Analysis of protein-ligand interactions of SARS-CoV-2 against selective drug using deep neural networks

Abstract

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.

Adaptive Diagnosis of Lung Cancer by Deep Learning Classification Using Wilcoxon Gain and Generator

“…e majority of cancer disease diagnostics systems use filtering models as the first step in identifying the relevant subset of characteristics. Such filtering methods aid in the removal of the irrelevant and redundant features that contribute to the high-dimensionality problem, which is one of the most significant challenges in illness detection [30]. As a result of the removal of extraneous features, the efficiency of lung cancer disease diagnosis is improved.…”

A novel deep neuroevolution-based image classification method to diagnose coronavirus disease (COVID-19)

“…In various applications, such as medical informatics [ 15 , 16 , 16 ], energy forecasting [ [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] ], uncertainty quantification [ [26] , [27] , [28] ], probabilistic forecasting [ [29] , [30] , [31] ], and recommendation models [ [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] ], AI has been employed with powerful algorithms. Deep-learning approaches have been highlighted to be used with the diagnosis of COVID-19 in chest X-rays after appearing the epidemic [ [46] , [47] , [48] , [49] , [50] ]. Several scientific studies have been conducted to analyze chest X-rays taken from COVID-19 patients.…”

Decorative Art Pattern Mining and Discovery Based on Group User Intelligence

“…In addition, more performance metrics (e.g., network load (Xiaolong Xiaolong Xu, Bowen Shen, 2020;Yun Li, 2021;Bi, 2021;Yun Li et al, 2020), diversity (Jianxin Li, 2020;) should be taken into considerations besides accuracy and time cost. Third, how to make full use of the cutting-edge learning techniques (e.g., deep learning, machine learning, transfer learning) Xue, 2019;Yuvaraj, 2021) to further improve the algorithm performances is still requiring challenging efforts. Finally, our proposed DPMD method still needs to be improved by measuring and quantifying its capability of privacy-preservation; therefore, we will further investigate current privacy protection solutions such as Kaiyang Li, 2020;Lina Wang, 2020;Zhipeng Cai, 2020;Zheng, 2017) to pursue a feasible and scientific privacy measurement solution.…”