Rezo offers a cloud‑based multi‑omics platform that integrates genomics, proteomics, structural biology, and chemistry data into unified disease networks for oncology research. Its AI‑driven pipeline predicts protein structures, annotates binding sites, and scores druggable targets, enabling pharma and biotech teams to query, visualize, and export hypotheses for rapid preclinical validation.
Funding
Funding not disclosed
Founders
Product
Problem
Identifying actionable, druggable targets in oncology is hampered by fragmented omics datasets and limited insight into how genetic, proteomic, and structural alterations interact within disease pathways. Traditional discovery pipelines often rely on single‑layer analyses, leading to missed opportunities and prolonged development timelines. Consequently, many potential therapeutic interventions remain undiscovered or de‑prioritized.
Solution
Rezo provides an integrated multi‑omics platform that consolidates proteomics, genomics, structural biology, chemistry, and bioinformatics into unified molecular disease networks. The system applies advanced computational modeling and machine‑learning algorithms to translate raw sequence data into functional protein structures and interaction maps. By overlaying these layers, the platform reveals high‑confidence, druggable nodes and pathways specific to cancer subtypes. Researchers can query, visualize, and export target hypotheses through a secure cloud interface, accelerating the transition from discovery to preclinical validation. The approach supports iterative hypothesis testing, enabling rapid refinement of precision‑therapeutic strategies.
Target Audience
Primary customers are pharmaceutical R&D divisions and biotech drug‑discovery teams focused on oncology, as well as academic research groups seeking comprehensive disease‑network insights for target validation.
Features
- Automated pipeline that ingests and harmonizes proteomic, genomic, and structural datasets into a single graph database
- High‑resolution in silico protein structure prediction and binding‑site annotation using state‑of‑the‑art deep‑learning models
- AI‑driven target scoring engine that ranks candidates based on druggability, pathway centrality, and disease relevance
- Interactive network visualization dashboard with customizable filters for tissue, mutation type, and functional annotation
- RESTful API and SDKs for seamless integration with downstream assay platforms, HTS workflows, and LIMS systems
- Scalable cloud compute environment leveraging GPU‑accelerated HPC clusters for large‑scale analyses
- Enterprise‑grade security and compliance (HIPAA, GDPR) with role‑based access controls and encrypted data storage