Develops a drug discovery platform that leverages a proprietary database of over 200 million microbial genes and machine learning to identify novel miniprotein and peptide therapeutics. This approach targets unmet clinical needs in immune and cardiometabolic disorders by discovering stable, orally administered scaffolds and advancing multiple candidates toward clinical development.
Funding
$6.6M 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
Current drug discovery methods often struggle to identify novel therapeutic candidates, particularly for complex diseases like immune and cardiometabolic disorders. Traditional approaches may not fully leverage the potential of naturally occurring molecules and can be limited by challenges in oral bioavailability and stability.
Solution
Wild employs a drug discovery platform that mines a proprietary database of over 200 million microbial genes to identify novel miniprotein and peptide therapeutics. The platform leverages machine learning to identify bioactive molecules and stable scaffolds suitable for oral administration. By mimicking evolutionary adaptations found in the animal kingdom, Wild aims to discover and develop innovative therapeutics that address unmet clinical needs in immune and cardiometabolic disorders. The platform includes a structural catalog of diverse miniproteins and peptides, including cyclic and knotted structures, enabling the discovery of molecules with unique therapeutic attributes.
Target Audience
The primary target audience includes pharmaceutical companies seeking novel therapeutic candidates and strategic partnerships to expand their pipelines in immune and cardiometabolic disease areas.
Features
- Wild DB: A database of over 200 million microbial genes sampled from diverse environments across six continents.
- Proprietary structural catalog of highly diverse miniproteins and peptides, including cyclic and knotted structures.
- Machine learning-powered mining of bioactive molecules.
- Large-scale synthetic biology screening for hit validation.
- Bioactivity testing and in-vivo characterization for lead optimization.
- Focus on discovering stable scaffolds suitable for oral administration.
- Identification of mimicry structures inspired by animal adaptations.