Turbine
Turbine is a biotech company specializing in predictive cell simulation technology that leverages machine learning and biological modeling to guide drug discovery and development. Their platform, Simulated Cell™, models cellular signaling to predict responses to therapies, identify novel targets, and optimize clinical strategies. They collaborate with leading pharma companies and research institutes to improve the success rate of oncology treatments and other complex diseases.
Industries
Nr. of Employees
small (1-50)
Products
Virtual Lab (computational platform) with ADC Payload Selector and Clinical Positioning modules
A cloud-accessible simulation engine and user interface that enables large-scale in silico pharmacology screens, payload ranking, combination testing, and generation of mechanistic hypotheses tied to experimental workplans.
EFFECT Benchmark Suite (model evaluation dataset and tools)
A public benchmark release providing curated train/test splits for gene essentiality and drug sensitivity prediction, evaluation scripts, bias detector notebooks, and example prediction formats to standardize model assessment.
Virtual Lab (computational platform) with ADC Payload Selector and Clinical Positioning modules
A cloud-accessible simulation engine and user interface that enables large-scale in silico pharmacology screens, payload ranking, combination testing, and generation of mechanistic hypotheses tied to experimental workplans.
EFFECT Benchmark Suite (model evaluation dataset and tools)
A public benchmark release providing curated train/test splits for gene essentiality and drug sensitivity prediction, evaluation scripts, bias detector notebooks, and example prediction formats to standardize model assessment.
Services
Target selection via in silico cell simulations
Computational identification and prioritization of novel therapeutic targets or target contexts by simulating genetic perturbations and analyzing mechanistic drivers of phenotype.
ADC payload selection and optimization
Virtual lab service to rank payload classes and payload combinations, predict payload sensitivity across cancer subtypes, and identify patient stratification biomarkers for ADC programs.
Clinical positioning and patient stratification
Identification of responder populations, candidate eligibility criteria, and biomarker-driven clinical positioning using virtual patient avatars and mechanistic simulation outputs.
DDR program acceleration and synthetic lethality discovery
Simulation-led discovery of synthetic lethal interactions, combination strategies, and biomarkers focused on DNA damage response (DDR) inhibitor programs.
Virtual Lab platform access (beta)
Cloud-based virtual lab providing modules for large-scale in silico experiments (e.g., payload selector, clinical positioning) and access to a library of virtual models.
Target selection via in silico cell simulations
Computational identification and prioritization of novel therapeutic targets or target contexts by simulating genetic perturbations and analyzing mechanistic drivers of phenotype.
ADC payload selection and optimization
Virtual lab service to rank payload classes and payload combinations, predict payload sensitivity across cancer subtypes, and identify patient stratification biomarkers for ADC programs.
Clinical positioning and patient stratification
Identification of responder populations, candidate eligibility criteria, and biomarker-driven clinical positioning using virtual patient avatars and mechanistic simulation outputs.
DDR program acceleration and synthetic lethality discovery
Simulation-led discovery of synthetic lethal interactions, combination strategies, and biomarkers focused on DNA damage response (DDR) inhibitor programs.
Virtual Lab platform access (beta)
Cloud-based virtual lab providing modules for large-scale in silico experiments (e.g., payload selector, clinical positioning) and access to a library of virtual models.
Expertise Areas
- Computational cell simulation for drug discovery
- Predictive computational biology and translational modeling
- Target selection and biomarker identification
- ADC payload modeling and optimization
Key Technologies
- Interpretable mechanistic cell simulation
- Graph attention networks
- Multi-omics data integration
- Machine learning for signaling network inference