The twenty-first century is presenting humankind with unprecedented environmental and
medical challenges. The ability to design novel proteins tailored for specific purposes would …

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 …

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

Recent advances in machine learning have leveraged evolutionary information in multiple
sequence alignments to predict protein structure. We demonstrate direct inference of full …

Protein design aims to build novel proteins customized for specific purposes, thereby
holding the potential to tackle many environmental and biomedical problems. Recent …

Three billion years of evolution has produced a tremendous diversity of protein molecules,
but the full potential of proteins is likely to be much greater. Accessing this potential has …

The current trend of scaling language models involves increasing both parameter count and
training dataset size. Extrapolating this trend suggests that training dataset size may soon be …

We consider the problem of predicting a protein sequence from its backbone atom
coordinates. Machine learning approaches to this problem to date have been limited by the …

Neural sequence models based on the transformer architecture have demonstrated
remarkable\emph {in-context learning}(ICL) abilities, where they can perform new tasks …

Collecting data is one of the bottlenecks of Human-Computer Interaction (HCI) research.
Motivated by this, we explore the potential of large language models (LLMs) in generating …