A Team of DeepMind Alumni Created The 'Bioptimus' AI Model for Biology
Colin Smith — February 21, 2024 — Tech
References: bioptimus & venturebeat
Bioptimus is a new startup that aims to create the first universal AI foundation model for biology. A foundation model is a machine learning framework that is trained on a large and diverse dataset, and can be adapted to various tasks and domains. Bioptimus plans to use this approach to integrate different scales of biological data, from molecules to cells to tissues, and generate new insights for biomedicine.
The company was founded by former scientists from Google DeepMind and Owkin, a French AI company that works with top biopharmas. Bioptimus has raised $35 million in seed funding from leading investors, including Index Ventures, Bpifrance, Frst, and Cathay Innovation. Bioptimus will leverage Owkin’s unique multimodal patient data, collected from partnerships with academic hospitals around the world, to train its foundation model. Bioptimus also has a partnership with Amazon Web Services to use its cloud computing infrastructure. Bioptimus hopes to transform biology and accelerate scientific breakthroughs with its ambitious project.
Image Credit: Shutterstock
The company was founded by former scientists from Google DeepMind and Owkin, a French AI company that works with top biopharmas. Bioptimus has raised $35 million in seed funding from leading investors, including Index Ventures, Bpifrance, Frst, and Cathay Innovation. Bioptimus will leverage Owkin’s unique multimodal patient data, collected from partnerships with academic hospitals around the world, to train its foundation model. Bioptimus also has a partnership with Amazon Web Services to use its cloud computing infrastructure. Bioptimus hopes to transform biology and accelerate scientific breakthroughs with its ambitious project.
Image Credit: Shutterstock
Trend Themes
1. Universal AI Foundation Models - Potential for revolutionizing how biological data is analyzed and utilized across various fields.
2. Integration of Biological Data Scales - Opportunity to uncover new insights by combining molecular, cellular, and tissue-level data.
3. Leveraging Multimodal Patient Data - Harnessing diverse patient data sources to enhance AI training and drive advancements in biomedicine.
Industry Implications
1. Healthcare and Biomedicine - AI-powered tools could reshape diagnostics, drug development, and personalized medicine.
2. Cloud Computing Services - Partnerships with tech giants offer scalable infrastructure for processing large biological datasets.
3. Investment and Venture Capital - Growing interest in AI-driven biology startups presents opportunities for funding innovative projects.
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