PT - JOURNAL ARTICLE AU - Nehme El-Hachem AU - Deena M.A. Gendoo AU - Laleh Soltan Ghoraie AU - Zhaleh Safikhani AU - Petr Smirnov AU - Ruth Isserlin AU - Gary D. Bader AU - Anna Goldenberg AU - Benjamin Haibe-Kains TI - Integrative pharmacogenomics to infer large-scale drug taxonomy AID - 10.1101/046219 DP - 2016 Jan 01 TA - bioRxiv PG - 046219 4099 - http://biorxiv.org/content/early/2016/04/06/046219.short 4100 - http://biorxiv.org/content/early/2016/04/06/046219.full AB - Identification of drug targets and mechanism of action (MoA), particularly for new and uncharacterized drugs, is important for the optimization of drug efficacy. Current approaches towards determining drug MoA largely rely on prior information such as side effects, therapeutic indication and chemo-informatics. However, such information is not transferable or applicable for newly identified small molecules. Despite continuous release of large-scale pharmacogenomic datasets, these valuable data remain underused to classify drugs. Accordingly, a systematic and unbiased approach towards MoA prediction is imperative to efficiently classify new compounds and infer their potential targets of MoA. Here, we propose a method that only relies on basic drug characteristics, including drug structural information, drug perturbation and drug sensitivity profiles, which have not been previously combined towards predicting drug targets and MoA. We harnessed the full potential of pharmacogenomics data using our Similarity Network Fusion approach to implement Drug Network Fusion (DNF), a scalable, integrative drug taxonomy. We demonstrate that DNF is effective towards prediction of drug targets and anatomical therapeutic chemical (ATC) classification). Our method enables robust inference of drug MoAs for new and existing compounds, using integrative computational pharmacogenomics