TeachOpenCADD goes Deep Learning!

We are thrilled to announce the release of TeachOpenCADD Deep Learning (DL) Edition, an expansion of TeachOpenCADD platform! If you are not familiar with TeachOpenCADD platform, more information is provided on its website or here.

The DL edition offers a comprehensive introduction to various deep learning topics with a focus on its application to drug discovery tasks. It empowers learners to explore and understand various concepts in DL field accomodating beginner users to advanced levels.

Here is a preview of the talktorials included in TeachOpenCADD-DL Edition:

• T033 Molecular Representations : Learn the fundamentals of molecular representations in DL tasks.
• T034 Recurrent Neural Networks : Dive into the world of recurrent neural networks (RNNs) and its application to predict molecular property.
• T035 Graph Neural Networks : Use GNNs for learning from molecular graphs and predicting molecular properties.
• T036 Equivariant Graph Neural Network : Explain concept of equivariant graph neural networks (eGNNs) and their role in capturing 3D information of molecular structures for molecular property prediction.
• T037 Uncertainty Estimation : Evaluate and quantify uncertainty in predictive models, with reliable confidence measures.
• T038 Protein-Ligand Interaction Prediction : Predict interactions between protein and ligands using GNNs.

To stay updated with the latest developments, make sure to follow the TeachOpenCADD project closely. If you have any feedback, want to report bugs, or have questions, you can contribute to the project and engage with the community on our Github repository.

Contributors

We would like to expresss our sincere thanks to Michael Backenköhler, Paula Linh Kramer, Joschka Groß, Gerrit Großmann, Roman Joeres, Azat Tagirdzhanov, Dominique Sydow, Hamza Ibrahim, Floriane Odje, Verena Wolf and Andrea Volkamer. Their huge contributions have made it possible to integrate deep learning content into the TeachOpenCADD platform.