Unobio offers an end‑to‑end bioplatform that begins drug discovery with patient‑derived real‑world data and continuously validates candidates against human biology. Its AI foundation model predicts protein‑compound interactions and a generative chemistry engine proposes and ranks up to 100,000 analogues, while an active‑learning loop drives weekly robotic synthesis and organoid testing to deliver IND‑enabling packages and Phase 1 protocols. The platform accelerates deal velocity and improves opportunity yield for pharma, biotech, and academic discovery teams.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional drug discovery pipelines focus on in‑vitro hits and animal models, leaving translation to human biology as a downstream step. This mismatch causes most candidates to fail in Phase 2, resulting in high attrition and wasted resources.
Solution
Unobio provides an end‑to‑end bioplatform that starts every discovery program with patient‑derived data and continuously validates candidates against human biology. A foundation model trained on 1.2 billion annotated sequences predicts protein‑compound interactions, while generative chemistry proposes up to 10⁵ analogues ranked for potency, ADMET, and synthesisability. An active‑learning loop selects the most informative 1,000 compounds each week for robotic synthesis and organoid testing, ensuring rapid in‑silico to wet‑lab iteration. The platform integrates a biomarker‑led translational team that designs patient stratification hypotheses from day 0 and partners with academic medical centers to deliver IND‑enabling packages and Phase 1 protocols. Continuous real‑world data sharing via live data rooms keeps partners updated in real time, accelerating deal velocity and increasing qualified opportunity yield.
Target Audience
Primary customers are pharmaceutical companies, biotech firms, and academic drug‑discovery groups seeking to accelerate candidate translation and reduce Phase 2 attrition.
Features
- Foundation AI model (1.2 B annotated sequences) delivering 94 % concordance between in‑silico predictions and wet‑lab results
- Multi‑modal target identification across genomics, transcriptomics, and proteomics using patient‑derived tissue and organoids
- Generative chemistry engine generating 10⁵ scaffold analogues with automated ranking for potency, ADMET, and synthetic feasibility
- Active‑learning workflow that selects 1,000 high‑impact compounds weekly for robotic synthesis and organoid assay
- Integrated biomarker design and patient stratification built into discovery, enabling ready‑to‑run Phase 1 protocols
- Live data room with weekly telemetry updates, providing partners continuous visibility into assay outcomes
- Validated CRO network and internal preclinical lab delivering DMPK, toxicology, and CMC packages
- Investigator network across leading academic centers (Karolinska, Memorial Sloan Kettering, INSERM) supporting global Phase 1 execution