Therapy Areas: Inflammatory Diseases
Recursion Pharmaceuticals Secures USD 121m in Series C Financing
16 July 2019 - - US-based biopharmaceutical company Recursion has closed a USD 121m Series C financing, the company said.
The round was led by Baillie Gifford's flagship investment trust, Scottish Mortgage Investment Trust PLC, with participation by new institutional investors Intermountain Ventures, Regents of the University of Minnesota, Texas Tech University System, and select angel investors.
All prior institutional investors also participated in the round, including Lux Capital, Data Collective, Mubadala Ventures, Two Sigma Ventures, Obvious Ventures, Felicis Ventures, Epic Ventures, Menlo Ventures, AME Cloud Ventures, and CRV.
The new financing will support Recursion's continued build-out of its machine learning-enabled drug discovery platform alongside new capabilities designed to radically accelerate new chemical entity chemistry and predict safety pharmacology.
In addition, the company will continue to advance its growing pipeline of pre-clinical and clinical assets, including clinical-stage programs for cerebral cavernous malformation and neurofibromatosis type 2.
While Recursion plans to prioritize the advancement of treatments for rare diseases within its own pipeline, it will continue to forge strong partnerships with pharmaceutical companies in a variety of therapeutic areas, including immuno-oncology, oncology, aging, and inflammation.
Recursion is a clinical-stage biotechnology company combining experimental biology and automation with artificial intelligence in a massively parallel system to efficiently discover potential drugs for diverse indications, including genetic disease, inflammation, immunology, and infectious disease.
Recursion applies causative perturbations to human cells to generate disease models and associated biological image data.
Recursion's rich, relatable database of more than two petabytes of biological images generated in-house on the company's robotics platform enables advanced machine learning approaches to reveal drug candidates, mechanisms of action, and potential toxicity, with the eventual goal of decoding biology and advancing new therapeutics to radically improve lives.