This company provides a physics-driven generative AI platform for drug discovery, specializing in mapping the dynamic conformational landscape of therapeutic targets. Its technology uncovers previously inaccessible binding pockets to design novel drug-like molecules and predict binding affinity with experimental accuracy. The platform accelerates preclinical development by screening candidates for toxicity, ADME properties, and potential drug repurposing opportunities.
Funding
$93.9K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Traditional drug discovery methods often rely on static protein structures, failing to capture the dynamic conformational changes that proteins undergo in physiological conditions. This incomplete understanding of target structures limits the accuracy of virtual screening and hinders the development of effective therapeutics, especially for targets considered "undruggable."
Solution
NEBULA provides a dataset-free generative AI platform that maps the complete 3D structural landscape of macromolecules, revealing the myriad shapes that therapeutic targets can adopt. By integrating physics-based modeling with generative AI, NEBULA generates exhaustive profiles of potential target structures without relying on pre-existing datasets. This comprehensive profiling enhances the accuracy of virtual screening, increasing the chances of identifying effective drug candidates and unlocking previously undruggable targets. NEBULA partners with pharmaceutical and biotech companies, offering customized solutions to enhance their drug discovery processes and reduce development time.
Target Audience
The primary target audience includes pharmaceutical and biotech companies seeking to improve their drug discovery process, particularly those working on challenging or previously undruggable targets.
Features
- Dataset-free generative AI to model all possible 3D shapes of therapeutic targets
- Physics-based modeling to capture dynamic conformations and structural changes
- Identification of cryptic and ephemeral binding pockets for novel allosteric modulation
- Comprehensive target profiling to enhance the accuracy of virtual screening
- Integration with existing drug discovery workflows
- Focus on enabling previously "undruggable" targets
- Application to therapies for hard-to-treat cancers and CNS disorders