Somerra
Somerra.ai develops a Virtual Organ platform that combines programmable organoids—living 3D human tissues derived from patient stem cells using synthetic-biology gene circuits—with machine learning to predict drug responses across untested conditions. The system trains on experimental data spanning donors, drugs, doses, and schedules, then actively selects the next experiment to run, accelerating drug discovery while reducing reliance on costly clinical trials.
- Drug Discovery & Therapeutics
- Biotechnology
- Artificial Intelligence
- Healthcare Technology
- Data & Analytics
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Clinical trials require years of time, substantial capital, and expose human participants to risk in order to determine whether a drug is safe, effective, and for whom. The knowledge gained from each trial is largely siloed within a single program, so learning is not cumulative across drugs, doses, or patient populations, making drug development slow and inefficient.
Solution
Somerra.ai builds a Virtual Organ—a predictive model trained on data from programmable organoids, which are living three-dimensional human tissues derived from patient stem cells using gene circuits pioneered at MIT. Each organoid experiment tests drugs, doses, and schedules across multiple donors, and the resulting data continuously trains the Virtual Organ. The model then predicts how untested conditions will behave and selects which experiment to run next, closing a loop that accelerates discovery. This platform enables researchers to explore far more of the therapeutic space than traditional trial methods allow, while reducing reliance on costly, time-consuming human studies that start from scratch each time.
Target Audience
Somerra.ai serves pharmaceutical companies, biotech firms, and clinical research organizations seeking to de-risk drug development by replacing or augmenting conventional preclinical and clinical testing with predictive tissue models.
Features
- Programmable organoids created from patient-derived stem cells using synthetic-biology gene circuits, enabling reproducible and customizable human tissue models
- Virtual Organ platform that aggregates experimental data across donors, drugs, doses, and schedules to make predictions about untested conditions
- Active learning loop where the model recommends the next experiment to maximize information gain and iteration speed
- Machine-learning infrastructure designed and scaled by a co-founder who previously built ML systems for protein design at Generate:Biomedicines
- End-to-end workflow that transforms raw organoid data into predictive insights for drug development decisions