Large Language Models for Bioinformatics

Applying large language models to biology

Large Language Models (LLMs) are reshaping bioinformatics by learning the language of biology directly from sequence and text. Protein and genomic language models capture the statistical structure of amino-acid and nucleotide sequences, producing powerful embeddings for tasks such as structure and function prediction, variant-effect estimation and protein design, while general and biomedical LLMs accelerate literature mining and knowledge extraction.

We help research teams design, build and deploy LLM-powered tools tailored to their data and questions, with a focus on accuracy, reproducibility and responsible use.

Our Services

• Protein and genomic language-model embeddings (e.g. ESM)
• Fine-tuning models for classification and prediction tasks
• Variant-effect and protein-function prediction pipelines
• Retrieval-augmented generation (RAG) over biomedical literature
• Custom biomedical chat assistants and research copilots
• Named-entity recognition and biomedical text mining
• Sequence-to-function and property-prediction models
• Benchmarking, evaluation and deployment

These tools turn large, unstructured biological data into actionable predictions and insight, integrated into your existing workflows.