A variety of large-scale pharmacogenomic data, such as perturbation experiments and sensitivity profiles, enable the systematical identification of drug mechanism of actions (MoAs), which is a crucial task in the era of precision medicine. However, integrating these complementary pharmacogenomic datasets is inherently challenging due to the wild heterogeneity, high-dimensionality and noisy nature of these datasets. In this work, we develop Mania, a novel method for the scalable integration of large-scale pharmacogenomic data. Mania first constructs a drug-drug similarity network through integrating multiple heterogeneous data sources, including drug sensitivity, drug chemical structure, and perturbation assays. It then learns a compact vector representation for each drug to simultaneously encode its structural and pharmacogenomic properties. Extensive experiments demonstrate that Mania achieves substantially improved performance in both MoAs and targets prediction, compared to predictions based on individual data sources as well as a state-of-the-art integrative method. Moreover, Mania identifies drugs that target frequently mutated cancer genes, which provides novel insights into drug repurposing.