Xaira Therapeutics offers an AI-driven platform that converts heterogeneous multi‑omics and perturb‑seq data into predictive models of disease biology, enabling in silico target identification, molecule design, and patient stratification. The system generates virtual cell simulations with diffusion language models and continuously refines predictions via a closed‑loop workflow, delivering results through a secure cloud API for integration with existing drug‑discovery pipelines.
Funding
Funding not disclosed
Founders
Product
Problem
Current drug discovery pipelines suffer from low predictive power, long timelines, and high attrition rates, leading to excessive R&D costs and delayed patient access to therapies. Existing experimental approaches generate fragmented data that are difficult to integrate into a coherent, actionable model of disease biology.
Solution
Xaira Therapeutics delivers an end‑to‑end AI platform that transforms heterogeneous biological data into predictive, agentic models spanning target identification, molecule design, and patient stratification. By training diffusion‑based language models on the world’s largest genome‑wide perturb‑seq datasets, the system generates “virtual cells” that simulate causal perturbations across diverse cellular contexts. These models enable rapid in silico screening of therapeutic modalities, reducing reliance on costly wet‑lab experiments. Integrated multi‑omics pipelines ingest proteomics, functional genomics, and clinical data, producing unified embeddings that capture disease mechanisms at molecular and phenotypic scales. The platform continuously refines its predictions as new experimental results are fed back, creating a closed‑loop engineering workflow that accelerates lead optimization and de‑riskes clinical development. All analytics are delivered through a secure cloud service with API access for downstream integration into existing drug‑discovery informatics stacks.
Target Audience
Primary customers are pharmaceutical R&D divisions, biotech companies, and contract research organizations seeking to accelerate target discovery and candidate optimization. The platform also serves academic drug‑discovery groups that require high‑throughput, data‑driven hypothesis generation.
Features
- Diffusion language models (e.g., X‑Cell) that generate high‑fidelity virtual cell simulations for causal perturbation prediction.
- Scalable perturb‑seq data platform (X‑Atlas/Orion) providing >10 billion single‑cell measurements across molecular to organismal scales.
- Multi‑omics integration engine that fuses proteomics, transcriptomics, and phenotypic readouts into unified latent representations.
- Automated target‑validation workflow that ranks candidates by predicted efficacy, safety, and developability using Bayesian optimization.
- AI‑driven de novo molecule and protein design pipelines leveraging RFdiffusion and foundation model architectures.
- Secure, HIPAA‑compliant cloud analytics with RESTful APIs for seamless connection to LIMS, ELN, and clinical data warehouses.
- Continuous model‑update loop that ingests experimental feedback to improve predictive accuracy over time.
- Dashboard and reporting suite offering quantitative risk metrics, progression curves, and patient‑cohort stratification visualizations.