Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Artificial intelligence for scientific discovery has recently generated significant interest within
the machine learning and scientific communities, particularly in the domains of chemistry …

Protein representation learning is a challenging task that aims to capture the structure and
function of proteins from their amino acid sequences. Previous methods largely ignored the …

Protein representation learning plays a crucial role in understanding the structure and
function of proteins, which are essential biomolecules involved in various biological …

Language Models (LMs) excel in understanding textual descriptions of proteins, as evident
in biomedical question-answering tasks. However, their capability falters with raw protein …

Learning effective protein representations is critical in a variety of tasks in biology such as
predicting protein functions. Recent sequence representation learning methods based on …

Is there a foreign language describing protein sequences and structures simultaneously?
Protein structures, represented by continuous 3D points, have long posed a challenge due …

Computational screening of naturally occurring proteins has the potential to identify efficient
catalysts among the hundreds of millions of sequences that remain uncharacterized. Current …

We introduce ProteinWorkshop, a comprehensive benchmark suite for representation
learning on protein structures with Geometric Graph Neural Networks. We consider large …

Learning effective representations is imperative for comprehending proteins and
deciphering their biological functions. Recent strides in language models and graph neural …