Repurposing Drugs for Infectious Diseases by Graph Convolutional Network with Sensitivity-Based Graph Reduction.

IF 3.9 2区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Rongting Yue, Abhishek Dutta
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引用次数: 0

Abstract

Computational systems biology employs computational algorithms and integrates diverse data sources, such as gene expression profiles, molecular interactions, and network modeling, to identify promising drug candidates through repurposing existing compounds in response to urgent healthcare needs. This study tackles the urgent need for rapid therapeutic development against emerging infectious diseases. We introduce a novel analytic expression for sensitivity analysis based on the Kronecker product and enhance model prediction performance using Graph Convolutional Networks (GCNs) with sensitivity-based graph reduction. Our algorithm refines prediction performance by leveraging sensitivity-based graph reduction. By integrating RNA-seq data, molecular interactions, and GCNs, we identify disease-related genes and pathways, construct heterogeneous graph models, and predict potential drugs. This approach involves novel analytical expressions that assess sensitivity to model loss, employing the Kronecker product approach. Subgraph analysis identifies nodes for removal, leading to a refined graph used for model retraining. This cost-effective pipeline focuses on computational methods for drug repurposing, targeting infectious diseases such as Zika virus and COVID-19 infection. Applied to these infections, our methodology integrates 659 proteins and 703 drugs for Zika virus, and 495 proteins and 468 drugs for COVID-19, along with their interactions derived from gene expression profiles. Top candidate drugs, such as Betamethasone phosphate and Bizelesin for Zika virus, and Chloroquine, Heparin Disaccharide, and Resveratrol for COVID-19, were validated through literature review or docking analysis. This scalable approach demonstrates promise in repurposing drugs for urgent healthcare challenges.

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来源期刊
Interdisciplinary Sciences: Computational Life Sciences
Interdisciplinary Sciences: Computational Life Sciences MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
8.60
自引率
4.20%
发文量
55
期刊介绍: Interdisciplinary Sciences--Computational Life Sciences aims to cover the most recent and outstanding developments in interdisciplinary areas of sciences, especially focusing on computational life sciences, an area that is enjoying rapid development at the forefront of scientific research and technology. The journal publishes original papers of significant general interest covering recent research and developments. Articles will be published rapidly by taking full advantage of internet technology for online submission and peer-reviewing of manuscripts, and then by publishing OnlineFirstTM through SpringerLink even before the issue is built or sent to the printer. The editorial board consists of many leading scientists with international reputation, among others, Luc Montagnier (UNESCO, France), Dennis Salahub (University of Calgary, Canada), Weitao Yang (Duke University, USA). Prof. Dongqing Wei at the Shanghai Jiatong University is appointed as the editor-in-chief; he made important contributions in bioinformatics and computational physics and is best known for his ground-breaking works on the theory of ferroelectric liquids. With the help from a team of associate editors and the editorial board, an international journal with sound reputation shall be created.
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