A geometric deep learning framework for drug repositioning over heterogeneous information networks. Issue 6 (19th September 2022)

• Record Type: Journal Article
• Authors: Zhao, Bo-Wei; Su, Xiao-Rui; Hu, Peng-Wei; Ma, Yu-Peng; Zhou, Xi; Hu, Lun
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 6(2022)
• Publication Date: 2022-09-19
• Subjects: drug repositioning -- geometric deep learning -- heterogeneous information network -- drug-disease association prediction -- artificial intelligence; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac384
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Drug repositioning (DR) is a promising strategy to discover new indicators of approved drugs with artificial intelligence techniques, thus improving traditional drug discovery and development. However, most of DR computational methods fall short of taking into account the non-Euclidean nature of biomedical network data. To overcome this problem, a deep learning framework, namely DDAGDL, is proposed to predict drug-drug associations (DDAs) by using geometric deep learning (GDL) over heterogeneous information network (HIN). Incorporating complex biological information into the topological structure of HIN, DDAGDL effectively learns the smoothed representations of drugs and diseases with an attention mechanism. Experiment results demonstrate the superior performance of DDAGDL on three real-world datasets under 10-fold cross-validation when compared with state-of-the-art DR methods in terms of several evaluation metrics. Our case studies and molecular docking experiments indicate that DDAGDL is a promising DR tool that gains new insights into exploiting the geometric prior knowledge for improved efficacy.