The research field of chem/bio-informatics with deep learning is quickly evolving, highly competitive and comprehensively interdiscplined, which is the major motivation to wrap up this tutorial.
The tutorial document is still under development. A stable (but not complete) version built upon source/ is available here, supported by sphinx.
Preview of executed notebooks under code/ is available here, supported by nbviewer.
Jupyter notebooks are runnable interactively, supported by 
- Quick start
- Tutorials
- Notebooks
- Dataset
- Data representations
- Discriminative models
- Tutorials
- Notebooks
- Generative models
- Tutorials
- Notebooks
- Useful packages
- Resources
- Set up notebooks in Colab, Binder and nbviewer
The web pages are available here, but you can also compile this documentation locally:
conda create -n sphinx python=3.10
conda activate sphinx
python -m pip install -U sphinx
pip install nbsphinx furo
pip install nbconvert nbformat
pip install pandoc
pip install sphinx-design
make html