Turbine's Simulated Cell™ platform utilizes AI to predict biological responses by simulating experiments across various models, including engineered cell lines and patient samples. This technology accelerates drug development by generating actionable insights in half the time of traditional methods, enabling researchers to identify optimal experimental pathways for novel cancer therapies.
Funding
$38.1M 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.
Founders
Product
Problem
Traditional drug development relies on wet lab experiments, which are time-consuming and costly, and may not accurately predict clinical outcomes due to the complexity of patient biology. The lack of predictive models hinders the efficient identification of promising drug candidates and optimal treatment strategies.
Solution
Turbine offers Simulated Cell™, an AI-powered platform that simulates biological experiments across various models, including engineered cell lines and patient samples, to predict drug responses. The platform learns the universal language of cellular behavior from scalable experiments, applying its knowledge to predict the response of models it has never seen before. This approach enables researchers to generate actionable insights in a fraction of the time required by traditional methods, identify novel targets, and optimize drug development pathways. Turbine's technology helps explain the biological mechanisms at play, revealing hypotheses with the greatest potential to benefit patients and identifying the exact experiments needed for validation. The platform's virtual lab environment allows scientists to run millions of in silico experiments, investigate hypotheses across cell samples, and test therapeutic perturbations before wet lab validation.
Target Audience
The primary target audience includes biopharmaceutical companies, drug developers, and researchers involved in oncology and drug discovery, seeking to accelerate R&D and improve clinical outcomes.
Features
- AI-driven simulation of biological experiments in cell lines, animal models, and patient samples
- Prediction of drug response and identification of novel drug targets
- ADC Payload Selector for payload ranking and positioning based on predicted sensitivity profiles
- Clinical Positioning Suite powered by harmonized patient datasets
- Virtual patient data library to enhance the Clinical Positioning Suite
- Identification of best responder patients for specific programs
- Simulation of payloads to match ADCs to the right patients
- Uncovering of novel targets to expand pipelines
- DDR capabilities to unlock the commercial potential of DDRis