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All Projects → joofio → py4chemoinformatics

joofio / py4chemoinformatics

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Python for chemoinformatics

Labels

machine-learningdeep-learningjupyterchemistrycheminformaticsscikit-learncvdrug-discoveryrdkitchemoinformaticsdrug-design

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Table of Contents

Update 03_2019: forked and tried to translate to english. Corrections are welcome.

Update 01_2020: updating the information to mimic original.

01 Introduction

  • What is RDKit?

  • Target audience

  • About the code in this book

  • Acknowledgments

  • bonus

  • License

02 Create an environment for chemoinformatics

  • About Anaconda

  • How to install Anaconda

  • Build virtual environment and install packages

  • Description of the installed package

  • More about Conda

03 Basics of Python programming

  • Python basics

  • Use it conveniently with the Jupyter notebook

  • Machine learning with Python

04 Public database for chemoinformatics

  • ChEMBL

  • PubChem

  • Search for desired information with ChEMBL

  • Other useful databases

05 Handling Structural Information with RDKit

  • What is SMILES?

  • Let’s draw the structure

  • How to handle multiple compounds at once?

  • Try hetero shuffling

06 Evaluating the similarity of compounds

  • What does it mean that compounds are similar?

  • Calculate similarity

  • Virtual screening

  • Clustering

  • Structure Based Drug Design (SBDD)

07 Evaluation of similarity using graph structure

  • Classification by major skeleton (MCS)

  • Matched Molecular Pair and Matched Molecular Series

  • Visualize MMP networks using Cytoscape

08 I want to have many compounds at once

  • Chemical Spaceとは

  • Mapping using tSNE

09 Basics of Quantitative Structure-Activity Relationship (QSAR)

  • Consider the cause of the effect (Classification problem)

  • Predict the efficacy of drugs (regression problem)

  • Model applicability (applicability domain)

10 Introduction to Deep-Learning

  • About TensorFlow and Keras

  • Google colab

  • Let’s install

11 Structure-activity relationship using deep-learning

  • Predictive model construction using DNN

  • Devising a descriptor (neural fingerprint)

13 Conclusion

  • Final remarks and further reading

License

This document is copyright © 2019 by @fmkz___ and @iwatobipen

CC-BY-NC-SA
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