scDEFT: A deep learning framework for drug-effect prediction and counterfactual reasoning
Predicting which patients will respond to drugs before treatment even starts
A new machine learning system called scDEFT can predict which inflammatory bowel disease patients will respond to a drug by analyzing their individual cells before they start treatment — achieving 70% accuracy where standard methods guess at random. The system also identifies which specific genes and cellular changes drive the difference between responders and non-responders, offering a mechanistic explanation for why identical drugs work for some patients but not others.
Doctors could use this approach to match patients with drugs that will actually work for them before wasting months on ineffective treatment. For inflammatory bowel disease and potentially other conditions, this could spare patients unnecessary side effects and speed them toward therapies that help. The method also pinpoints which genes to target with new drugs, directly informing drug development.