Tutorials in Chemoinformatics

30 tutorials and more than 100 exercises in chemoinformatics, supported by online software and data sets

Chemoinformatics is widely used in both academic and industrial chemical and biochemical research worldwide. Yet, until this unique guide, there were no books offering practical exercises in chemoinformatics methods. Tutorials in Chemoinformatics contains more than 100 exercises in 30 tutorials exploring key topics and methods in the field. It takes an applied approach to the subject with a strong emphasis on problem–solving and computational methodologies.

Each tutorial is self–contained and contains exercises for students to work through using a variety of software packages. The majority of the tutorials are divided into three sections devoted to theoretical background, algorithm description and software applications, respectively, with the latter section providing step–by–step software instructions. Throughout, three types of software tools are used: in–house programs developed by the authors, open–source programs and commercial programs which are available for free or at a modest cost to academics. The in–house software and data sets are available on a dedicated companion website.

Key topics and methods covered in Tutorials in Chemoinformatics include:

Data curation and standardization Development and use of chemical databases Structure encoding by molecular descriptors, text strings and binary fingerprints The design of diverse and focused libraries Chemical data analysis and visualization Structure–property/activity modeling (QSAR/QSPR) Ensemble modeling approaches, including bagging, boosting, stacking and random subspaces 3D pharmacophores modeling and pharmacological profiling using shape analysis Protein–ligand docking Implementation of algorithms in a high–level programming language

Tutorials in Chemoinformatics is an ideal supplementary text for advanced undergraduate and graduate courses in chemoinformatics, bioinformatics, computational chemistry, computational biology, medicinal chemistry and biochemistry. It is also a valuable working resource for medicinal chemists, academic researchers and industrial chemists looking to enhance their chemoinformatics skills.

List of Contributors

Preface

About the Companion Website

1. Chemical databases

1.1 Data curation

1.2 Relational chemical databases: creation, management and usage

1.3 Manipulations with Markush structures

1.4 Processing of SMILES, InChI and hashed fingerprints

2. Library design

3. Data analysis and visualization

3.1 Hierarchical clustering in R

3.2 Data visualization and analysis using Kohonen self–organizing maps

4. Obtaining and validation of QSAR/QSPR models

4.1. Descriptors generation using the CDK toolkit and web services

4.2 QSPR models on fragment descriptors

4.3 Cross–validation and the variable selection bias

4.4 Classification models

4.5 Regression models

4.6 Benchmarking machine–learning methods

4.7 Compound Classification Using the scikit–learn Library

5. Ensemble modeling

5.1 Bagging and Boosting of Classification Models

5.2 Bagging and Boosting of Regression Models

5.3 Instability of interpretable rules

5.4 Random Subspaces and Random Forest

5.5 Stacking

6. 3D Pharmacophore Modeling

7. The protein 3D–structures in virtual screening

8. Protein–ligand docking

9. Pharmacophorical profiling using shape analysis

10. Algorithmic Chemoinformatics

10.1 Similarity Searching using Data Fusion Techniques

10.2 Introduction to Chemoinformatics Toolkits

10.3 Canonical SMILES: The Canon algorithm

10.4 Substructure Searching: The Ullmann Algorithm

Alexandre Varnek, PhD, is a professor of theoretical chemistry at The University of Strasbourg, France where he heads the Laboratory of Chemoinformatics, and is Director of two MSc programs: Chemoinformatics and In Silico Drug Design. Professor Varnek′s research focuses on developing new approaches and tools for virtual screening and "in silico" design of new compounds and chemical reactions.