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Analysis of Protein-Ligand Interactions of SARS-CoV-2 Against Selective Drug Using Deep Neural Networks

查看全文 作　　者：Natarajan [1]Yuvaraj;Kannan [2]Srihari;Selvaraj [3]Chandragandhi;Rajan Arshath [4]Raja;Gaurav [5]Dhiman;Amandeep [6]Kaur 高影响力作者 机构地区：[1]St.Peter’s Institute of Higher Education and Research,Information Communication Technology Academy,Chennai 600096,India;[2]Department of Computer Science and Engineering,SNS College of Engineering,641107,India;[3]Department of Computer Science and Engineering,Jagannath Educational Health and Charitable Trust College of Engineering and Technology,Coimbatore 641105,India;[4]B.S.Abdur Rahman Crescent Institute of Science and Technology,Chennai 600048,India;[5]Department of Computer Science,Government Bikram College of Commerce,Patiala 147001,India;[6]Department of Computer Science and Engineering,Sri Guru Granth Sahib World University,Fatehgarh 140406,India高影响力机构 出　　处：《Big Data Mining and Analytics》索引2021年第4卷第2期,共8页高影响力期刊 摘　　要：In recent time, data analysis using machine learning accelerates optimized solutions on clinical healthcare systems. The machine learning methods greatly offer an efficient prediction ability in diagnosis system alternative with the clinicians. Most of the systems operate on the extracted features from the patients and most of the predicted cases are accurate. However, in recent time, the prevalence of COVID-19 has emerged the global healthcare industry to find a new drug that suppresses the pandemic outbreak. In this paper, we design a Deep Neural Network(DNN)model that accurately finds the protein-ligand interactions with the drug used. The DNN senses the response of protein-ligand interactions for a specific drug and identifies which drug makes the interaction that combats effectively the virus. With limited genome sequence of Indian patients submitted to the GISAID database, we find that the DNN system is effective in identifying the protein-ligand interactions for a specific drug. 关 键 词：Deep Neural Network(DNN) CORONAVIRUS protein-ligand interactions deep learning clinical healthcare system
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