Multi-objective deep learning framework for COVID-19 dataset problems.

Background: It has been reported that a deadly virus known as COVID-19 has arisen in China and has spread rapidly throughout the country. The globe was shattered, and a large number of people on the planet died. It quickly became an epidemic due to the absence of apparent symptoms and causes for patients, confusion appears due to the lack of sufficient laboratory results, and its intelligent algorithms were used to make decisions on clinical outcomes. Methods: This study developed a new framework for medical datasets with high missing values based on deep-learning optimization models. The robustness of our model is achieved by combining: Data Missing Care (DMC) Framework to overcome the problem of high missing data in medical datasets, and Grid-Search optimization used to develop an improved deep predictive training model for patients with COVID-19 by setting multiple hyperparameters and tuning assessments on three deep learning algorithms: ANN (Artificial Neural Network), CNN (Convolutional Neural Network), and Recurrent Neural Networks (RNN). Results: The experiment results conducted on three medical datasets showed the effectiveness of our hybrid approach and an improvement in accuracy and efficiency since all the evaluation metrics were close to ideal for all deep learning classifiers. We got the best evaluation in terms of accuracy 98%, precession 98.5%, F1-score 98.6%, and ROC Curve (95% to 99%) for the COVID-19 dataset provided by GitHub. The second dataset is also Covid-19 provided by Albert Einstein Hospital with high missing data after applying our approach the accuracy reached more than 91%. Third dataset for Cervical Cancer provided by Kaggle all the evaluation metrics reached more than 95%. Conclusions: The proposed formula for processing this type of data can replace the traditional formats in optimization while providing high accuracy and less time to classify patients. Whereas, the experimental results of our approach, supported by comprehensive statistical analysis, can improve the overall evaluation performance of the problem of classifying medical data sets with high missing values. Therefore, this approach can be used in many areas such as energy management, environment, and medicine.

Microwave Assisted Synthesis of 2-amino-4-chloro-pyrimidine Derivatives: Anticancer and Computational Study on Potential Inhibitory Action against COVID-19

We report microwave synthesis of seven unique pyrimidine anchored derivatives (1-7) incorporating multifunctional amino derivatives along with their in vitro anticancer activity and their activity against COVID-19 in silico. 1-7 were characterized by different analytical and spectroscopic techniques. Cytotoxic activity of 1-7 was tested against HCT116 and MCF7 cell lines, whereby 6 exhibited highest anticancer activity on HCT116 and MCF7 with EC50 values of 89.24±1.36 µM and 89.37±1.17 µM, respectively. Molecular docking was performed for derivatives (1-7) on main protease for SARS-CoV-2 (PDB ID: 6LU7). Results revealed that most of the derivatives had superior or equivalent affinity for the 3CLpro, as determined by docking and binding energy scores. 6 topped the rest with highest binding energy score of -8.12 kcal/mol with inhibition constant reported as 1.11 µM. ADME, drug-likeness, and pharmacokinetics properties of 1-7 were tested using Swiss ADME tool. Toxicity analysis was done with pkCSM online server. All derivatives showed high GI absorption. Except 1 and 3, all derivatives showed blood brain barrier permeability. Most derivatives showed negative logKp values suggesting derivatives are less skin permeable and bioavailability score of all derivatives was 0.55. The toxicity analysis demonstrated that all derivatives have no skin sensitization properties. 6 and 7 showed maximum tolerated dose (Human) values of -0.03 and -0.018, respectively and absence of AMES toxicity.

Phytocompounds as potential inhibitors of SARS-CoV-2 Mpro and PLpro through computational studies.

The inhibition of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) and papain-like protease (PLpro) prevents viral multiplications; these viral enzymes have been recognized as one of the most favorable targets for drug discovery against SARS-CoV-2. In the present study, we screened 225 phytocompounds present in 28 different Indian spices to identify compounds as potential inhibitors of SARS-CoV-2 Mpro and PLpro. Molecular docking, molecular dynamics simulation, molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) binding free energy calculations, and absorption, distribution, metabolism, excretion and toxicity (ADMET) studies were done. Based on binding affinity, dynamics behavior, and binding free energies, the present study identifies pentaoxahexacyclo-dotriacontanonaen-trihydroxybenzoate derivative (PDT), rutin, and dihyroxy-oxan-phenyl-chromen-4-one derivative (DOC), luteolin-7-glucoside-4'-neohesperidoside as promising inhibitors of SARS-CoV-2 Mpro and PLpro, respectively.