Covid-19 and Artificial Intelligence: Genome sequencing, drug development and vaccine discovery

Bagabir, Sali Abubaker; Ibrahim, Nahla Khamis; Bagabir, Hala Abubaker; Ateeq, Raghda

doi:10.1016/j.jiph.2022.01.011

Cited by 56 publications

(33 citation statements)

References 42 publications

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“…A fundamental support to improve and accelerate the drug repurposing strategy might derive from the use of Artificial Intelligence (AI), a powerful approach that has been widely exploited in the sequencing of SARS-CoV-2 genome from the beginning of the pandemic (reviewed in [ 288 ]). In brief, AI consists of creating synergy between the structure-based, the ligand-based screening methods and the AI algorithms, to generate accurate prediction models.…”

Section: Discussionmentioning

confidence: 99%

Antiparasitic Drugs against SARS-CoV-2: A Comprehensive Literature Survey

et al. 2022

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More than two years have passed since the viral outbreak that led to the novel infectious respiratory disease COVID-19, caused by the SARS-CoV-2 coronavirus. Since then, the urgency for effective treatments resulted in unprecedented efforts to develop new vaccines and to accelerate the drug discovery pipeline, mainly through the repurposing of well-known compounds with broad antiviral effects. In particular, antiparasitic drugs historically used against human infections due to protozoa or helminth parasites have entered the main stage as a miracle cure in the fight against SARS-CoV-2. Despite having demonstrated promising anti-SARS-CoV-2 activities in vitro, conflicting results have made their translation into clinical practice more difficult than expected. Since many studies involving antiparasitic drugs are currently under investigation, the window of opportunity might be not closed yet. Here, we will review the (controversial) journey of these old antiparasitic drugs to combat the human infection caused by the novel coronavirus SARS-CoV-2.

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Section: Discussionmentioning

confidence: 99%

Antiparasitic Drugs against SARS-CoV-2: A Comprehensive Literature Survey

et al. 2022

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“…Considering the time it would take for these inventions to make an impact in a relatively distant field like archiving, it appeared rational to start the study in 2015. A superficial review of journals before this date was adequate to authorize our conclusion; additionally, we desired to concentrate on current controversies and future perceptions and hence considered using modern AI [ 27 , 37 ]. This research work focuses on avoiding the existing drawbacks of CNN-RNN techniques [ 38 , 39 ], which are also listed in Table 2 .…”

Section: System Methodologymentioning

confidence: 99%

Prediction of Omicron Virus Using Combined Extended Convolutional and Recurrent Neural Networks Technique on CT-Scan Images

Gupta

Srinivasulu

Hiran

et al. 2022

Interdisciplinary Perspectives on Infectious Diseases

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COVID-19 has sparked a global pandemic, with a variety of inflamed instances and deaths increasing on an everyday basis. Researchers are actively increasing and improving distinct mathematical and ML algorithms to forecast the infection. The prediction and detection of the Omicron variant of COVID-19 brought new issues for the health fraternity due to its ubiquity in human beings. In this research work, two learning algorithms, namely, deep learning (DL) and machine learning (ML), were developed to forecast the Omicron virus infections. Automatic disease prediction and detection have become crucial issues in medical science due to rapid population growth. In this research study, a combined Extended CNN-RNN research model was developed on a chest CT-scan image dataset to predict the number of +ve and −ve cases of Omicron virus infections. The proposed research model was evaluated and compared against the existing system utilizing a dataset of 16,733-sample training and testing CT-scan images collected from the Kaggle repository. This research article aims to introduce a combined ML and DL technique based on the combination of an Extended Convolutional Neural Network (ECNN) and an Extended Recurrent Neural Network (ERNN) to diagnose and predict Omicron virus-infected cases automatically using chest CT-scan images. To overcome the drawbacks of the existing system, this research proposes a combined research model that is ECNN-ERNN, where ECNN is used for the extraction of deep features and ERNN is used for exploration using extracted features. A dataset of 16,733 Omicron computer tomography images was used as a pilot assessment for this proposed prototype. The investigational experiment results show that the projected prototype provides 97.50% accuracy, 98.10% specificity, 98.80% of AUC, and 97.70% of F1-score. To the last, the study outlines the advantages being offered by the proposed model with respect to other existing models by comparing different parameters of validation such as accuracy, error rate, data size, time complexity, and execution time.

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“…In addition, CVL218 can target to SARS-CoV-2-N, thus interfering with the phase separation process of the N protein-viral RNA-nsp12 complex and making it easier for other antiviral drugs to enter the virus. Therefore, CVL218 can be used to treat COVID-19 as a single drug or in combination with other drugs 13 .…”

Section: Small-molecule Drugsmentioning

confidence: 99%