On February 20th, in Paris, the French startup Bioptimus announced it had completed a $35 million seed funding round, emerging from stealth mode. Under the leadership of Professor Jean-Philippe Vert, Bioptimus has brought together a world-class team of scientists from Owkin and Google DeepMind to advance translational biology and cutting-edge AI foundational model technologies, capturing biology at various scales.This round of financing was led by Sofinova Partners and Bpifrance Large Venture, with additional support from France-based global funds, including Frst and multi-stage VC firm Cathay Innovation Ventures. Global top-tier technology investors BV Capital, Hummingbird, NJF Capital, Owkin, and Top Harvest Capital, as well as renowned tech entrepreneur Xavier Niel, joined this round, establishing a global leader in the AI biology sector in France.
Bioptimus will connect different scales of biology with generative artificial intelligence;From molecules to cells, tissues, and entire organisms, driving scientific breakthroughs and accelerating innovation in biomedicine and beyond.
Professor Jean-Philippe Vert, Co-founder and CEO of Bioptimus, Chief R&D Officer of Owkin, and former Head of R&D at Google Brain, stated:"The application of foundational models and generative artificial intelligence in biology will have a profound impact on science. By harnessing the power of foundational models and advanced algorithms trained on large-scale biological and multimodal data, we aim to capture biological laws that have so far been too complex to properly understand. This holistic, cross-scale understanding of biology is crucial for accelerating biomedicine and environmental science."
Bioptimus will benefit from Owkin’s data generation capabilities and federated global access to multimodal patient data from world-leading academic hospitals, as well as Amazon (AWS)’s top-tier, scalable, and secure computing environment. Driven by rich data from all scales and modalities, this enables us to create computational representations that strongly differentiate themselves from models trained solely on public datasets and single data modalities that fail to capture the full diversity of biology.Follow the official account below to see the world!
