Parse Biosciences and Graph Therapeutics (Graph) today announced a strategic partnership to create one of the largest and most comprehensive immune cell perturbation atlases. The collaboration will leverage Graph's lab-in-the-loop platform with Parse's GigLab to profile hundreds of millions of cells from patients with immune diseases under systematic perturbation, making the immune system’s highly dynamic behavior accessible to AI-first drug discovery and development. This endeavor derisks and speeds up the development of new curative therapies for patients with immune mediated diseases.
Autoimmune and immune-mediated diseases are driven by context- and patient-specific immune cell population responses, making it difficult for drug developers to identify drug targets and predict clinical outcomes. The partnership between Graph and Parse addresses this challenge by combining Graph’s realistic patient-derived disease models and Parse’s scalable whole transcriptome single cell technology to analyze hundreds of millions of human cells and reveal the diverse immune and tissue interactions that drive disease. This approach streamlines discovery and can prevent costly late-stage failures, with the potential to save millions for each drug that comes to market.
Graph's platform combines sophisticated primary patient cell assays with an iterative active learning technology to systematically select and test perturbations in a wide range of disease-relevant contexts. Rather than hoping simplified or static sampling models translate to clinical settings, Graph’s scientists use an experimental framework that efficiently distinguishes promising from dead-end hypotheses before committing massive development resources. Each validation cycle informs the next, creating a compounding knowledge effect that accelerates future discoveries.
After Graph's platform intelligently selects which conditions to profile across the enormously complex space of immune dysfunction, Parse's GigaLab, which uses the Evercode™ technology, will generate massive single cell datasets with unprecedented speed and quality.
"After a decade of evolving impactful AI drug discovery platforms, we've learned that closing the gap between prediction and clinical reality demands active investment in biological context," says Gregory Vladimer, PhD, CEO and co-founder of Graph Therapeutics. "This partnership treats data as strategic infrastructure: every experiment is designed to reduce biological uncertainty, every validation compounds institutional knowledge, and every discovery accelerates the next. When you invest in fit-for-purpose, clinically relevant data generation at the discovery stage, you fundamentally change the economics and success rates of drug development."
“Graph’s systematic testing of patient cells is the kind of transformative work the GigaLab was designed to support,” said Charlie Roco, PhD, Co-founder and Chief Technology Officer at Parse. “This partnership shows how industrial-scale single cell biology and advanced AI can reveal disease mechanisms directly in patient cells and reshape drug discovery.”
