This company develops proprietary deep generative AI models leveraging large datasets to advance human longevity. Their platform focuses on transforming drug discovery through technologies like molecule optimization and high-throughput virtual screening. They also offer AI-designed biomarker binders for improved diagnostics.
Funding
$2M 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
The traditional drug discovery and development lifecycle is protracted, resource-intensive, and carries a high risk of failure. This lengthy process hinders the rapid advancement of novel therapeutics and diagnostics, delaying patient access to potentially life-saving treatments.
Solution
DíaGen provides a generative AI platform, referred to as the "Día" engine, designed to accelerate the design and development of novel proteins and peptides. This platform enables the creation of custom molecules with specific functionalities, such as enhanced binding affinity, improved stability, or targeted therapeutic action. By leveraging advanced AI algorithms, DíaGen significantly reduces the time and cost associated with early-stage drug discovery and the development of advanced diagnostics and biosensors. The platform's capabilities extend to generating molecules for therapeutic applications, diagnostic assays, and stabilizing agents.
Target Audience
DíaGen targets pharmaceutical and biotechnology companies engaged in drug discovery and development, as well as organizations developing advanced diagnostics and biosensors.
Features
- Generative AI engine ("Día") for de novo protein and peptide design.
- Capabilities include designing molecules for enhanced binding, stabilization, adherence, therapeutic targeting, immune response generation, and disease detection.
- Application areas include therapeutics (e.g., oncology drugs, nanobodies), diagnostics (e.g., TnI for heart attack detection, Rhinosinusitis tests), and vaccine development.
- Proprietary AI models trained on extensive biological datasets for predictive molecular design.
- Focus on accelerating preclinical development and identifying lead candidates for licensing.
- Validation through peer-reviewed publications and experimental case studies in oncology and diagnostics.