A novel multi-modal drug repurposing approach for identification of potent ACK1 inhibitors.

Exploiting drug polypharmacology to identify novel modes of actions for drug repurposing has gained significant attentions in the current era of weak drug pipelines. From a serendipitous to systematic or rational ways, a variety of unimodal computational approaches have been developed but the complexity of the problem clearly needs multi-modal approaches for better solutions. In this study, we propose an integrative computational framework based on classical structure-based drug design and chemical-genomic similarity methods, combined with molecular graph theories for this task. Briefly, a pharmacophore modeling method was employed to guide the selection of docked poses resulting from our high-throughput virtual screening. We then evaluated if complementary results (hits missed by docking) can be obtained by using a novel chemo-genomic similarity approach based on chemical/sequence information. Finally, we developed a bipartite-graph based on the extensive data curation of DrugBank, PDB, and UniProt. This drug-target bipartite graph was used to assess similarity of different inhibitors based on their connections to other compounds and targets. The approaches were applied to the repurposing of existing drugs against ACK1, a novel cancer target significantly overexpressed in breast and prostate cancers during their progression. Upon screening of ∼1,447 marketed drugs, a final set of 10 hits were selected for experimental testing. Among them, four drugs were identified as potent ACK1 inhibitors. Especially the inhibition of ACK1 by Dasatinib was as strong as IC(50)=1nM. We anticipate that our novel, integrative strategy can be easily extended to other biological targets with a more comprehensive coverage of known bio-chemical space for repurposing studies.