Abstract
Drug repurposing involves the identification of new applications for existing drugs at a lower cost and in a shorter time. There are different computational drug-repurposing strategies and some of these approaches have been applied to the coronavirus disease 2019 (COVID-19) pandemic. Computational drug-repositioning approaches applied to COVID-19 can be broadly categorized into (i) network-based models, (ii) structure-based approaches and (iii) artificial intelligence (AI) approaches. Network-based approaches are divided into two categories: network-based clustering approaches and network-based propagation approaches. Both of them allowed to annotate some important patterns, to identify proteins that are functionally associated with COVID-19 and to discover novel drug-disease or drug-target relationships useful for new therapies. Structure-based approaches allowed to identify small chemical compounds able to bind macromolecular targets to evaluate how a chemical compound can interact with the biological counterpart, trying to find new applications for existing drugs. AI-based networks appear, at the moment, less relevant since they need more data for their application.
Keywords: AI; COVID-19; drug repurposing; molecular docking; network-based approaches; new therapies.
【저자키워드】 COVID-19, Drug repurposing, molecular docking, AI, network-based approaches, new therapies., 【초록키워드】 coronavirus disease, Drug repurposing, Coronavirus disease 2019, pandemic, artificial intelligence, drugs, molecular docking, drug, Protein, Clustering, target, molecular, Therapies, drug-target, chemical compounds, chemical compound, approaches, moment, Compound, Computational drug, approach, identify, evaluate, applied, less, macromolecular, with COVID-19,