Intelligent Modeling of Protein Function and Drug Discovery Using Large Language Models: A Review

Abstract

Large language models (LLMs), originally developed for natural language processing, are increasingly being applied to biological data, offering a novel paradigm for intelligent modeling in protein function prediction and drug discovery. By leveraging their ability to capture contextual dependencies in sequences, LLMs such as ProteinBERT, ESM, and BioGPT have shown strong potential in learning informative representations from protein sequences and biomedical literature. These models can support downstream tasks including function annotation, protein–protein interaction prediction, and de novo drug design. This review presents a comprehensive overview of recent advances in applying LLMs to molecular biology, focusing on their architectures, training strategies, and integration with domain-specific knowledge. We highlight the strengths and current limitations of LLM-based approaches, including challenges in data scarcity, interpretability, and biological relevance. Finally, we discuss future research directions for enhancing the reliability, efficiency, and domain adaptation of LLMs in life sciences. This work aims to provide a foundation for researchers seeking to apply intelligent systems based on LLMs in computational biology and drug development.