Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Large AI models, or foundation models, are models recently emerging with massive scales
both parameter-wise and data-wise, the magnitudes of which can reach beyond billions …

More than three billion years of evolution have produced an image of biology encoded into
the space of natural proteins. Here we show that language models trained at scale on …

Prediction methods inputting embeddings from protein language models have reached or
even surpassed state-of-the-art performance on many protein prediction tasks. In natural …

The recent success of large language models (LLMs) trained on static, pre-collected,
general datasets has sparked numerous research directions and applications. One such …

Artificial Intelligence (AI) techniques have made great advances in assisting antibody
design. However, antibody design still heavily relies on isolating antigen-specific antibodies …

Enzymes can be engineered at the level of their amino acid sequences to optimize key
properties such as expression, stability, substrate range, and catalytic efficiency─ or even to …

Directed protein evolution is central to biomedical applications but faces challenges like
experimental complexity, inefficient multi-property optimization, and local maxima traps …

Background Taste peptides are oligopeptides that improve the flavor and palatability of food.
Due to their unique taste characteristics and nutritional values, the development of taste …

Flexible modeling of the protein–ligand complex structure is a fundamental challenge for in
silico drug development. Recent studies have improved commonly used docking tools by …