Protein representations from deep language models have yielded state-of-the-art
performance across many tasks in computational protein engineering. In recent years …

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

Abstract Motivation Protein language models (PLMs), which borrowed ideas for modelling
and inference from natural language processing, have demonstrated the ability to extract …

Protein is linked to almost every life process. Therefore, analyzing the biological structure
and property of protein sequences is critical to the exploration of life, as well as disease …

Attention-based models trained on protein sequences have demonstrated incredible
success at classification and generation tasks relevant for artificial-intelligence-driven …

Deep-learning language models have shown promise in various biotechnological
applications, including protein design and engineering. Here we describe ProGen, a …

Understanding the relationships between protein sequence, structure and function is a long-
standing biological challenge with manifold implications from drug design to our …

Large language models have recently been shown to develop emergent capabilities with
scale, going beyond simple pattern matching to perform higher level reasoning and …

As opposed to scaling-up protein language models (PLMs), we seek improving performance
via protein-specific optimization. Although the proportionality between the language model …

This paper demonstrates that language models are strong structure-based protein
designers. We present LM-Design, a generic approach to reprogramming sequence-based …