Predicting Unseen Scientific Interactions: Conditional Flow Models Hallucinate Non-Additive Effects
Why AI models fool us by predicting accurate results while missing how drugs actually interact
Machine learning models trained to predict scientific outcomes often appear accurate overall but fail to capture how one intervention changes another's effect—a crucial scientific question. Researchers found that these models tend to invent fake interactions and struggle to predict real ones when moving beyond their training data, even when their overall predictions look correct.
This matters for drug development and biology research: a model that says two drugs work well together might be hallucinating that interaction rather than actually understanding it. If scientists rely on these predictions to test new drug combinations or cell treatments, they could waste resources pursuing effects that don't actually exist, or miss real interactions that traditional accuracy metrics hide.