ML-based cancer cell line drug response models are well-motivated, and significant research effort has gone into developing complex modeling approaches (over 100 papers in 2025). The problem: under rigorous evaluation, we found none that actually works.

Most are published based on inflated metrics, break down when probed in realistic application scenarios, and are outperformed by simple baselines. We built DrEval, a pipeline for unbiased evaluation of these models, to encourage more meaningful progress in the field.