novel biologically active molecules. |
| Author | Varnek, Alexandre Tropsha, Alex |
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| BackLink | https://cir.nii.ac.jp/crid/1130282269585053952$$DView record in CiNii |
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| Notes | 0854041443 (hbk.) 9780854041442 (hbk.) Includes bibliographical references and index |
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| Snippet | This book is the first monograph that summarizes innovative applications of efficient chemoinformatics approaches towards the goal of screening large chemical... Focuses on chemoinformatics approaches applicable to virtual screening of very large available collections of chemical compounds to identify novel biologically... |
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| SubjectTerms | Analytical Chemistry Cheminformatics Chemistry Chemistry & Chemical Engineering Chemistry, Pharmaceutical -- methods Combinatorial Chemistry Techniques -- methods Computational Biology -- methods Computer simulation Computer-Aided Design Data processing Design Drug Discovery -- methods Drugs High throughput screening (Drug development) Quantitative Structure-Activity Relationship |
| SubjectTermsDisplay | Chemistry |
| TableOfContents | Chemoinformatics approaches to virtual screening -- Preface -- Contents -- Chapter 1. Fragment Descriptors in SAR/QSAR/QSPR Studies, Molecular Similarity Analysis and in Virtual Screening -- Chapter 2. Topological Pharmacophores -- Chapter 3. Pharmacophore-based Virtual Screening in Drug Discovery -- Chapter 4. Molecular Similarity Analysis in Virtual Screening -- Chapter 5. Molecular Field Topology Analysis in Drug Design and Virtual Screening -- Chapter 6. Probabilistic Approaches in Activity Prediction -- Chapter 7. Fragment-based De Novo Design of Drug-like Molecules -- Chapter 8. Early ADME/T Predictions: Toy or Tool? -- Chapter 9. Compound Library Design - Principles and Applications -- Chapter 10. Integrated Chemo- and Bioinformatics Approaches to Virtual Screening -- Subject Index Front Matter Preface Table of Contents 1. Fragment Descriptors in SAR/QSAR/QSPR Studies, Molecular Similarity Analysis and in Virtual Screening 2. Topological Pharmacophores 3. Pharmacophore-Based Virtual Screening in Drug Discovery 4. Molecular Similarity Analysis in Virtual Screening 5. Molecular Field Topology Analysis in Drug Design and Virtual Screening 6. Probabilistic Approaches in Activity Prediction 7. Fragment-Based De Novo Design of Drug-Like Molecules 8. Early ADME/T Predictions: Toy or Tool? 9. Compound Library Design - Principles and Applications 10. Integrated Chemo- and Bioinformatics Approaches to Virtual Screening Subject Index 3.1 Introduction -- 3.2 Virtual Screening Methods -- 3.3 Chemical Feature-based Pharmacophores -- 3.3.1 The Term "3D Pharmacophore" -- 3.3.2 Feature Definitions and Pharmacophore Representation -- 3.4 Generation and Use of Pharmacophore Models -- 3.4.1 Ligand-based Pharmacophore Modeling -- 3.4.2 Structure-based Pharmacophore Modeling -- 3.4.3 Inclusion of Shape Information -- 3.4.4 Qualitative vs. Quantitative Pharmacophore Models -- References -- Chapter 4 Molecular Similarity Analysis in Virtual Screening -- 4.1 Introduction -- 4.2 Ligand-based Virtual Screening -- 4.3 Foundations of Molecular Similarity Analysis -- 4.3.1 Molecular Similarity and Chemical Spaces -- 4.3.2 Similarity Measures -- 4.3.3 Activity Landscapes -- 4.3.4 Analyzing the Nature of Structure-Activity Relationships -- 4.4 Strengths and Limitations of Similarity Methods -- 4.5 Conclusion and Future Perspectives -- References -- Chapter 5 Molecular Field Topology Analysis in Drug Design and Virtual Screening -- 5.1 Introduction: Local Molecular Parameters in QSAR, Drug Design and Virtual Screening -- 5.2 Supergraph-based QSAR Models -- 5.2.1 Rationale and History -- 5.2.2 Molecular Field Topology Analysis (MFTA) -- 5.3 From MFTA Model to Drug Design and Virtual Screening -- 5.3.1 MFTA Models in Biotarget and Drug Action Analysis -- 5.3.2 MFTA Models in Virtual Screening -- 5.4 Conclusion -- Acknowledgements -- References -- Chapter 6 Probabilistic Approaches in Activity Prediction -- 6.1 Introduction -- 6.2 Biological Activity -- 6.2.1 Dose-Effect Relationships -- 6.2.2 Experimental Data -- 6.3 Probabilistic Ligand-based Virtual Screening Methods -- 6.3.1 Preparation of Training Sets -- 6.3.2 Creation of Evaluation Sets -- 6.3.3 Mathematical Approaches -- 6.3.4 Evaluation of Prediction Accuracy -- 6.3.5 Single-targeted vs. Multi-targeted Virtual Screening -- 6.4 PASS Approach 6.4.1 Biological Activities Predicted by PASS -- 6.4.2 Chemical Structure Description in PASS -- 6.4.3 SAR Base -- 6.4.4 Algorithm of Activity Spectrum Estimation -- 6.4.5 Interpretation of Prediction Results -- 6.4.6 Selection of the Most Prospective Compounds -- 6.5 Conclusions -- References -- Chapter 7 Fragment-based De Novo Design of Drug-like Molecules -- 7.1 Introduction -- 7.2 From Molecules to Fragments -- 7.2.1 Pseudo-retrosynthesis -- 7.2.2 Shape-derived Fragment Definition -- 7.3 From Fragments to Molecules -- 7.4 Scoring the Design -- 7.5 Conclusions and Outlook -- Acknowledgements -- References -- Chapter 8 Early ADME/T Predictions: Toy or Tool? -- 8.1 Introduction -- 8.2 Which Properties are Important for Early Drug Discovery? -- 8.2.1 Pfizer -- 8.2.2 Abbot -- 8.2.3 Novartis -- 8.2.4 Bayer -- 8.2.5 Inpharmatica -- 8.3 Physicochemical Profiling -- 8.3.1 Lipophilicity -- 8.3.2 Solubility -- 8.4 Why Predictions Fail: The Applicability Domain Challenge -- 8.4.1 AD Based on Similarity in the Descriptor Space -- 8.4.2 AD Based on Similarity in the Property-based Space -- 8.4.3 How Reliable are Physicochemical Property Predictions? -- 8.5 Available Data for ADME/T Biological Properties -- 8.5.1 Absorption -- 8.5.2 Distribution -- 8.6 The Usefulness of ADME/T Models is Limited by the Available Data -- 8.7 Conclusions -- Acknowledgements -- References -- Chapter 9 Compound Library Design - Principles and Applications -- 9.1 Introduction -- 9.1.1 Compound Library Design -- 9.2 Methods for Compound Library Design -- 9.2.1 Design for Specific Biological Activities -- 9.2.2 Design for Developability or Drug-likeness -- 9.2.3 Design for Multiple Objectives and Targets Simultaneously -- 9.3 Concluding Remarks -- References -- Chapter 10 Integrated Chemo- and Bioinformatics Approaches to Virtual Screening -- 10.1 Introduction Chemoinformatics Approaches to Virtual Screening -- Contents -- Chapter 1 Fragment Descriptors in SAR/QSAR/QSPR Studies, Molecular Similarity Analysis and in Virtual Screening -- 1.1 Introduction -- 1.2 Historical Survey -- 1.3 Main Characteristics of Fragment Descriptors -- 1.3.1 Types of Fragments -- 1.3.2 Fragments Describing Supramolecular Systems and Chemical Reactions -- 1.3.3 Storage of Fragment Information -- 1.3.4 Fragment Connectivity -- 1.3.5 Generic Graphs -- 1.3.6 Labeling Atoms -- 1.4 Application in Virtual Screening and In Silico Design -- 1.4.1 Filtering -- 1.4.2 Similarity Search -- 1.4.3 SAR Classification (Probabilistic) Models -- 1.4.4 QSAR/QSPR Regression Models -- 1.4.5 In Silico Design -- 1.5 Limitations of Fragment Descriptors -- 1.6 Conclusion -- Acknowledgements -- References -- Chapter 2 Topological Pharmacophores -- 2.1 Introduction -- 2.1.1 3D Pharmacophore Models and Descriptors -- 2.1.2 Topological Pharmacophores -- 2.2 Topological Pharmacophores from 2D-Aligments -- 2.3 Topological Pharmacophores from Pharmacophore Fingerprints -- 2.3.1 Topological Pharmacophore Pair Fingerprints -- 2.3.2 Topological Pharmacophore Triplets -- 2.3.3 Similarity Searching with Pharmacophore Fingerprints - Technical Issues -- 2.3.4 Similarity Searching with Pharmacophore Fingerprints - Some Examples -- 2.3.5 Machine-learning of Topological Pharmacophores from Fingerprints -- 2.4 Topological Index-based "Pharmacophores"? -- 2.5 Conclusions -- 2.5.1 How Important is 3D Modeling for Pharmacophore Characterization? -- 2.5.2 2D Pharmacophore Fingerprints are Mainstream Chemoinformatics Tools, whereas 2D Pharmacophore Elucidation has been Rarely Attempted -- 2.5.3 Each QSAR Problem should be Allowed to Choose its Descriptors of Predilection -- Abbreviations -- References -- Chapter 3 Pharmacophore-based Virtual Screening in Drug Discovery 10.2 Availability of Large Compound Collections for Virtual Screening -- 10.2.1 NIH Molecular Libraries Roadmap Initiative and the PubChem Database -- 10.2.2 Other Chemical Databases in the Public Domain -- 10.3 Structure-based Virtual Screening -- 10.3.1 Major Methodologies -- 10.3.2 Challenges and Limitations of Current Approaches -- 10.4 Implementation of Cheminformatics Concepts in Structure-based Virtual Screening -- 10.4.1 Predictive QSAR Models as Virtual Screening Tools -- 10.4.2 Structure-based Chemical Descriptors of Protein-Ligand Interface: The EnTESS Method -- 10.4.3 Structure-based Cheminformatics Approach to Virtual Screening: The CoLiBRI Method -- 10.5 Summary and Conclusions: Integration of Conventional and Cheminformatics Structure-based Virtual Screening Approaches -- Acknowledgements -- References -- Subject Index |
| Title | Chemoinformatics approaches to virtual screening |
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