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, single-cell technologies, and systematic functional genomics to accelerate drug discovery for immune-mediated diseases. The collaboration brings together Graph’s lab-in-the-loop platform with Parse Biosciences’ GigaLab, a high-throughput single-cell sequencing system capable of profiling hundreds of millions of cells from patients with immune disorders under controlled perturbations. The goal is to map the dynamic behavior of the immune system at unprecedented resolution by applying systematic genetic and chemical perturbations across diverse patient samples. By integrating AI-driven analysis with massive-scale single-cell data, the project aims to uncover causal relationships between genetic variations, immune cell function, and disease phenotypes. This approach enables researchers to identify key therapeutic targets and predict drug responses with greater accuracy, reducing development risks and accelerating the path to new, potentially curative treatments. The partnership leverages the strengths of both companies: Graph Therapeutics brings its proprietary AI-powered platform for hypothesis generation and experimental design, while Parse Biosciences contributes its GigaLab technology, which enables scalable, cost-effective single-cell profiling across complex biological systems. The integration of these platforms allows for closed-loop experimentation—where AI designs perturbations, experiments are executed, and results are fed back into the model to refine future predictions—creating a powerful engine for discovery. This initiative is particularly significant for autoimmune and inflammatory diseases, where immune system dysfunction is central to pathology. By studying immune cells from real patient populations under diverse perturbations, the atlas will capture the true heterogeneity of immune responses, offering insights that traditional bulk sequencing or animal models cannot provide. The resulting data will be used to train machine learning models that can predict disease mechanisms and therapeutic outcomes, paving the way for personalized medicine. The collaboration builds on previous successes in the field. Parse Biosciences has already partnered with companies like Tahoe Therapeutics and Codebreaker Labs to generate large-scale single-cell atlases and apply causal genomics at scale. Most recently, Parse launched Evercode Whole Blood Fixation, a kit enabling immediate stabilization of blood samples at the point of collection, eliminating the need for specialized equipment or personnel and improving the quality and accessibility of translational research. The new alliance with Graph Therapeutics represents a major step forward in functional genomics and drug development. It reflects a growing trend in biotech: combining AI, high-throughput experimentation, and single-cell resolution to decode complex biological systems. By making large-scale perturbation studies feasible and affordable, the partnership could transform how researchers identify and validate drug targets. The project is expected to generate a publicly accessible resource that will benefit academic and industry researchers alike. While the full data will be made available to the scientific community, the commercial application of findings may be pursued through partnerships and licensing. As AI continues to reshape biomedical research, this collaboration exemplifies how integrating machine learning with experimental biology can unlock new frontiers in understanding human disease. With a focus on immune system dynamics and therapeutic discovery, the initiative stands to significantly advance the development of safer, more effective treatments for patients suffering from immune-mediated conditions.