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

Big Data Mining and Analytics 2021年第2期4卷 76-83页

作者： Natarajan Yuvaraj Kannan Srihari selvaraj chandragandhi Rajan Arshath Raja Gaurav Dhiman Amandeep Kaur St.Peter’s Institute of Higher Education and Research Information Communication Technology AcademyChennai 600096India Department of Computer Science and Engineering SNS College of Engineering641107India Department of Computer Science and Engineering Jagannath Educational Health and Charitable Trust College of Engineering and TechnologyCoimbatore 641105India B.S.Abdur Rahman Crescent Institute of Science and Technology Chennai 600048India Department of Computer Science Government Bikram College of CommercePatiala 147001India Department of Computer Science and Engineering Sri Guru Granth Sahib World UniversityFatehgarh 140406India

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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