DeepPurpose-based drug discovery in chondrosarcoma

Background:Chondrosarcoma(CS)is the second most common primary bone tumor,accounting for approximately30%of all malignant bone tumors.Unfortunately,the efficacy of currently available drug therapies is limited.Therefore,this study aimed to explore drug therapies for CS using novel computational methods.Methods:In this study,text mining,Gene Codis STRING,and Cytoscape were used to identify genes closely related to CS,and the Drug Gene Interaction Database(DGIdb)was used to select drugs targeting the genes.Drug-target interaction prediction was performed using Deep Purpose,to finally obtain candidate drugs with the highest predicted binding affinities.Results:Text-mining searches identified 168 genes related to CS.Gene enrichment and protein-protein interaction analysis generated 14 genes representing 10 pathways using Gene Codis,STRING,and Cytoscape.Seventy drugs targeting genes closely related to CS were analyzed using DGIdb.Deep Purpose recommended 25 drugs,including integrin beta 3 inhibitors,hypoxia-inducible factor 1 alpha inhibitors,E1A binding protein P300 inhibitors,vascular endothelial growth factor A inhibitors,AKT1 inhibitors,tumor necrosis factor inhibitors,transforming growth factor beta 1 inhibitors,interleukin 6 inhibitors,mitogen-activated protein kinase 1 inhibitors,and protein tyrosine kinase inhibitors.Conclusion:Drug discovery using in silico text mining and Deep Purpose may be an effective method to explore drugs targeting genes related to CS.