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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\u2013protein 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\u2010specific knowledge. We highlight the strengths and current limitations of LLM\u2010based 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. 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