Proteins play a crucial role as mediators of the therapeutic potential of molecules. Capturing meaningful information on proteins with AI has an enormous potential in drug discovery and chemical biology, e.g.
, for structure-based drug discovery and polypharmacology. Despite such potential, strategies to capture sophisticated information on protein structure with AI are underexplored compared to small molecules.
This ERC-funded project has the ambitious goal to develop new AI strategies to learn efficiently from protein structures, to accelerate small molecule drug discovery. The project will be fueled by methodological innovation and aimed to leverage large corpora of protein data with cutting-edge deep learning algorithms. The developed approaches will be applied experimentally for structure-based drug discovery, thereby providing a unique opportunity to validate the AI predictions in a real-world setting.Job Description
Your tasks will include:
- Developing and implementing innovative algorithms to capture sophisticated structural information for structure-based drug discovery with AI.
- Implementing cutting-edge deep learning approaches to efficiently learn from large corpora of protein structures.
- Collaborating and interacting with ongoing research in ligand-based AI, as well as in medicinal chemistry and chemical biology.
- Mentoring and supervising junior researchers and students who are working on AI-assisted drug discovery.
- Communicating the results of your research through publications in scientific journals and presentations at conferences.
You will work at the interface between AI, chemistry, and biology, with a proactive and interdisciplinary attitude. You will become a member of the Molecular Machine Learning
team (led by Dr. F. Grisoni), whose mission is to augment human intelligence in drug discovery with novel AI technology. You will also be embedded in the Chemical Biology group, the Dept. of Biomedical Engineering, the Institute for Complex Molecular Systems, and the Eindhoven AI Systems Institute, which are characterized by a highly interdisciplinary and collaborative approach to science and research.