The analysis of biological data has suffered from decades of fragmentation, with each modality—genetic, imaging, DNA—processed in isolation. Paris-based startup Bioptimus aims to tackle this issue by introducing generative AI models capable of integrating and linking these different data scales.
A multi-scale, multi-modal approach
Bioptimus’ AI models leverage multi-scale data, spanning microscopic genetic mutations to tissue-level analysis. The goal is to connect these various dimensions, providing a comprehensive view of organisms.
In 2025, Bioptimus plans to launch a model capable of linking genetic data to biological imaging, a first commercial product targeted at applications like identifying mutations responsible for cancer.
A strategy built on exclusive data
To ensure the performance of its models, Bioptimus combines public and private data sources. The startup has established strategic partnerships with research laboratories and has access to patient data from Owkin, which incubated the company. This strategy differentiates Bioptimus from other AI players that often rely on common data sources.
Increasing competition in biotechnology
Bioptimus operates in a highly competitive field, alongside major players like Google (via DeepMind/AlphaFold) and Microsoft, who are heavily investing in applying AI to life sciences. Startups in this domain face growing financing needs to stay competitive.
Fundraising to accelerate growth
Bioptimus has announced raising €41M in a Series A round led by Cathay Innovation, with participation from Bpifrance, Sofinnova Partners, Andera Partners, and Hitachi Ventures. International investors such as Sunrise, Boom Capital Ventures, and Pomifer Capital also contributed. This funding follows €35M raised in the Seed stage and will support the development of the startup’s first commercial model, as well as the recruitment of commercial and technical talent.
Founded by Jean-Philippe Vert, David Cahané, and Eric Durand, Bioptimus aims to transform the understanding of living organisms and biological processes.
