About
Euretos is a comprehensive platform designed for data-driven translational research in the life sciences and biopharma industry. At its core is a massive biological knowledge graph that seamlessly integrates scientific literature, patents, experimental data, and real-world clinical patient data. This unified data environment enables researchers to generate novel insights that would be impossible to surface through manual desk research alone. The platform offers a broad suite of ready-to-use applications and workflows covering key research tasks: identifying disease markers and targets using multi-omics criteria, analyzing single-cell expression profiles, comparing tumor versus healthy tissue expression, ranking targets by disease association and clinical stage, and selecting appropriate cell lines for in-vitro experiments. A standout capability is Euretos's AI-predicted gene-disease associations, powered by machine learning models trained on biological networks including co-expression data, protein-protein interactions, and pathway information. These models can quantify gene-disease relationships even when no direct evidence exists, opening new avenues for target discovery. Euretos is built for research scientists and biopharma professionals who need the power of bioinformatics and data science without deep technical expertise. It has a proven track record, having contributed to approved drugs and active clinical trials in oncology, Sjögren's disease, Ulcerative Colitis, NASH, and epilepsy across five biopharma clients. Enterprise customers such as Teva Pharmaceuticals rely on it for advanced target scouting, literature analysis, and hypothesis generation.
Key Features
- Biological Knowledge Graph: Integrates literature, patents, experimental results, and real-world clinical data into a unified, queryable biological knowledge graph for holistic research insights.
- AI-Predicted Gene-Disease Associations: Machine learning models leverage biological networks (co-expression, PPIs, pathways) to calculate gene-disease scores, even without direct evidence.
- Multi-Criteria Target Ranking: Ranks drug targets across disease associations, tissue expression, protein-protein interactions, and clinical stage to prioritize the most promising candidates.
- Single-Cell & Tumor Expression Analysis: Understand single-cell expression profiles and identify cancer targets based on differential expression between healthy and tumor tissues with a single click.
- Cell-Line Selection & Experiment Design: Assess target expression across cell lines to plan and optimize in-vitro experiments before committing lab resources.
Use Cases
- Biopharma R&D teams identifying and ranking novel drug targets using multi-omics and clinical data in early-stage drug discovery
- Oncology researchers analyzing differential gene expression between healthy and tumor tissues to prioritize cancer target candidates
- Scientists designing in-vitro experiments by selecting the most relevant cell lines based on target expression profiles
- Translational researchers performing gene set enrichment analysis against multi-omics databases to validate biological hypotheses
- Life science companies accelerating literature and patent scouting to assess competitive landscape and freedom-to-operate for therapeutic programs
Pros
- No Bioinformatics Skills Required: Ready-to-use workflows make advanced data-driven research accessible to wet-lab scientists and non-computational researchers.
- Proven Real-World Impact: The platform has contributed to approved drugs and active clinical trials across oncology, autoimmune, metabolic, and neurological diseases.
- Comprehensive Data Integration: Combines literature, patents, experimental, and clinical data in one place, eliminating the need to query and reconcile multiple disparate sources.
- Novel AI-Driven Insights: Predictive gene-disease scoring surfaces non-obvious associations, enabling researchers to identify targets that traditional literature searches would miss.
Cons
- Enterprise Pricing: Euretos is aimed at biopharma organizations; pricing is not publicly listed and likely requires a sales consultation, making it inaccessible for individual researchers or small teams.
- Specialized Domain Focus: The platform is tightly focused on drug discovery and translational biology, limiting applicability outside the life sciences and biopharma sectors.
- Learning Curve for Workflows: Despite not requiring bioinformatics expertise, users may need onboarding and mentoring to fully leverage the platform's depth of features and applications.
Frequently Asked Questions
What kind of data does the Euretos knowledge graph include?
The Euretos knowledge graph integrates scientific literature, patents, experimental datasets, multi-omics databases, and real-world clinical and patient data into a unified biological network.
Do I need bioinformatics expertise to use Euretos?
No. The platform is designed to empower researchers without bioinformatics or data science skills through ready-to-use applications and guided workflows. Professional support is also available.
How does Euretos predict gene-disease associations?
Euretos uses machine learning models trained on biological networks — including co-expression data, protein-protein interactions, and pathways — to score gene-disease relationships, even when direct evidence is absent.
What therapeutic areas has Euretos been used in?
Euretos has contributed to drug discovery and clinical development in oncology, Sjögren's disease, Ulcerative Colitis, NASH (non-alcoholic steatohepatitis), and epilepsy.
How can I get access to the Euretos platform?
Access is granted through a request process via the Euretos website. Pricing and access details are available upon inquiry, typically targeting biopharma teams and research institutions.
