Antimicrobial resistance is a global public health threat, and the World Health Organization
(WHO) has announced a priority list of the most threatening pathogens against which novel …

Artificial intelligence has made significant advances in the field of protein structure prediction
in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated …

Through evolution, nature has presented a set of remarkable protein materials, including
elastins, silks, keratins and collagens with superior mechanical performances that play …

Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …

Motivation Predicting protein structures with high accuracy is a critical challenge for the
broad community of life sciences and industry. Despite progress made by deep neural …

Protein structure prediction has been greatly improved by deep learning in the past few
years. However, the most successful methods rely on multiple sequence alignment (MSA) of …

The prediction of three-dimensional (3D) protein structure from amino acid sequences has
stood as a significant challenge in computational and structural bioinformatics for decades …

Abstract Background Protein–protein interactions (PPIs) are crucial in various biological
functions and cellular processes. Thus, many computational approaches have been …

Apparent parallels between natural language and antibody sequences have led to a surge
in deep language models applied to antibody sequences for predicting cognate antigen …

Geometric graph is a special kind of graph with geometric features, which is vital to model
many scientific problems. Unlike generic graphs, geometric graphs often exhibit physical …