Genialis develops AI models for precision oncology to improve drug efficacy and patient outcomes. The platform provides therapeutic intelligence and biomarker discovery to stratify patients and enhance clinical success rates. This approach aims to ensure that promising drug molecules successfully become life-saving medicines.
Funding
$13M 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.
DITCFounders
Product
Problem
Many cancer patients receive therapies that provide limited benefit, and a high proportion of oncology drug candidates fail in clinical trials due to inadequate patient selection and insufficient understanding of drug mechanisms and resistance pathways.
Solution
Genialis delivers AI‑powered therapeutic intelligence and biomarker discovery platforms that translate RNA‑sequencing data into actionable, clinically validated biomarkers. Its large molecular model (Supermodel) learns cancer biology from a globally diverse transcriptomic repository, enabling rapid generation of predictive classifiers for specific drug targets. Products such as krasID, Expressions, and ResponderID provide patient stratification, response duration forecasts, and mechanistic insights that guide trial design, combination strategies, and regulatory submissions. By integrating these models into secure cloud services, Genialis offers explainable analytics and API access that streamline decision‑making across preclinical, early‑clinical, and late‑stage development stages.
Target Audience
Primary customers are pharmaceutical and biotech companies developing oncology therapeutics, as well as diagnostic partners and academic researchers seeking predictive RNA‑based biomarkers for patient stratification and trial optimization.
Features
- Large Molecular Model (Supermodel) trained on hundreds of thousands of RNA‑seq samples to map high‑dimensional gene expression into biologically interpretable signatures.
- krasID: a KRAS‑inhibitor specific predictor that classifies patients by expected response, duration of benefit, and resistance mechanisms with >80 % accuracy in real‑world cohorts.
- Expressions platform: FAIR‑compliant data infrastructure that normalizes, bias‑corrects, and aggregates multi‑omics NGS data, delivering ML‑ready datasets via scalable microservices and Python‑based APIs.
- ResponderID pipeline: end‑to‑end workflow for rapid biomarker development, from raw sequencing to validated, code‑free visualizations and reports.
- Explainable machine‑learning classifiers that expose pathway‑level drivers of response and suggest rational combination therapies.
- HIPAA and GDPR‑compliant cloud deployment with role‑based access controls and optional private‑cloud installation on AWS‑certified infrastructure.
- Flexible licensing that allows customization for different drug targets, tissue types, and therapeutic modalities.