Gabriel Studer

This study evaluates four protein-ligand cofolding methods using a new benchmark dataset, finding they primarily memorize ligand poses rather than predict them de novo.

This paper introduces a new 20-letter alphabet, TEA, derived from protein language models, enabling efficient homology searches comparable to structure-based methods without structural data.

PLINDER provides a large, annotated PLI dataset with diverse annotations and improved evaluation splits to better assess computational methods for drug design.

This paper introduces an embedding-based alignment method using protein language models to detect structural similarities in the twilight zone, outperforming existing approaches without training.