Using informative features in machine learning based method for COVID-19 drug repurposing

Aghdam, RosaInst Res Fundamental Sci IPM, Sch Biol Sci, Tehran, Iran. (författare)

QC 20211006
Coronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases worldwide, creating a serious danger to public health. However, there are no specific therapies or drugs available for COVID-19 treatment. While new drug discovery is a long process, repurposing available drugs for COVID-19 can help recognize treatments with known clinical profiles. Computational drug repurposing methods can reduce the cost, time, and risk of drug toxicity. In this work, we build a graph as a COVID-19 related biological network. This network is related to virus targets or their associated biological processes. We select essential proteins in the constructed biological network that lead to a major disruption in the network. Our method from these essential proteins chooses 93 proteins related to COVID-19 pathology. Then, we propose multiple informative features based on drug-target and protein-protein interaction information. Through these informative features, we find five appropriate clusters of drugs that contain some candidates as potential COVID-19 treatments. To evaluate our results, we provide statistical and clinical evidence for our candidate drugs. From our proposed candidate drugs, 80% of them were studied in other studies and clinical trials.

Habibi, MahnazIslamic Azad Univ, Dept Math, Qazvin Branch, Qazvin, Iran. (författare)
Taheri, GolnazKTH,Beräkningsvetenskap och beräkningsteknik (CST),Science for Life Laboratory, SciLifeLab(Swepub:kth)u1cq22h1 (författare)
Inst Res Fundamental Sci IPM, Sch Biol Sci, Tehran, Iran.Islamic Azad Univ, Dept Math, Qazvin Branch, Qazvin, Iran. (creator_code:org_t)

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