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© 2021 American Chemical Society.Identification of drug–pathway associations plays an important role in pathway–based drug repurposing. However, it is time–consuming and costly to uncover new drug–pathway associations experimentally. The drug–induced transcriptomics data provide a global view of cellular pathways and tell how these pathways change under different treatments. These data enable computational approaches for large–scale prediction of drug–pathway associations. Here we introduced DPNetinfer, a novel computational method to predict potential drug–pathway associations based on substructure–drug–pathway networks via network–based approaches. The results demonstrated that DPNetinfer performed well in a pan–cancer network with an AUC (area under curve) = 0.9358. Meanwhile, DPNetinfer was shown to have a good capability of generalization on two external validation sets (AUC = 0.8519 and 0.7494, respectively). As a case study, DPNetinfer was used in pathway–based drug repurposing for cancer therapy. Unexpected anticancer activities of some nononcology drugs were then identified on the PI3K–Akt pathway. Considering tumor heterogeneity, seven primary site–based models were constructed by DPNetinfer in different drug–pathway networks. In a word, DPNetinfer provides a powerful tool for large–scale prediction of drug–pathway associations in pathway–based drug repurposing. A web tool for DPNetinfer is freely available at http://lmmd.ecust.edu.cn/netinfer/.
