Parse Biosciences and Graph Therapeutics have announced a strategic partnership to create one of the largest and most comprehensive immune cell perturbation atlases, combining cutting-edge AI-driven drug discovery with high-throughput single cell technology. The collaboration aims to map immune dysfunction in patients with autoimmune and immune-mediated diseases, accelerating the development of curative therapies. By integrating Graph’s lab-in-the-loop platform with Parse’s GigaLab and Evercode™ technology, the project will profile hundreds of millions of human immune cells under systematic perturbations, generating vast, high-quality datasets that reflect real-world biological complexity. Autoimmune and immune-mediated diseases are driven by dynamic, patient-specific responses of immune cells, making it difficult to identify effective drug targets or predict clinical outcomes. Traditional approaches often rely on simplified or static models that fail to translate to human patients. This partnership directly addresses that gap by using patient-derived cells in physiologically relevant contexts. Graph’s platform employs active learning to intelligently select and test perturbations—such as genetic, chemical, or environmental changes—across diverse disease states, enabling efficient hypothesis testing and iterative refinement of biological understanding. Each experimental cycle not only validates or refutes a hypothesis but also feeds into a growing knowledge base, creating a compounding effect that accelerates future discovery. This data-driven, feedback-loop approach reduces biological uncertainty early in development, helping to de-risk drug candidates before large-scale investment. Once Graph identifies the most promising perturbation conditions, Parse’s GigaLab takes over. Leveraging its proprietary Evercode™ technology, GigaLab can generate whole transcriptome single cell data at industrial scale—processing millions of cells with high speed, accuracy, and reproducibility. This enables researchers to uncover rare cell populations, complex cell-state transitions, and intricate immune-tissue interactions that drive disease, all directly from patient samples. Gregory Vladimer, PhD, CEO and co-founder of Graph Therapeutics, emphasized the strategic value of data in this partnership: “Every experiment is designed to reduce biological uncertainty, every validation compounds institutional knowledge, and every discovery accelerates the next.” He highlighted that investing in clinically relevant, fit-for-purpose data at the discovery stage fundamentally improves the success rate and economics of drug development. Charlie Roco, PhD, Co-founder and CTO of Parse Biosciences, added that the partnership exemplifies the power of combining scalable single cell biology with advanced AI: “Graph’s systematic testing of patient cells is the kind of transformative work the GigaLab was designed to support. This shows how industrial-scale single cell analysis and AI can reveal disease mechanisms directly in human cells and reshape drug discovery.” Graph Therapeutics, headquartered in Vienna, Austria, is building a next-generation AI platform for immunology and inflammation drug discovery, drawing on its prior success in precision oncology. Parse Biosciences, a global life sciences company, specializes in high-throughput single cell sequencing, enabling breakthroughs in cancer, regenerative medicine, neurodevelopment, and immune system research. Together, this collaboration represents a major step toward more efficient, predictive, and patient-centered drug development. By turning biological complexity into actionable data, the partnership has the potential to reduce late-stage clinical failures, lower costs, and bring life-changing therapies to patients with immune-mediated diseases faster.