Language models have recently emerged as a powerful machine-learning approach for
distilling information from massive protein sequence databases. From readily available …

Unsupervised protein language models trained across millions of diverse sequences learn
structure and function of proteins. Protein language models studied to date have been …

Data-centric approaches have been used to develop predictive methods for elucidating
uncharacterized properties of proteins; however, studies indicate that these methods should …

In the field of artificial intelligence, a combination of scale in data and model capacity
enabled by unsupervised learning has led to major advances in representation learning and …

Abstract Models based on machine learning can enable accurate and fast molecular
property predictions, which is of interest in drug discovery and material design. Various …

Unsupervised contact prediction is central to uncovering physical, structural, and functional
constraints for protein structure determination and design. For decades, the predominant …

Pre-trained language models (PLMs) have been the de facto paradigm for most natural
language processing tasks. This also benefits the biomedical domain: researchers from …

Inverse protein folding is challenging due to its inherent one-to-many mapping
characteristic, where numerous possible amino acid sequences can fold into a single …

Therapeutic antibodies make up a rapidly growing segment of the biologics market.
However, rational design of antibodies is hindered by reliance on experimental methods for …

Recent developments in deep learning, coupled with an increasing number of sequenced
proteins, have led to a breakthrough in life science applications, in particular in protein …