Paper ID: 2409.13057 • Published Sep 19, 2024
Natural Language Processing Methods for the Study of Protein-Ligand Interactions
James Michels, Ramya Bandarupalli, Amin Ahangar Akbari, Thai Le, Hong Xiao, Jing Li, Erik F. Y. Hom
TL;DR
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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.