Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Recent breakthroughs in AI coupled with the rapid accumulation of protein sequence and
structure data have radically transformed computational protein design. New methods …

Pretrained protein sequence language models have been shown to improve the
performance of many prediction tasks and are now routinely integrated into bioinformatics …

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

In the complex and multidimensional field of medicine, multimodal data are prevalent and
crucial for informed clinical decisions. Multimodal data span a broad spectrum of data types …

In recent years, significant progress has been made in this field of protein function prediction
with the development of various machine-learning approaches. However, most existing …

Recent advancements in biological research leverage the integration of molecules, proteins,
and natural language to enhance drug discovery. However, current models exhibit several …

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

Effectively representing materials as text has the potential to leverage the vast
advancements of large language models (LLMs) for discovering new materials. While LLMs …

Structure-based drug design (SBDD) utilizes the three-dimensional geometry of proteins to
identify potential drug candidates. Traditional methods, grounded in physicochemical …