Leveraging Uncertainty Estimates for Drug Response Prediction in Cancer Cell Lines

MotivationMachine learning models that predict drug response from cancer cell line omics profiles could advance precision oncology, yet their utility is limited by heterogeneous prediction quality and silent failures under distribution shifts. Uncertainty quantification can address these challenges, but systematic evaluation of methods for this domain is lacking. ResultsWe benchmark seven uncertainty-aware models for drug response prediction, comparing epistemic uncertainty via ensemble disagreement, aleatoric uncertainty via distributional modeling, and their combination. Gaussian neural network ensembles reliably flag out-of-distribution inputs and achieve a 64% reduction in mean squared error when filtering to the 10% most confident predictions. We discuss how probabilistic predictions can enable drug candidate analyses that account for therapeutically relevant response ranges. Through uncertainty attribution, we identify transcriptomic signatures of unpredictability, i.e., genes associated with prediction uncertainty. We also demonstrate that uncertainty-guided active learning can prioritize informative experiments. Availability and ImplementationThe code and data are available at https://github.com/PascalIversen/LUDRP and https://zenodo.org/records/19219091. Contactkatharina.baum@fu-berlin.de