Computer-aided drug screening by molecular docking, molecular dynamics (MD) and structural-activity relationship (SAR) can offer an efficient approach to identify promising drug repurposing candidates for COVID-19 treatment. In this study, computational screening is performed by molecular docking of 1615 Food and Drug Administration (FDA) approved drugs against the main protease (Mpro) of SARS-CoV-2. Several promising approved drugs, including Simeprevir, Ergotamine, Bromocriptine and Tadalafil, stand out as the best candidates based on their binding energy, fitting score and noncovalent interactions at the binding sites of the receptor. All selected drugs interact with the key active site residues, including His41 and Cys145. Various noncovalent interactions including hydrogen bonding, hydrophobic interactions, pi-sulfur and pi-pi interactions appear to be dominant in drug-Mpro complexes. MD simulations are applied for the most promising drugs. Structural stability and compactness are observed for the drug-Mpro complexes. The protein shows low flexibility in both apo and holo form during MD simulations. The MM/PBSA binding free energies are also measured for the selected drugs. For pattern recognition, structural similarity and binding energy prediction, multiple linear regression (MLR) models are used for the quantitative structural-activity relationship. The binding energy predicted by MLR model shows an 82% accuracy with the binding energy determined by molecular docking. Our details results can facilitate rational drug design targeting the SARS-CoV-2 main protease.

COVID-19 or novel coronavirus disease, which has already been declared as a worldwide pandemic, at first had an outbreak in a large city of China, named Wuhan. More than two hundred countries around the world have already been affected by this severe virus as it spreads by human interaction. Moreover, the symptoms of novel coronavirus are quite similar to the general seasonal flu. Screening of infected patients is considered as a critical step in the fight against COVID-19. As there are no distinctive COVID-19 positive case detection tools available, the need for supporting diagnostic tools has increased. Therefore, it is highly relevant to recognize positive cases as early as possible to avoid further spreading of this epidemic. However, there are several methods to detect COVID-19 positive patients, which are typically performed based on respiratory samples and among them, a critical approach for treatment is radiologic imaging or X-Ray imaging. Recent findings from X-Ray imaging techniques suggest that such images contain relevant information about the SARS-CoV-2 virus. Application of Deep Neural Network (DNN) techniques coupled with radiological imaging can be helpful in the accurate identification of this disease, and can also be supportive in overcoming the issue of a shortage of trained physicians in remote communities. In this article, we have introduced a VGG-16 (Visual Geometry Group, also called OxfordNet) Network-based Faster Regions with Convolutional Neural Networks (Faster R–CNN) framework to detect COVID-19 patients from chest X-Ray images using an available open-source dataset. Our proposed approach provides a classification accuracy of 97.36%, 97.65% of sensitivity, and a precision of 99.28%. Therefore, we believe this proposed method might be of assistance for health professionals to validate their initial assessment towards COVID-19 patients.

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Many Bangladeshi medicinal plants have been used to treat Alzheimer’s disease and other neurodegenerative diseases. In the present study, the anticholinesterase effects of eight selected Bangladeshi medicinal plant species were investigated. Species were selected based on the traditional uses against CNS-related diseases. Extracts were prepared using a gentle cold extraction method. In vitro cholinesterase inhibitory effects were measured by Ellman’s method in 96-well microplates. Blumea lacera (Compositae) and Cyclea barbata (Menispermaceae) were found to have the highest acetylcholinesterase inhibitory (IC50, 150 ± 11 and 176 ± 14 µg/mL, respectively) and butyrylcholinesterase inhibitory effect (IC50, 297 ± 13 and 124 ± 2 µg/mL, respectively). Cyclea barbata demonstrated competitive inhibition, where Blumea lacera showed an uncompetitive inhibition mode for acetylcholinesterase. Smilax guianensis (Smilacaceae) and Byttneria pilosa (Malvaceae) were also found to show moderate AChE inhibition (IC50, 205 ± 31 and 221 ± 2 µg/mL, respectively), although no significant BChE inhibitory effect was observed for extracts from these plant species. Among others, Thunbergia Grandiflora (Acanthaceae) and Mikania micrantha (Compositae) were found to display noticeable AChE (IC50, 252 ± 22 µg/mL) and BChE (IC50, 314 ± 15 µg/mL) inhibitory effects, respectively. Molecular docking experiment suggested that compounds 5-hydroxy-3,6,7,3′,4′-pentamethoxyflavone (BL4) and kaempferol-3-O-α-L-rhamnopyranosyl-(1⟶6)-β-D-glucopyranoside (BL5) from Blumea lacera bound stably to the binding groove of the AChE and BChE by hydrogen-bond interactions, respectively. Therefore, these compounds could be candidates for cholinesterase inhibitors. The present findings demonstrated that Blumea lacera and Cyclea barbata are interesting objects for further studies aiming at future therapeutics for Alzheimer’s disease.

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