Tutorials in chemoinformatics / edited by Alexandre Varnek.

Includes bibliographical references and index.

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Title Page ; Copyright Page; Contents; List of Contributors; Preface; About the Companion Website; Part 1 Chemical Databases; Chapter 1 Data Curation; Theoretical Background; Software; Step-by-Step Instructions ; Conclusion; References; Chapter 2 Relational Chemical Databases: Creation, Management, and Usage ; Theoretical Background; Step-by-Step Instructions ; Conclusion; References; Chapter 3 Handling of Markush Structures ; Theoretical Background; Step-by-Step Instructions ; Conclusion; References; Chapter 4 Processing of SMILES, InChI, and Hashed Fingerprints ; Theoretical Background

AlgorithmsStep-by-Step Instructions ; Conclusion; References; Part 2 Library Design; Chapter 5 Design of Diverse and Focused Compound Libraries ; Introduction; Data Acquisition; Implementation; Compound Library Creation; Compound Library Analysis; Normalization of Descriptor Values; Visualizing Descriptor Distributions; Decorrelation and Dimension Reduction; Partitioning and Diverse Subset Calculation; Partitioning; Diverse Subset Selection; Combinatorial Libraries; Combinatorial Enumeration of Compounds; Retrosynthetic Approaches to Library Design; References

Part 3 Data Analysis and VisualizationChapter 6 Hierarchical Clustering in R ; Theoretical Background; Algorithms; Instructions; Hierarchical Clustering Using Fingerprints; Hierarchical Clustering Using Descriptors; Visualization of the Data Sets; Alternative Clustering Methods; Conclusion; References; Chapter 7 Data Visualization and Analysis Using Kohonen Self-Organizing Maps ; Theoretical Background; Algorithms; Instructions; Conclusion; References; Part 4 Obtaining and Validation QSAR/QSPR Models; Chapter 8 Descriptors Generation Using the CDK Toolkit and Web Services

Theoretical BackgroundAlgorithms; Step-by-Step Instructions ; Conclusion; References; Chapter 9 QSPR Models on Fragment Descriptors ; Abbreviations; DATA; ISIDA_QSPR input; Data Split Into Training and Test Sets; Substructure Molecular Fragment (SMF) Descriptors; Regression Equations; Forward and Backward Stepwise Variable Selection; Parameters of Internal Model Validation; Applicability Domain (AD) of the Model; Storage and Retrieval Modeling Results; Analysis of Modeling Results; Root-Mean Squared Error (RMSE) Estimation ; Setting the Parameters; Analysis of n-Fold Cross-Validation Results

Loading Structure-Data File Descriptors and Fitting Equation; Variables Selection; Consensus Model; Model Applicability Domain; n-Fold External Cross-Validation ; Saving and Loading of the Consensus Modeling Results; Statistical Parameters of the Consensus Model; Consensus Model Performance as a Function of Individual Models Acceptance Threshold; Building Consensus Model on the Entire Data Set; Loading Input Data; Loading Selected Models and Choosing their Applicability Domain; Reporting Predicted Values; Analysis of the Fragments Contributions; References