Yatiri Bio integrates advanced proteomics and deep learning models with patient data sets to guide therapeutic strategy and patient selection in drug discovery. The company builds bioinformatics tools to reveal disease-driving proteins and identify robust therapeutic signatures. This data-centric approach aims to accelerate clinical development by matching patients to the most effective treatments.
Funding
$4.6M 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
Traditional drug discovery methods are costly, time-consuming, and have high failure rates, with only a small percentage of therapeutics entering clinical trials achieving FDA approval. Pharmaceutical companies often rely on genetic data, overlooking the critical role of proteins as drivers of biology and disease. This approach leads to inefficiencies in patient stratification and selection for drug therapies.
Solution
Yatiri Bio utilizes high-resolution mass spectrometry and deep learning algorithms to analyze the proteome, enabling the identification of thousands of proteins and their post-translational modifications. By integrating patient data sets with empirical models, Yatiri Bio enhances patient stratification and selection for drug therapies. This approach facilitates the discovery of robust therapeutic signatures and positions empirical models within a learned space to efficiently predict patient outcomes. Yatiri Bio aims to match a patient’s proteomic readout to individualized therapeutics at the biochemical level.
Target Audience
Yatiri Bio's primary customers are pharmaceutical companies and clinical researchers seeking to improve drug discovery and development through proteomics and bioinformatics.
Features
- High-resolution mass spectrometry for proteomic analysis
- Deep learning algorithms for patient outcome prediction
- Identification of thousands of proteins and their post-translational modifications
- Integration of patient data sets with testable empirical models
- Discovery of robust therapeutic signatures
- Improved patient stratification and selection