Redwood AI Corp. (CSE: AIRX) (OTCQB: RDWCF) (Frankfurt: Y0N, WKN: A422EZ) is joining a Canadian research collaboration backed by the Terry Fox Research Institute to contribute its data integration and chemical modelling expertise to the development of SynoGraph(TM), an artificial intelligence platform being developed by Onco-Innovations Limited for oncology research. The project, titled “Causal AI to Predict First-in-Human Clinical Outcomes from Preclinical and Real-World Clinico-Genomic Data,” was announced on September 22 and names Onco as lead applicant, with Redwood AI, the Michael Smith Genome Sciences Centre and the Centre de recherche du CHU de Québec-Université Laval as co-applicants. The grant supports a project with an estimated budget of $3.5 million and reimburses eligible costs up to 25% of that amount.
Redwood AI will contribute proprietary data integration and physicochemical modelling methods to expand SynoGraph’s predictive capabilities. The project will investigate whether causal AI can improve predictions of first-in-human clinical outcomes using preclinical and real-world clinico-genomic data. Academic partners will provide clinico-genomic and imaging data for research using federated learning, which is intended to support analysis while protecting data privacy. This collaboration underscores the growing role of AI in drug development, where accurate predictions can reduce costs and accelerate timelines. For investors, the involvement of a major cancer research funder like the Terry Fox Research Institute signals confidence in the technology’s potential.
Redwood AI Corp. is the developer of an AI-powered platform for real-world applications across multiple critical industries. The company’s participation in this consortium highlights its capabilities in handling complex biomedical data and its commitment to advancing healthcare through AI. The project’s focus on causal AI—a branch of AI that seeks to understand cause-and-effect relationships—could lead to more reliable predictions than traditional correlation-based models. If successful, SynoGraph could become a valuable tool for oncology researchers and pharmaceutical companies, potentially improving patient outcomes by better predicting which treatments will work in humans.
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