Orakl Oncology Raises $34M to Predict Cancer Drug Results with AI
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Orakl Oncology raises $34M to predict cancer drug results with AI
Paris-based Orakl Oncology has raised a $34M Series A co-led by Norrsken VC and Singular. It grows copies of patients’ tumours in the lab, tests drugs on them and uses AI to predict trial results. It will expand into lung and gastric cancer. Paris-based Orakl Oncology has raised a $34M Series A to predict how […] This story continues at The Next Web
Orakl Oncology Raises $34 Million with InfraVia Backing to Scale AI-Powered Cancer Drug Development
The Series A co-led by Norrsken VC and Singular will support expansion into new cancer types and therapeutic target discovery following reported 90% accuracy in clinical trial prediction. IN BRIEFKEY TAKEAWAYSFAQs InfraVia Growth has invested in Orakl Oncology as part … Read the full press release →
Orakl raises $34M to scale AI platform predicting cancer drug responses - European Biotechnology Magazine
Paris-based Orakl Oncology has raised an oversubscribed $34 million (€31 million) Series A, co-led by Swedish investor Norrsken VC and French venture firm Singular, to expand its platform combining patient-derived tumour models with AI to predict cancer drug responses. The financing follows agreements in July giving the company exclusive rights to evaluate four Sanofi oncology assets.
Born from Gustave-Roussy's reference cancer control centre, Orakl aims to predict the effectiveness of experimental cancer treatments from virtual patient cohorts. The company completes a $34 million A series co-directed by Norrsken and Singular, with support from InfraVia, Serena, Bpifrance, Teampact and Plug and Play.
Orakl Oncology Raises $34M to Scale Virtual Cancer Patient Cohorts
What You Should Know Virtual Cancer Patients: Merging Tumor Organoids with Clinical AI Founded in 2023 as a spin-out of the Gustave Roussy cancer campus in France, Orakl bridges wet-lab experimental biology and machine learning by constructing cohorts of virtual cancer patients. Rather than training algorithms exclusively on static, retrospective genomic databases, the company cultures ...
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