Generare lands €20M to accelerate AI-designed molecules
French biotech startup Generare has raised a €20 million funding round co-led by venture capital firms Alven and Daphni, aiming to radically speed up the discovery of new therapeutic molecules. The company targets a bold milestone: producing up to ten times more novel molecules by 2027 compared with traditional discovery pipelines.
AI and automation at the core of Generare’s strategy
Generare is building an integrated platform that combines AI algorithms, high-throughput experimentation and advanced computational chemistry to design, generate and validate drug candidates. By automating key stages of early-stage research, the startup seeks to reduce both the time and cost associated with discovering new molecules.
The platform is designed to continuously learn from experimental data, allowing machine learning models to propose increasingly sophisticated molecular structures. This feedback loop, supported by robotic labs and cloud-based analytics, is expected to significantly expand the chemical space that pharmaceutical partners can explore.
Backing from leading European venture investors
The round, co-led by Alven and Daphni, underlines growing investor confidence in data-driven drug discovery. Both funds have a track record of supporting deep-tech and life sciences startups that operate at the intersection of biotechnology, software and automation.
Existing and new investors are expected to support Generare in scaling its scientific team, expanding lab infrastructure and strengthening partnerships with global pharmaceutical companies and biotech firms. The fresh capital will also be used to industrialise its platform so it can handle a dramatically higher volume of design–build–test cycles.
Ambition: 10x more novel molecules by 2027
With this funding, Generare plans to validate its claim of producing “10x novel molecules” by 2027, positioning itself as a strategic partner for drug developers seeking differentiated pipelines. If successful, its approach could help address long-standing bottlenecks in drug discovery, from hit identification to lead optimisation, and potentially shorten the path from concept to clinic.