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Generative AI

Generative AI

Case Study

Accelerating innovation in pharma with advanced language models

Harnessing LLMs to Expedite Scientific Discovery and Unlock New Insights


The overwhelming volume of scientific literature poses a substantial challenge toward new discoveries due to its volume, the constant flux of new publications, and potential for conflicting conclusions. Researchers face the daunting task of manually sifting through an extensive array of scientific articles to extract critical information and develop innovative insights. This time-consuming and labor-intensive process not only hinders productivity but also limits the depth of understanding achievable within a reasonable timeframe. Advances in Large Language Models (LLMs) allow us to develop customized LLM solutions specifically optimized for biological literature and restricted to produce factually accurate answers and results.


We deployed state of the art LLMs (e.g. GPT3.5/4 and Llama2) into a biological framework, customizing the solution for biologic use cases. This approach allows us to capture the complexity in how biological entities and relationships are discussed in literature to create an LLM solution for life sciences, producing accurate, relevant, and factually sourced answers.

Users can query the vast set of published literature and arrive at the information most germane to their area of interest. This tool accelerates research, extracts precise information, and fosters a deeper understanding of disease biology, expediting biomedical research.

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