Aizen Therapeutics utilizes a novel AI model, DaX™, trained on receptor-ligand physics, to design modern peptide drugs incorporating non-canonical amino acids. This platform expands the accessible design space for peptide therapeutics beyond naturally occurring building blocks. The company focuses on guided diffusion to engineer oral biologics capable of targeting complex indications like cytokine inhibition.
Funding
$13M 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.
MFounders
Product
Problem
Traditional peptide therapeutics face challenges related to stability and immunogenicity, limiting their effectiveness and potential for treating neurological disorders, oncology, and genetic diseases. Natural L-amino acid peptides are susceptible to enzymatic degradation and can trigger unwanted immune responses, hindering their development as safe and effective drugs.
Solution
Aizen is developing Mirror Peptides, a novel class of fully D-amino acid peptides, to overcome the limitations of traditional peptide therapeutics. These synthetic peptides exhibit enhanced therapeutic stability and reduced immunogenicity, offering improved safety and efficacy for treating a range of diseases. Aizen's DaX™ platform, a structure-based protein design model powered by Generative AI, accelerates the discovery and development of Mirror Peptides by enabling rapid design-build-test cycles and improving hit rates against clinically relevant targets. The platform's multi-objective optimization refines key drug properties like specificity, half-life, and solubility, transforming the speed and scope of drug development.
Target Audience
Aizen's primary target audience includes pharmaceutical companies and research institutions focused on developing novel therapeutics for neurological disorders, oncology, and genetic diseases.
Features
- Mirror Peptides composed of fully D-amino acids for enhanced stability and reduced immunogenicity
- DaX™ platform utilizing structure-based protein design and Generative AI for de novo Mirror Peptide structure generation
- Guided diffusion, computational binding models, and GPU compute architecture for efficient drug development
- Rapid design-build-test cycles completed in approximately 4 weeks
- Multi-objective optimization for simultaneous refinement of drug properties, including specificity, half-life, and solubility
- Demonstrated success against multiple transmembrane receptor targets