Proteinea employs proprietary AI models and computational tools to engineer next-generation antibodies, enhancing their efficacy, safety, and convenience for therapeutic applications. The platform addresses the challenges of traditional protein engineering by optimizing protein characteristics and accelerating lead validation through a combination of advanced computational and experimental methods.
Funding
$610K 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 antibody engineering methods face challenges in optimizing multiple protein characteristics simultaneously, often resulting in trade-offs between efficacy, safety, and developability. Existing computational tools may introduce biases, while experimental validation can be slow and costly.
Solution
Proteinea offers an AI-enabled biologics design platform that engineers next-generation antibodies with improved efficacy, safety, and convenience. The platform leverages proprietary AI models, including the Ankh protein transformer, and computational tools to explore a wide range of sequence and structure space. By integrating physics-based tools, AI-based predictors, and molecular dynamics pipelines, Proteinea evaluates and optimizes key protein characteristics, such as efficacy, yield, safety, and pharmacological properties. The platform incorporates experimental lead validation capabilities with specialized lab facilities for therapeutic protein production and functional testing, enabling knowledge-guided optimization through iterative feedback loops.
Target Audience
The primary target audience includes pharmaceutical and biotechnology companies seeking to develop next-generation antibody therapeutics with improved properties, as well as researchers in protein engineering and AI-driven drug discovery.
Features
- Ankh: A general-purpose protein transformer exceeding the performance of current state-of-the-art models with less than 10% of the parameters.
- Multi-parameter optimization: Integrates Ankh and other proprietary models to enhance multiple protein parameters like stability and solubility.
- FC Engineering (Fc-SubQer): Enables subcutaneous antibody formulation without detrimental effects on affinity, stability, or biological functions.
- RaGene: An AI-driven gene design platform specializing in codon optimization for maximal expression yield.
- Computational pipelines utilizing diverse learning objectives to overcome computational bias.
- Evaluation of generated hits using physics-based tools, AI-based predictors, and molecular dynamics.
- Specialized lab facilities for therapeutic protein production and functional testing.