Graph Neural Networks

Evaluation beyond goodness of fit: Quantifying biophysical alignment of AI models for kinase-centric drug discovery

Accurately modeling interactions between small molecules and proteins using machine learning (ML) remains a central challenge in modern …

@article{grossEvaluationBeyondGoodnessOf2026,
 author = {Joschka Groß and Michael Backenköhler and Paula Linh Kramer and Raquel López-Ríos De Castro and Guillermo Pérez-Hernández and Verena Wolf and Andrea Volkamer},
 doi = {10.1016/j.ailsci.2026.100169},
 journal = {Artificial Intelligence in the Life Sciences (AILSCI)},
 month = {6},
 number = {article 100169},
 pages = {1-17},
 publisher = {Elsevier},
 title = {Evaluation beyond goodness of fit: Quantifying biophysical alignment of AI models for kinase-centric drug discovery},
 url = {https://www.sciencedirect.com/science/article/pii/S2667318526000176?via%3Dihub},
 volume = {9},
 year = {2026}
}

Hands-On Graph Neural Networks

The basics of graph neural networks and their applications in various domains, summarized by the participants in easy-to-follow talktorials.

1. Oct, 2024

Neuro-symbolic General Policy Learning

Neural network-based policies that solve all instances of a planning domain, built on graph neural networks or transformers.

1. Oct, 2024

Deep Learning for Drug Discovery

Joint seminar on deep learning for molecular representations, property prediction, and de-novo molecular design — with a focus on geometric deep learning.

21. Apr, 2022

Exploring Graph Neural Networks

Recent advances in Graph Neural Networks and Geometric Deep Learning, with applications to drug discovery, brain analysis, and social networks.

19. Apr, 2021