NEBULA: Enabling the Undruggable
Physics-driven generative AI that opens a drug-discovery space that no dataset has ever reached.
NEBULA’s dataset-free AI technology discovers every 3-D conformation a therapeutic target can adopt, revealing details that conventional methods cannot access. By unlocking targets that have long been labeled undruggable, we fast-track novel therapies—while competitors are still searching for data.
The Drug Discovery’s 80 % blind spot
Only 20 % of the ~5,000 identified therapeutic protein targets are currently “druggable.”
80 % remain undruggable, blocking innovation and driving late-stage failures, while competition for the small slice grows fiercer.
Harnessing the capabilities of dataset-free Generative AI to tackle undruggable drug targets
NEBULA’s platform unveils dynamic 3-D conformations of proteins without relying on pre-existing datasets, revealing shapes traditional methods can’t access to.
This opens-up treatment avenues for diseases for which therapeutic targets have long been considered as impossible to develop drugs for.
Pipeline Focus: Cancer and Neurological Disease
We apply NEBULA Enabeling Technology to develop therapies for hard-to-treat cancers (e.g. advanced pancreatic, lung, and blood cancers) and CNS disorders (such as drug-resistant epilepsy and progressive neurodegeneration).
Meet the TEAM
See who is behind the company
Daniel Wiczew
An entrepreneur with deep expertise in artificial intelligence, biomedical sciences, and drug design. Daniel brings unparalleled technical knowledge to NEBULA, driving our technology’s development and application in drug discovery.
Mounir Tarek
A senior research director with extensive experience in molecular modeling and computational biophysics. Mounir leads NEBULA with a vision to transform the pharmaceutical industry through innovative AI technologies.
Fabrice Balavoine
An accomplished biotech executive with over 20 years of leadership in drug discovery and pharmaceutical development. Fabrice drives NEBULA’s commercial growth by securing strategic partnerships and leveraging his proven success.
SPECIAL THANKS TO
Frequently Asked Questions
Q1: How does NEBULA’s technology differ from traditional drug discovery methods?
NEBULA’s AI-driven platform provides a comprehensive mapping of therapeutic target structures, capturing dynamic conformations that traditional methods often miss. This enhances the accuracy of virtual screening and increases the likelihood of identifying effective drug candidates.
Q2: How does NEBULA compare to AlphaFold in protein structure prediction?
While AlphaFold revolutionized protein science by predicting static 3D structures of proteins from amino acid sequences, it primarily provides a single, most probable conformation. Proteins in physiological conditions are dynamic and can adopt multiple conformations. NEBULA goes beyond static predictions by mapping the full spectrum of a protein’s possible shapes. This dynamic profiling offers deeper insights into how drugs can interact with targets in real-world conditions, enhancing the success rate of drug discovery.
Q3: Can NEBULA integrate with our existing drug discovery processes?
Absolutely. Our solutions are designed to complement and enhance your current workflows. We provide detailed target profiling that seamlessly integrates into your virtual screening and drug design processes.
Q4: How can pharmaceutical companies partner with NEBULA?
Pharmaceutical and biotech companies can partner with NEBULA through our flexible, project-based service model. We offer customized solutions tailored to the complexity of your therapeutic targets, including initial setup, milestone deliverables, and revenue-sharing agreements. This collaborative approach aligns our success with yours, fostering a strong partnership for mutual benefit.
Q5: What support does NEBULA provide during a project?
Our team offers comprehensive support, including expert consultation, detailed data analysis, and ongoing collaboration. We’re committed to ensuring optimal outcomes for your drug discovery efforts.
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