Bifrost provides a platform that connects single-cell phenotypes, such as morphology and protein localization, to underlying alleles at massive scale. Its integrated software facilitates rapid progression from experimental data to actionable insights by linking cellular features to genetic codes. This capability accelerates early drug discovery by enabling developers to identify and advance promising candidates sooner while de-risking costly development failures.
Funding
Funding not disclosed
Founders
Product
Problem
Drug discovery is a costly and failure-prone process, often hampered by a lack of high-quality, early-stage insights into the relationship between cellular phenotypes and their underlying genotypes. Traditional arrayed screening methods are slow and expensive, while existing pooled screening approaches lack the resolution to reliably link single-cell characteristics to genetic variations.
Solution
Bifrost provides a biological data analytics platform that connects single-cell phenotypes, such as morphology, protein localization, cellular responses, and cell-cell interactions, to their corresponding genotypes at scale. The platform employs optical pooled screening, combining high-resolution immunofluorescent imaging with next-generation sequencing to analyze tens of millions of cells cost-effectively. Integrated software and advanced machine learning algorithms then rapidly cluster related alleles and extract insights, enabling drug developers to identify viable therapeutic targets and de-risk projects earlier in the discovery phase. By providing significantly more high-quality information, Bifrost helps reduce costs and accelerate the development of critical drugs.
Target Audience
The primary target audience consists of drug developers and pharmaceutical companies seeking to improve the efficiency and success rate of their drug discovery programs by leveraging high-quality, early-stage insights into cellular biology.
Features
- High-resolution immunofluorescent imaging for detailed phenotypic analysis at the single-cell level
- Next-generation sequencing technology for accurate and comprehensive genotype identification
- Integrated platform linking single-cell phenotypes to genotypes at massive scale (50 million per week)
- Advanced software analysis and machine learning algorithms for rapid clustering of related alleles
- Identification of potential therapeutic targets based on phenotype-genotype correlations
- Pooled screening technology enabling cost-effective analysis of tens of millions of cells