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

Abstract

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.

Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors

“…On the other hand, recent years have witnessed a runaway increase in the involvement of promising machine learning (ML) approaches in the field of medicine, from basic medical sciences research, to clinical decision-making [ 37 , 38 ]. Several studies have employed a variety of potential ML algorithms for the understanding of the nature of SARS-CoV-2 and its transmission dynamics [ 39 , 40 ], forecasting pandemic scenarios [ 41 , 42 ], predicting COVID-19 diagnosis and prognosis [ 43 ], drug repurposing [ 44 ], and vaccine development against COVID-19 [ 45 ], as well as for predicting COVID-19 vaccination willingness [ 46 ] and post-vaccination side effects [ 13 ]. Interestingly, for the post-vaccination stage, a few studies have utilized ML applications to build predictive models for the reactogenicity and morbidity incidences, and for the severity of side effects following COVID-19 vaccination [ 13 , 47 ].…”

COVID-19 Drug Repurposing: A Network-Based Framework for Exploring Biomedical Literature and Clinical Trials for Possible Treatments

“…Artificial neural network (ANN) models are particularly useful to train with virus protein sequences as inputs and antiviral agents are deemed safe in humans as outputs [ 33 ]. ML methods also reveal the relationship between viral, drug and the host proteins [ 34 ]. Traditional ML approaches also played an important role in the COVID-19 drug repurposing.…”

Using unsupervised learning algorithms to identify essential genes associated with SARS-CoV-2 as potential therapeutic targets for COVID-19

“…Therefore, researchers are sharing their findings on SARS-CoV-2's genome and evolution around the world. Some of these researchers are focusing on finding a therapy with the help of existing drugs using picture(0,0) (-35,0)(1,0)30 (0,35)(0,-1)30 picture picture(0,0)(35,0)(-1,0)30 (0,35)(0,-1)30 picture "main" -2022/1/19 -page 2 -#2 picture(0,0) (-35,0)(1,0)30 (0,-35)(0,1)30 picture picture(0,0)(35,0)(-1,0)30 (0,-35)(0,1)30 picture the drug repurposing method as a faster and less expensive approach Aghdam et al (2021). Gene analysis is another useful method for drug repurposing and understanding different patients' responses to the virus.…”