About me
Welcome to my home page!
I am a faculty researcher at Mines Paris - PSL, in the CBIO (Centre for Computational Biology). I am a co-organizer of the MASIM community, and a member of the PRAIRIE Institute and the ELLIS network.
Research Interests
I work on machine learning for biomolecular structures. My goal is to leverage the increasing volume of structural data via geometric deep learning to transform structural biology and drug design.
My research follows four main directions:
- A. Embedding protein structures with multimodal geometric deep learning (focusing on surfaces).
- B. Embedding RNA structures with geometric graphs and laying the foundations for encoder development (datasets, benchmarks).
- C. Using protein representations for protein-protein interaction prediction (e.g., host-pathogen).
- D. Using RNA representations for protein-small molecule interaction prediction (drug design).
Quick Bio
Prior to joining CBIO, I did a post-doc with Maks Ovsjanikov, working on protein representation and cryo-EM data analysis. I did my PhD with Jean-Philippe Vert and Michael Nilges on equivariant methods and drug design. Before that, I studied at École Polytechnique and earned a Master of Research in Computer Science from McGill University, working on RNA structure with Jérôme Waldispühl.
Contact: vincent.mallet[at]minesparis.psl.eu