Freyr Sverrisson

The paper introduces a computational framework for fragment-based screening and binding affinity prediction using protein surface descriptors to identify ligands for challenging protein targets.

This paper introduces a deep learning framework for protein structure analysis that processes raw atomic coordinates directly, achieving state-of-the-art results in interaction site identification and protein-protein interaction prediction with faster speeds and fewer parameters.

The paper introduces MaSIF, a geometric deep learning framework to predict biomolecular interactions by learning interaction fingerprints from protein molecular surfaces.

This paper introduces a computational framework for designing functional proteins by integrating folding and sequence design, showing that functional motifs can be embedded without requiring the global energy minimum.