D2Cell is a novel pipeline that combines large language models (LLMs) with genome-scale metabolic models (GEMs) to extract and predict metabolic engineering strategies from scientific literature. The approach leverages the text-understanding capabilities of LLMs to mine published research for relevant metabolic engineering designs, then integrates these insights with computational metabolic models.

This work represents a promising intersection of AI-driven text mining and systems biology, potentially accelerating the design of engineered microorganisms for biotechnology applications. By automating the extraction of metabolic engineering knowledge from vast bodies of scientific literature, D2Cell could significantly reduce the time researchers spend on manual literature review and strategy formulation.