Biomolecular Network-based Study of a Parasitic Disease and Therapeutic Drugs

Main Article Content

Altankhuyag Avirmed
Uranchimeg Erdenedalai
Selenge Erdenechimeg
Yansen Su
Tseren-Onolt Ishdorj

Keywords

drug repurposing, interaction network, machine-learning, parasitic disease.

Abstract

Computational drug repurposing methods, particularly biomolecular network-based disease-drug-target interaction models, are essential tools for integrating large-scale heterogenous molecular information and revealing functional mechanisms, as well as for main regulatory modules of interactants which can be useful in developing new drugs. In the present study, a drug-centric network for a parasitic disease (Echinococcosis) and therapeutic drugs have been considered. A complex network with more than 12,000 vertices and more than 33,000 edges representing interactions of 84 echinococcosis-related genes with associated proteins was built and analyzed. The networks of disease similarity and drug similarity were constructed based on the complex network. As a result, three drugs (D08356, D00701, and D00506) associated with three candidate diseases through three pathways and a protein complex have been extracted. This effort tries to predict the anti-echinococcosis effects of the drugs’ combinations with benzimidazole.

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