05/14/2026
🧬 Bridging the gap in AI drug discovery.
Ali Denton, Staff Machine Learning Scientist at Recursion and one of the authors on the recent paper in Nature Biotechnology, explains how the AI model TxPert predicts how a cell will respond to perturbations.
Predicting a cell’s RNA activity, or transcriptome, is key to bridging the gap between cellular changes and clinical outcomes and advancing the potential for AI drug discovery. As Ali says, “with hundreds of cell types and so much disease variation, the total possibilities are too vast to measure in a lab.”
She describes how TxPert allows us to perform a “Virtual Assay,” taking the mathematical signature of a healthy cell called the Basal State and adding the perturbation’s embedding to deliver a highly accurate prediction of what the cell’s transcriptome will look like after treatment.
TxPert uses layered graph-based models that integrate phenomics — or how a cell looks — and transcriptomics — which genes are expressed — along with massive public biological knowledge resources.
The model can even predict how a perturbation will work in entirely new cell lines it hasn’t seen before as well as accurately forecast the effects of “double perturbations,” consistently identifying “unknown unknowns” that traditional models — and even massive general-purpose AI — often miss.
Ali notes that TxPert is currently predicting genetic perturbations, but more flexible models — including those predicting drug effects — are in the works.
👉 Check out the full paper in Nature Biotech: https://www.nature.comarticles/s41587-026-03113-4
