Recent advances in Natural Language Processing (NLP) have ignited interest in developing effective methods for predicting protein-ligand interactions (PLIs) given their relevance to drug discovery and protein engineering efforts and the ever-growing volume of biochemical sequence and structural data available. The parallels between human languages and the "languages" used to represent proteins and ligands have enabled the use of NLP machine learning approaches to advance PLI studies. In this review, we explain where and how such approaches have been applied in the recent literature and discuss useful mechanisms such as long short-term memory, transformers, and attention. We conclude with a discussion of the current limitations of NLP methods for the study of PLIs as well as key challenges that need to be addressed in future work.

本研究解决了蛋白质-配体相互作用（PLI）预测中的方法不足问题，特别是在药物发现与蛋白质工程中的重要性。文章提出了借助自然语言处理（NLP）机器学习方法的创新思路，探讨了其在近期文献中的应用以及相关机制。最重要的发现是，尽管NLP方法在PLI研究中具有潜力，但仍存在显著的局限性和未来需要克服的挑战。

用于研究蛋白质-配体相互作用的自然语言处理方法