Mapping drug-target shapes and designing candidate molecules with physics-based AI
Drug targets adopt multiple three-dimensional shapes, with potential binding sites that can be difficult to identify. Insufficient structural data can leave targets beyond the reach of drug discovery, limiting the search for molecules that interact with them effectively.
NEBULA uses physics-based generative AI to map changing drug-target structures and design candidate molecules. Its platform helps pharmaceutical and biotech teams explore binding sites and screen candidates for predicted drug properties before laboratory testing.
What stands out about NEBULA
Won France’s i-Lab 2025 innovation competition for its computational drug-discovery technology.
Co-founded by Mounir Tarek, a CNRS research director working at the intersection of AI and computational biophysics.
Supported by Incubateur Lorrain and SATT Sayens in developing its deeptech venture.
Originated from innovations at LPCT, the joint CNRS and Université de Lorraine laboratory, with the company created in 2024.
Selected for the 2026 DayOne Accelerator, a three-month Basel programme connecting startups with pharma companies, investors and experts.
Selected as a laureate of the 2024 Prix Pépite for its biotechnology and AI venture.
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