The Columbia Cancer Target Discovery and Development (CTD2) Center is developing PANACEA, a resource comprising dose-responses and RNA sequencing (RNA-seq) profiles of 25 cell lines perturbed with ∼400 clinical oncology drugs, to study a tumor-specific drug mechanism of action. Here, this resource serves as the basis for a DREAM Challenge assessing the accuracy and sensitivity of computational algorithms for de novo drug polypharmacology predictions. Dose-response and perturbational profiles for 32 kinase inhibitors are provided to 21 teams who are blind to the identity of the compounds. The teams are asked to predict high-affinity binding targets of each compound among ∼1,300 targets cataloged in DrugBank. The best performing methods leverage gene expression profile similarity analysis as well as deep-learning methodologies trained on individual datasets. This study lays the foundation for future integrative analyses of pharmacogenomic data, reconciliation of polypharmacology effects in different tumor contexts, and insights into network-based assessments of drug mechanisms of action. Display omitted •Drug-perturbed RNA sequencing data can be used to identify drug targets•Technology-based drug-target definitions often subsume literature definitions•Literature and screening datasets provide complementary information on drug mechanisms Douglass et al. report the results of a crowdsourced challenge to develop machine-learning algorithms that use drug-perturbed transcriptome data to rapidly predict drug targets on a proteomic scale. Winning methods effectively predicted off-target binding of clinical kinase inhibitors and clarified disparate literature on these drugs’ mechanisms of action.