Protein inverse folding has attracted increasing attention in recent years. However, we
observe that current methods are usually limited to the CATH dataset and the recovery …

Abstract Large Language Models (LLMs), including GPT-x and LLaMA2, have achieved
remarkable performance in multiple Natural Language Processing (NLP) tasks. Under the …

Recent studies have shown competitive performance in protein inverse folding, while most
of them disregard the importance of predictive confidence, fail to cover the vast protein …

Motivation RNA design shows growing applications in synthetic biology and therapeutics,
driven by the crucial role of RNA in various biological processes. A fundamental challenge is …

Abstract Structure-based protein design has attracted increasing interest, with numerous
methods being introduced in recent years. However, a universally accepted method for …

Molecule inverse folding has been a long-standing challenge in chemistry and biology, with
the potential to revolutionize drug discovery and material science. Despite specified models …

The capabilities of AI for biomedicine span a wide spectrum, from the atomic level, where it
solves partial differential equations for quantum systems, to the molecular level, predicting …

Generative models for protein design trained on experimentally determined structures have
proven useful for a variety of design tasks. However, such methods are limited by the …

Protein language models have demonstrated significant potential in the field of protein
engineering. However, current protein language models primarily operate at the residue …

Antibody design plays a pivotal role in advancing therapeutics. Although deep learning has
made rapid progress in this field, existing methods make limited use of general protein …