Synthesize Bio provides a generative genomics platform that turns natural‑language experiment descriptions into high‑fidelity synthetic RNA‑seq data, enabling pharmaceutical and biotech teams to model patient responses, test trial designs, and uncover safety signals in silico. By expanding limited or failed clinical datasets into synthetic cohorts that retain biological variance, the platform accelerates biomarker discovery, companion‑diagnostic validation, and evidence generation for alternative indications while ensuring HIPAA‑compliant data sharing.
Funding
$10M 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.
3OMFounders
Product
Problem
Biopharma researchers often rely on limited clinical and tissue datasets, making it difficult to predict human responses, identify responder subgroups, and design optimal trial protocols, especially for rare diseases or early‑phase studies.
Solution
Synthesize Bio offers a generative genomics platform that converts natural‑language experiment descriptions into high‑fidelity synthetic RNA‑seq data grounded in the largest curated transcriptomic corpus. The system enables in‑silico modeling of patient responses across genetic backgrounds and enrollment criteria, allowing teams to test trial designs, refine inclusion/exclusion rules, and uncover safety signals before human studies. By expanding underpowered datasets into synthetic cohorts that retain key biological characteristics, the platform supports biomarker discovery, companion‑diagnostic validation, and evidence generation for alternative indications. All synthetic data are de‑identified, facilitating secure collaboration across organizations without HIPAA constraints.
Target Audience
Primary customers are pharmaceutical and biotechnology companies developing therapeutics who need to de‑risk trial design, accelerate biomarker discovery, and extract insight from limited or failed clinical datasets.
Features
- Large‑scale generative model trained on the most extensive annotated RNA‑seq corpus, producing lab‑quality expression profiles from textual experiment inputs
- In‑silico trial simulation tools that model patient responses across diverse genetic backgrounds and enrollment scenarios
- Synthetic cohort generation for rare diseases and small sample studies, preserving biological variance while expanding statistical power
- Automated safety‑signal surfacing and mechanism‑of‑action validation in human tissue contexts
- HIPAA‑compliant data sharing via synthetic datasets that contain no patient‑identifying information
- Integrated database of over 10,000 AI‑simulated cancer samples spanning 27 tumor types for rapid hypothesis testing