In silico discovery of multi-targeting inhibitors for the COVID-19 treatment by molecular docking, molecular dynamics simulation studies, and ADMET predictions

Coronavirus disease-2019 (COVID-19), a global pandemic, has currently infected more than 247 million people around the world. Nowadays, several receptors of COVID-19 have been reported, and few of them are explored for drug discovery. New mutant strains of COVID-19 are emerging since the first outbreak of disease and causing significant morbidity and mortality across the world. Although, few drugs were approved for emergency uses, however, promising drug with well-proven clinical efficacy is yet to be discovered. Hence, researchers are continuously attempting to search for potential drug candidates targeting the well-established enzymatic targets of the virus. The present study aims to discover the antiviral compounds as potential inhibitors against the five targets in various stages of the SARS-CoV-2 life cycle, i.e., virus attachments (ACE2 and TMPRSS2), viral replication, and transcription (M-pro, PLpro and RdRp), using the most reliable molecular docking and molecular dynamics method. The ADMET study was then carried out to determine the pharmacokinetics and toxicity of several compounds with the best docking results. To provide a more effective mechanism for demonstrating protein-ligand interactions, molecular docking data were subjected to a molecular dynamic (MD) simulation at 300 K for 100 ns. In terms of structural stability, structure compactness, solvent accessible surface area, residue flexibility, and hydrogen bond interactions, the dynamic features of complexes have been compared.

Applicability of double Channel Technique in AlGaN/GaN HEMT for future biosensing applications

Sensing COVID-19, GOx (glucose oxidase enzyme) in exhaled breath condensate/saliva, bio-molecules like KIM (Kidney Injury Molecule) in human body and pH value in human body fluids have gained huge attention in the present scenario as well as in the past decade. Hence, for the first time, double channel technique in AlGaN/GaN High Electron Mobility Transistor (HEMT) is proposed and its applicability is demonstrated by biosensing application. Simulation using SILVACO Technology Computer Aided Design (TCAD) based on numerical solid state models has been extensively used for investigation and analysis. The sensitivity of double channel device is compared with single channel device and its performance is evaluated in terms of the transconductance. Unlike the single channel device, double channel device exhibited wide range of transconductance with respect to gate bias. The device recorded a sensitivity of 136%, which is 74% higher than the sensitivity of single channel device. Hence, it is inferred that the sensitivity enhances with the use of multiple channels and could be increased by increasing the number of channels. The results of this research show that the proposed sensor stands a promising candidate for future biosensing applications that demand high detection limits. © 2021

Jaya Spider Monkey Optimization-driven Deep Convolutional LSTM for the prediction of COVID'19

COVID'19 is an emerging disease and the precise epidemiological profile does not exist in the world. Hence, the COVID'19 outbreak is treated as a Public Health Emergency of the International Concern by the World Health Organization (WHO). Hence, an effective and optimal prediction of COVID'19 mechanism, named Jaya Spider Monkey Optimization-based Deep Convolutional long short-term classifier (JayaSMO-based Deep ConvLSTM) is proposed in this research to predict the rate of confirmed, death, and recovered cases from the time series data. The proposed COVID'19 prediction method uses the COVID'19 data, which is the trending domain of research at the current era of fighting the COVID'19 attacks thereby, to reduce the death toll. However, the proposed JayaSMO algorithm is designed by integrating the Spider Monkey Optimization (SMO) with the Jaya algorithm, respectively. The Deep ConvLSTM classifier facilitates to predict the COVID'19 from the time series data based on the fitness function. Besides, the technical indicators, such as Relative Strength Index (RSI), Rate of Change (ROCR), Exponential Moving Average (EMA), Williams %R, Double Exponential Moving Average (DEMA), and Stochastic %K, are extracted effectively for further processing. Thus, the resulted output of the proposed JayaSMO-based Deep ConvLSTM is employed for COVID'19 prediction. Moreover, the developed model obtained the better performance using the metrics, like Mean Square Error (MSE), and Root Mean Square Error (RMSE) by considering confirmed, death, and the recovered cases of COVID'19 for China and Oman. Thus, the proposed JayaSMO-based Deep ConvLSTM showed improved results with a minimal MSE of 1.791, and the minimal RMSE of 1.338 based on confirmed cases in Oman. In addition, the developed model achieved the death cases with the values of 1.609, and 1.268 for MSE and RMSE, whereas the MSE and the RMSE value of 1.945, and 1.394 is achieved by the developed model using recovered cases in China. © 2020 Walter de Gruyter GmbH, Berlin/Boston 2020.