Chemoinformatics approaches to virtual screening

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