ProxAI develops AI-powered solutions for drug discovery, accelerating the identification and optimization of novel therapeutic compounds. Their platform leverages advanced machine learning algorithms to analyze complex biological data, enabling more efficient lead generation and preclinical development.
Funding
Funding not disclosed
Founders
Product
Problem
The discovery of novel peptide therapeutics is often slow and expensive, relying on traditional methods that struggle to efficiently identify and optimize peptides with desired therapeutic properties and high binding affinity to specific protein targets. Existing drug discovery processes lack the ability to rapidly iterate and refine peptide designs based on biological data, hindering the development of effective treatments.
Solution
ProxAI offers an AI-powered platform that accelerates the design and optimization of peptide therapeutics. Their generative AI platform designs peptide binders tailored to specific protein targets using state-of-the-art AI algorithms. The platform incorporates structure prediction to accurately model the structures of peptide-target pairs for rational drug design. ProxAI refines peptide designs by incorporating biological data into its AI-driven design pipeline, ensuring iterative improvement and enhanced predictive accuracy. The platform accelerates the development of new therapies at a fraction of the cost.
Target Audience
The primary customers are pharmaceutical companies and research institutions involved in drug discovery and development, specifically those focused on peptide therapeutics.
Features
- Generative AI platform for designing peptide binders tailored to specific protein targets.
- Structure prediction capabilities for accurate modeling of peptide-target pairs.
- AI-driven design pipeline that incorporates biological data for iterative improvement.
- High-throughput cell-based affinity screening to quantify and identify peptides with the highest binding affinities.
- CRISPR screen to identify peptides with high binding affinities.