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The drug discovery and development is a complex and expensive process, and the probability of success is low. Nowadays, the philosophy of drug discovery has been transformed from  one-drug one-target  to  multiple-drug multiple-targets , called as Polypharmacology, in order to discover new drugs or novel targets for existing drugs, known as Drug repurposing. In particular, the improvements in drug discovery for complex diseases such as cancer, could be achieved by studying drug action through network biology. These networks has contributed to the genesis of Network pharmacology. Integrating and analyzing heterogeneous genome-scale data is a huge algorithmic challenge for modern systems biology. In this paper Power Graph Analysis (PGA) has been applied to explore the tripartite Drug-Target-Disease networks, which is a lossless transformation of biological networks into a compact, less redundant representation. Specifically, the effectiveness of Power Graph is analysed with state-of-the-art SNS (Shared Neighbourhood Scoring) algorithm, in two case studies. We analysed two separate integrated tripartite biomedical networks from (i) PharmDB, a tripartite pharmacological network database; and (ii) COVIDrugNet, the SARS-CoV-2 Virus&#x2013;Host&#x2013;Drug Interactome. Despite very high edge reduction, PGA helps to easily explore much more enriched information without any loss and discover novel potential drugs currently in clinical trial to treat lung cancer - Squamous Cell Carcinoma (SCC) and SARS-CoV-2 diseases. Also it outperformed SNS algorithm in terms of accuracy and efficiency, as the SNS algorithm requires computationally expensive calculations for large networks. Furthermore, it exhibits superior scalability, making it suitable for analyzing large-scale datasets.

Computational Drug Repurposing using Power Graph Analysis of Integrated drug-target-disease network