Biotech startup Network Bio secured $50 million to build disease-specific AI models from human biological data to support the personalized practice of medicine.
Network Bio said the investment round was led by Section 32, Thiel Bio, Founders Fund, Breyer Capital, Blue Venture Fund, and JSL Health Capital, with participation from other life science and AI funds.
Through collaboration with leading academic medical centers, including Mass General Brigham, the University of Pennsylvania, and the University of Colorado Anschutz, the company has created a research network of large biobanks. With fresh funds, the startup plans to accelerate the development and launch of its disease-specific AI models trained on large-scale tissue, blood, molecular, and clinical datasets.
“Medicine is approaching an inflection point where AI can fundamentally change how biomedical discovery is done,” said Mike Pellini, M.D., Managing Partner at Section 32 and Chairman of Network Bio’s Board of Directors.
Mike Pellini said: “What differentiates Network Bio is the combination of the network of academic medical center biobanks with an AI architecture built for medicine. Rather than building models one disease at a time, the company is building General Medical Intelligence that becomes more capable with every new biological question it answers.”
Network Bio is developing disease-specific AI models designed to accelerate diagnostics, biomarker discovery, and drug development.
With fresh funds, Network Bio plans to bring its AI platform to the market for real-world clinical applications. Network Bio said its platform has demonstrated potential application across multiple disease areas, including published results in respiratory disease and additional studies in ovarian and bone disease.
AI Platform Designed to Learn the Biology of Disease
Unlike conventional AI systems built for a single disease or application, Network Bio is developing an AI infrastructure that is capable of learning biological principles that transfer across diseases, tissues, and data modalities. The company’s vertically integrated platform combines two foundational technologies:
- A biological research network that sources, links, and structures tissue, blood, and longitudinal clinical outcomes from leading academic medical centers to create AI-ready biological datasets.
- Bio-native AI architecture that is purpose-built to learn from multimodal biological data, overcome technical confounders, and generate interpretable representations of disease biology that generalize across diseases, with peer-reviewed research published in top-tier journals, including Nature Machine Intelligence.
According to our latest funding insights, digital health startups have raised $20 billion in funding so far this year. More recently, Perceptic, a London, UK-based developer of an infrastructure-agnostic end-to-end AI operating system for drug discovery, raised $12 million in seed funding led by Accel, with participation from Air Street Capital and Elder Gull.