Deep Learning-Based Drug Screening for COVID-19 and Case Studies

Saravanan, Konda Mani; Zhang, Haiping; Hossain, Md. Tofazzal; Reza, Md. Selim; Wei, Yanjie

doi:10.1007/7653_2020_58

Cited by 11 publications

(5 citation statements)

References 123 publications

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“…Similarly, multiple deep learning techniques have also been used to identify the repurposable drug candidates for Covid-19. One of the deep learning-based studies, is done in [205] where deep learning-based drug screening was performed followed by a study done in [206] where a resource based on deep learning is created to enable the fast repurposing of the drug candidates for Covid-19. Finally, in the light of the examples tabulated in Table 8 , it can be concluded that the drug repurposing methods are quite helpful and quick to response in case Covid-19 and related emergent situations.…”

Section: Benefits Of Drug Repurposing Methods In Clinical Practicementioning

confidence: 99%

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19

Ahmed

Soomro

Salih

et al. 2022

Biomedicine & Pharmacotherapy

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Section: Benefits Of Drug Repurposing Methods In Clinical Practicementioning

confidence: 99%

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19

Ahmed

Soomro

Salih

et al. 2022

Biomedicine & Pharmacotherapy

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“…Ton et al [124] utilized a deep learning platform called Deep Docking to screen over 1.3 billion compounds for inhibitors targeting the SARS-CoV-2 main protease, providing candidate drugs for drug development. Saravanan et al [125] employed deep learning to predictively screen known compounds, enabling rapid and accurate identification of potential antiviral drugs.…”

Section: Drug Discoverymentioning

confidence: 99%

Advances in the Development of Representation Learning and Its Innovations against COVID-19

Li,

Parvej,

Zhang

et al. 2023

COVID

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In bioinformatics research, traditional machine-learning methods have demonstrated efficacy in addressing Euclidean data. However, real-world data often encompass non-Euclidean forms, such as graph data, which contain intricate structural patterns or high-order relationships that elude conventional machine-learning approaches. Representation learning seeks to derive valuable data representations from enhancing predictive or analytic tasks, capturing vital patterns and structures. This method has proven particularly beneficial in bioinformatics and biomedicine, as it effectively handles high-dimensional and sparse data, detects complex biological patterns, and optimizes predictive performance. In recent years, graph representation learning has become a popular research topic. It involves the embedding of graphs into a low-dimensional space while preserving the structural and attribute information of the graph, enabling better feature extraction for downstream tasks. This study extensively reviews representation learning advancements, particularly in the research of representation methods since the emergence of COVID-19. We begin with an analysis and classification of neural-network-based language model representation learning techniques as well as graph representation learning methods. Subsequently, we explore their methodological innovations in the context of COVID-19, with a focus on the domains of drugs, public health, and healthcare. Furthermore, we discuss the challenges and opportunities associated with graph representation learning. This comprehensive review presents invaluable insights for researchers as it documents the development of COVID-19 and offers experiential lessons to preempt future infectious diseases. Moreover, this study provides guidance regarding future bioinformatics and biomedicine research methodologies.

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“…Table 4 summarizes all AI technologies as well as the COVID-19 areas in which they are used. Deep Neural Network and Deep learning algorithm [93]; [94]; [95]; [96] As shown in table 4, the DL is the most applied technique across all the areas and concerns of COVID-19. DL technology has been used for interventions in the areas of COVID-19 prediction, screening, contact tracing, prognosis, and for both drug and vaccine discovery.…”

Section: 7mentioning

confidence: 99%

A Study of Impacts of Artificial Intelligence on COVID-19 Prediction, Diagnosis, Treatment, and Prognosis

Isiaka¹,

Babatunde²,

Ajao³

et al. 2022

AIMS Research Publication

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Following the identification of Coronavirus Disease 2019 (COVID-19) in Wuhan, China in December 2019, AI researchers have teamed up with a health specialist to combat the virus. This study explores the medical and non-medical areas of COVID-19 that AI has impacted: the prevalence of the AI technologies adopted across all stages of the pandemic, the collaboration networks of global AI researchers, and the open issues. 21,219 papers from ACM Digital, Science Direct and Google Scholar were examined. Adherence to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and utilizing the PICO (population, intervention, comparison, and outcome) paradigm, guided the inclusion of researches in the review. Tables and graphs were utilized to display the results. Analysis revealed that AI has impacted 4 molecular, 4 clinical, and 7 societal areas of COVID-19. Deep Learning among other AI technologies was traced to all aspects of the pandemic. 2173 authors and co-authors were traced to these achievements, while 32 of the most connected 51 authors were affiliated with institutions in China, 18 to the United States, and 1 to Europe. The open issues identified had to do with the quality of datasets, AI model deployment, and privacy issues. This study demonstrates how AI may be utilized for COVID-19 diagnosis, prediction, medication and vaccine identification, prognosis, and contact person monitoring. This investigation began at the beginning of the epidemic and continued until the first batch of vaccinations received approval. The study provided collaboration opportunities for AI researchers and revealed open issues that will spike further research toward preparing the world for any future pandemic Keywords: Artificial Intelligence, COVID-19, Diagnosis, Prognosis, Contact Tracing

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Deep Learning-Based Drug Screening for COVID-19 and Case Studies

Cited by 11 publications

References 123 publications

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19

A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19

Advances in the Development of Representation Learning and Its Innovations against COVID-19

A Study of Impacts of Artificial Intelligence on COVID-19 Prediction, Diagnosis, Treatment, and Prognosis

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