Parse Biosciences and Graph Therapeutics have announced a strategic collaboration to create one of the largest and most comprehensive immune cell perturbation atlases to date.
The partnership combines Graph’s lab-in-the-loop platform with Parse’s GigaLab, leveraging AI and single-cell technology to profile hundreds of millions of cells from patients with immune-mediated diseases under systematic perturbations. This initiative aims to make the dynamic behavior of the immune system accessible to AI-driven drug discovery and accelerate the development of new therapies.
Autoimmune and immune-mediated diseases are highly complex, with patient- and context-specific immune cell responses that challenge traditional drug development. By combining Graph’s patient-derived disease models with Parse’s scalable whole-transcriptome single-cell technology, the collaboration will analyze vast numbers of human cells to uncover the diverse immune and tissue interactions driving disease. This approach is expected to streamline target identification, predict clinical outcomes more accurately, and reduce costly late-stage drug development failures.
Graph’s platform integrates sophisticated primary patient cell assays with iterative active learning technology, systematically selecting and testing perturbations across disease-relevant contexts. This experimental framework allows researchers to distinguish promising therapeutic hypotheses from ineffective ones before committing large-scale development resources, creating a compounding knowledge effect that accelerates future discoveries.
Once Graph identifies the conditions to be profiled, Parse’s GigaLab, powered by Evercode™ technology, will generate large-scale single-cell datasets with high speed and quality. The combined approach is designed to de-risk drug development and enhance the efficiency of AI-enabled discovery, potentially improving success rates for immune disease therapies.
The partnership reflects a growing trend of integrating industrial-scale single-cell biology with AI to reveal disease mechanisms directly in patient cells. By generating fit-for-purpose, clinically relevant datasets, the collaboration seeks to transform the economics and outcomes of drug discovery for immune-mediated conditions.
