DeepPurpose-based drug discovery in chondrosarcoma

Abstract

Background: Chondrosarcoma (CS) is the second most common primary bone tumor, accounting for approximately 30% 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, GeneCodis 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 DeepPurpose, 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 GeneCodis, STRING, and Cytoscape. Seventy drugs targeting genes closely related to CS were analyzed using DGIdb. DeepPurpose 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 DeepPurpose may be an effective method to explore drugs targeting genes related to CS.

Keywords

• Chondrosarcoma
• Text mining
• DeepPurpose
• Drug therapy
• Drug-target interaction