Deep Learning-Assisted Repurposing of Plant Compounds for Treating Vascular Calcification: An In Silico Study with Experimental Validation. (5th January 2022)

• Record Type: Journal Article
• Authors: Chao, Chia-Ter; Tsai, You-Tien; Lee, Wen-Ting; Yeh, Hsiang-Yuan; Chiang, Chih-Kang
• Other Names: Jakovljevic Vladimir Academic Editor.
• Is Part Of: Oxidative medicine and cellular longevity. Volume 2022(2022)
• Publication Date: 2022-01-05
• Subjects: Oxidative stress -- Periodicals; Cells -- Aging -- Periodicals; Cells -- Aging; Oxidative stress; Oxidative Stress -- Periodicals; Cell Aging -- Periodicals; Periodicals; 611.0181
• DOI: 10.1155/2022/4378413
• Languages: English
• ISSNs: 1942-0900

Abstract

Abstract : Background. Vascular calcification (VC) constitutes subclinical vascular burden and increases cardiovascular mortality. Effective therapeutics for VC remains to be procured. We aimed to use a deep learning-based strategy to screen and uncover plant compounds that potentially can be repurposed for managing VC. Methods . We integrated drugome, interactome, and diseasome information from Comparative Toxicogenomic Database (CTD), DrugBank, PubChem, Gene Ontology (GO), and BioGrid to analyze drug-disease associations. A deep representation learning was done using a high-level description of the local network architecture and features of the entities, followed by learning the global embeddings of nodes derived from a heterogeneous network using the graph neural network architecture and a random forest classifier established for prediction. Predicted results were tested in an in vitro VC model for validity based on the probability scores. Results . We collected 6, 790 compounds with available Simplified Molecular-Input Line-Entry System (SMILES) data, 11, 958 GO terms, 7, 238 diseases, and 25, 482 proteins, followed by local embedding vectors using an end-to-end transformer network and a node2vec algorithm and global embedding vectors learned from heterogeneous network via the graph neural network. Our algorithm conferred a good distinction between potential compounds, presenting as higher prediction scores for the compound categories with a higher potential but lower scores for other categories. Probability score-dependent selection revealed that antioxidants such as sulforaphane and daidzein were potentially effective compounds against VC, while catechin had low probability. All three compounds were validated in vitro . Conclusions. Our findings exemplify the utility of deep learning in identifying promising VC-treating plant compounds. Our model can be a quick and comprehensive computational screening tool to assist in the early drug discovery process.