Our mission

Augmenting human intelligence in drug discovery with meaningful innovation in AI

Discovering innovative molecules with the desired bioactivity is an essential step to develop new drugs and gather a greater understanding of biological systems. Machine learning bears promise to accelerate the molecule discovery pipeline, by allowing for a time- and cost-efficient navigation of the incredibly vast chemical universe. In the Molecular Machine Learning team (hosted at the TU/e), we develop and apply data-driven methods to design novel molecular entities and unveil structure-activity relationships of small molecules and peptides. With research located at the interface between chemical biology and AI, our final mission is to augment human intelligence in drug and molecule discovery.

Our Team

Francesca Grisoni

Assistant Professor

Derek van Tilborg

PhD Candidate

Rıza Özçelik

PhD Candidate

Yves Gaetan Nana Teukam

PhD Candidate w/ IBM Zurich

Cecile Valsecchi

Guest PhD Candidate @Uni Milano-Bicocca

Luke Rossen

Master Student


Generative deep learning
De novo molecule design with AI
Molecular property prediction
Molecule prioritization with AI
Prospective ML applications
AI-driven molecule discovery


In the Molecular Machine learning team, we believe in the importance of open and reproducible research.
Check out our GitHub repository for freely-available code!



Molecular ML @ Eindhoven University of Technology (TU/e)
Institute for Complex Molecular Systems (ICMS)
Department of Biomedical Engineering
Ceres building, Groene Loper 7, Eindhoven, Netherlands
Email: f.grisoni [at] tue.nl (F. Grisoni)

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