ADC discovery & positioning
In which indications is my payload / ADC most effective?
How selective is my ADC?
What are the predictive biomarkers of sensitivity / resistance?
In which indications is my payload / ADC most effective?
How selective is my ADC?
What are the predictive biomarkers of sensitivity / resistance?
Which payload pairs are the most synergistic?
Which ADC – small molecule pairs are the most synergistic?
Which genes should I target alongside my drug to improve efficacy?
How translatable are my in vitro ADC efficacy results into PDX models?
What clinically-relevant biomarkers can I identify early to inform patient inclusion/exclusion criteria?
Which genes when inhibited cause disease cell death?
What are the predictive biomarkers of sensitivity / resistance?
In which indications is my target the most effective?
What is the functional impact of inhibiting my gene of interest?
Which new or known drug targets can my target be combined with?
Is this a good target in the background of a treatment e.g, standard-of-care?
System-level model of how disease biology behaves under drug stimulus.
In silico experiments that simulate cellular response to drug and genetic perturbations at computational scale.
Where teams design, run, and interpret virtual experiments that generate decision-ready outputs, making simulation part of the discovery workflow.
Virtual experiments guide what goes to the wet lab and their results identify data needs and are used to refine future assays so that every experiment that reaches the bench is more likely to succeed. Predictions can be validated in your own laboratory, a CRO partner or Turbine’s own facility.
Turbine’s proprietary, licensed & public data
Simulation-guided data generation