Large language models revolutionize bioinformatics, accelerating breakthroughs in protein analysis, drug discovery, and genomic research

Artificial intelligence-powered large language models (LLMs), like those behind ChatGPT, are rapidly reshaping bioinformatics research. A new study systematically details how these models decode complex biological data—from predicting protein structures to identifying disease-linked genes—with unprecedented speed and accuracy.

Published in Briefings in Bioinformatics, the review outlines five core strengths of LLMs:

1. Processing long biological sequences (e.g., DNA, proteins) using advanced tokenization and attention mechanisms.
2. Capturing semantic patterns in data for tasks like gene annotation and drug-target interaction prediction.
3. Cross-modal learning to integrate text, genomics, and structural biology data.
4. Reducing manual effort via end-to-end learning.
5. Leveraging unlabeled data through self-supervised training.

LLMs have enabled breakthroughs such as:

• Protein folding prediction (e.g., ESMFold) for drug design.
• Genome interpretation (e.g., DNABERT) for identifying disease mutations.
• Drug repurposing using tools like PharmBERT to analyze clinical literature.

However, challenges persist in model transparency, computational costs, and data biases. The authors call for:

• Multimodal AI systems combining genomic, imaging, and clinical data.
• Explainable AI frameworks to build scientific trust.
• Ethical guidelines for privacy in biomedical AI.

"LLMs are not just tools—they represent a paradigm shift in how we study life sciences," said senior author Dr. Peng Luo. "Their integration with experimental biology will accelerate discoveries from lab to clinic."