Turbine’s Wet Lab

Explore beyond the limits of your data, contextualize it in our virtual cell model and generate additional data sets to improve predictions. Validate predictions in vitro, establishing a continuous learning loop to power every decision.

Speak with an expert

Turbine puts your existing data into biological context, identifies and generates the data needed to drive model performance, and validates predictions in the lab (yours, a CRO’s, or ours).

The result is a decision-ready data package to build and sharpen future Virtual Assays.

Gregory Vladimer, ex VP of Translational Research at Exscientia & ex CSO at Allcyte, on why Turbine’s lab in the loop is the differentiator for cell modeling.

Turbine’s Lab in
the Loop Capabilities

Contextualize

See Beyond the Limits of your Data

We harmonize and standardize your target-biology datasets, before integrating them with public, proprietary, and licensed data. Turbine uses this combined dataset to train Virtual Assays for your specific question. Benchmarking against unseen results establishes predictive performance and limitations before new predictions are made.

1400 cell lines
50+ immune cell types
1000+ PDX models & perturbations
200+ drugs and & combinations, incl. SoC, payloads, PROTACs & full ADCs
100,000 patient profiles & clinical response data

Generate

FILL THE GAPS

Generate fit-for-purpose datasets to deploy ready-to-use Virtual Assays in weeks, not months. Simulations identify which data points carry the most signal, and which experiments should be validated on the bench.

50,000 data points / month for mono & drug combinations
10,000 data points / month for drug + siRNA combinations
Viability, growth inhibition, brightfield microscopy images
Transcriptomics & proteomics assays
Biobank of cancer cell lines & immortalized healthy cell lines

Validate

Measure Results & Close the Loop

Validate predictions in Turbine’s wet lab, integrating in silico results directly into lab workflows. Scientists receive experimental results to inform pipeline decisions and fine tune Virtual Assays.

100s of validated hypotheses for more than 30 customers

A Data Strategy For
Virtualizing Biology

Large perturbational screens take months, cost six figures, and carry many redundant signals. Read how ablation studies and model-guided experiment selection produce fit-for-purpose datasets in days instead.

Download the white paper