A teaching platform for computer-aided drug design (CADD) using open source packages and data.
If you use TeachOpenCADD in a publication, please cite us! If you use TeachOpenCADD in class, please include a link back to our repository.
In any case, please star (and tell your students to star) those repositories you consider useful for your learning/teaching activities.
Figure adapted from Figure 1 in the TeachOpenCADD publication
(D. Sydow et al., J. Cheminformatics, 2019).
Open source programming packages for cheminformatics and structural bioinformatics are powerful tools to build modular, reproducible, and reusable pipelines for computer-aided drug design (CADD). While documentation for such tools is available, only few freely accessible examples teach underlying concepts focused on CADD applications, addressing especially users new to the field.
TeachOpenCADD is a teaching platform developed by students for students, which provides teaching material for central CADD topics. Since we cover both the theoretical as well as practical aspect of these topics, the platform addresses students and researchers with a biological/chemical as well as a computational background.
Each topic is covered in an interactive Jupyter Notebook, using open source packages such as the Python packages rdkit, pypdb, biopandas, nglview, and mdanalysis (find the full list here). Topics are continuously expanded and open for contributions from the community. Beyond their teaching purpose, the TeachOpenCADD material can serve as starting point for users’ project-directed modifications and extensions.
New edition: we have extended the TeachOpenCADD platform with 6 notebooks introducing deep learning and its application to CADD related topics.
If you can't wait and just want to read through the materials, please go to the read-only version here.
You can run the TeachOpenCADD talktorials either in the cloud for an instant start or locally for a full development experience.
The fastest way to explore is via Google Colab. No installation is required.
- Navigate to the Talktorials list below in Open in Google Colab section.
- Click the notebook URL on the title column to launch the tutorial directly in your browser.
To set up the project on your machine you can use the TeachOpenCADD runner. This takes care of downloading the talktorial and necessary data and setting up a virtual environment for talktorials.
You can install TeachOpenCADD easily via its pip package.
pip install teachopencadd
teachopencadd -hTo start a notebook, you simply call the runner with the talktorial ID:
teachopencadd 6 # change the ID to whichever talktorial you are interested inYou can also use uv to directly run a notebook. There is no need to download the notebook by hand.
uv run --with teachopencadd teachopencadd -h
If you prefer to work in the context of a graphical interface, talktorials T001-T008 are also available as KNIME workflows. Questions regarding this version should be addressed using the "Discussion section" available at this post. You need to create a KNIME account to use the forum.
Please refer to our TeachOpenCADD website to find a list of external resources:
- External packages and webservices that are used in the TeachOpenCADD material
- Further reading material on Python programming, cheminformatics, structural bioinformatics, and more.