Recent advances and future trends for protein–small molecule interaction predictions with protein language models

Abstract

In recent years, the application of natural language models to protein amino acid sequences, referred to as protein language models (PLMs), has demonstrated a significant potential for uncovering hidden patterns related to protein structure, function, and stability. The critical functions of proteins in biological processes often arise through interactions with small molecules; central examples are enzymes, receptors, and transporters. Understanding these interactions is particularly important for drug design, for bioengineering, and for understanding cellular metabolism. In this review, we present state-of-the-art PLMs and explore how they can be integrated with small molecule information to predict protein-small molecule interactions. We present several such prediction tasks and discuss current limitations and potential areas for improvement.