LAUSR

Most approved drugs and small-molecule compounds modulate their therapeutic and toxic effects through multiple protein targets and biological pathways. Proteome-wide compound-target interaction networks (among both onand off-targets) are therefore essential resources to better understand and make use of such polypharmacological effects of promiscuous compounds (Knight et al. 2010). Interestingly, systematic bioactivity profiling has revealed that some compounds may have higher potency against their unintended off-targets, compared to the primary on-targets (Tang et al. 2018). Unexplored off-target potencies of approved drugs may therefore lead to novel drug repurposing opportunities, i.e., finding new uses or targets for existing drugs (Pemovska et al. 2015; Kuenzi et al. 2019). A prime example of the utilization of compound-target interaction networks comes from finding synergistic compound combinations that lead to increased efficacy. For instance, the recent AstraZeneca-Sanger Drug Combination DREAM Challenge benchmarked a number of machine learning algorithms in terms of their accuracy to predict synergistic drug pairs and associated biomarkers for 910 drug combinations across 85 cancer cell lines (http://dreamchallenges.org/initialr e su l t s f r omthe a s t r a zeneca s ange r -d rug combination-prediction-challenge-presented/). Among 160 participants, all the winning methods incorporated prior knowledge of putative drugtarget interactions (Menden et al. 2019). However, proteome-wide target profiles are not important merely when exploring therapeutic potential, e.g., synergistic drug pairs, but also when profiling excess toxicity prior to clinical studies.