The coronavirus disease (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has rapidly spread worldwide recently, leading to global social and economic disruption. Although the emergently approved vaccine programs against SARS-CoV-2 have been rolled out globally, the number of COVID-19 daily cases and deaths has remained significantly high. Here, we attempt to computationally screen for possible medications for COVID-19 via rapidly estimating the highly potential inhibitors from an FDA-approved drug database against the main protease (Mpro) of SARS-CoV-2. The approach combined molecular docking and fast pulling of ligand (FPL) simulations that were demonstrated to be accurate and suitable for quick prediction of SARS-CoV-2 Mpro inhibitors. The results suggested that twenty-seven compounds were capable of strongly associating with SARS-CoV-2 Mpro. Among them, the seven top leads are daclatasvir, teniposide, etoposide, levoleucovorin, naldemedine, cabozantinib, and irinotecan. The potential application of these drugs in COVID-19 therapy has thus been discussed.

To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database. First, the trained ML model was used to scan the library quickly and reliably for possible Mpro inhibitors. The ML output was then confirmed using atomistic simulations integrating molecular docking and molecular dynamic simulations with the linear interaction energy scheme. The results turned out to show that there was evidently good agreement between ML and atomistic simulations. Ten substances were proposed to be able to inhibit SARS-CoV-2 Mpro. Seven of them have high-nanomolar affinity and are very potential inhibitors. The strategy has been proven to be reliable and appropriate for fast prediction of SARS-CoV-2 Mpro inhibitors, benefiting for new emerging SARS-CoV-2 variants in the future accordingly.

Callicarpa candicans (Burm. f.) Hochr. (Callicarpa cana L.) is a medicinal plant that is distributed mainly in the tropics and subtropics of Asia and finds a wide range of uses in traditional medicine. In this study, we attempted and optimized the microwave-assisted hydro-distillation (MAHD) process to obtain essential oil from the leaves of C. candicans. In addition, the obtained oil was analyzed for volatile composition by gas chromatography–mass spectrometry (GC-MS) and assayed for bioactivity against several bacteria and cancer cell lines. To optimize the extraction process, response surface methodology (RSM) in combination with central composite design (CCD) was adopted. Experimental design and optimization were carried out with respect to three experimental factors including the ratio of water to raw material, extraction time, and microwave power. The optimal extraction conditions were obtained as follows: water to raw material ratio of 6/1 (v/w), extraction time 42 min, and microwave power 440 W. Composition determination of the obtained C. candicans essential oil indicated the presence of predominant components including caryophyllene (10.45%), cadinene (10.28%), gurjunene (8.95%), muurolene (8.92%), selinene (7.06%), selinene (5.59%), and copaene (5.40%). In comparison with the essential oils obtained via traditional hydro-distillation method, the essential oil extracted by MAHD exhibited superior anti-proliferative activity on all tested cancer cell lines. Current results imply that the MAHD is capable of recovering biologically-active natural products of greater quantity than that recovered by the conventional distillation.

Alchemical binding free energy calculations are one of the most accurate methods for estimating ligand-binding affinity. Assessing the accuracy of the approach over protein targets is one of the most interesting issues. The free energy difference of binding between a protein and a ligand was calculated via the alchemical approach. The alchemical approach exhibits satisfactory accuracy over four targets, including AmpC beta-lactamase (AmpC); glutamate receptor, ionotropic kainate 1 (GluK1); heat shock protein 90 (Hsp90); and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro). In particular, the correlation coefficients between calculated binding free energies and the respective experiments over four targets range from 0.56 to 0.86. The affinity computed via free energy perturbation (FEP) simulations is overestimated over the experimental value. Particularly, the electrostatic interaction free energy rules the binding process of ligands to AmpC and GluK1. However, the van der Waals (vdW) interaction free energy plays an important role in the ligand-binding processes of HSP90 and SARS-CoV-2 Mpro. The obtained results associate with the hydrophilic or hydrophobic properties of the ligands. This observation may enhance computer-aided drug design.

Oligomerization of amyloid beta (Aβ) peptides has been considered as the crucially causative agent in the development of Alzheimer's disease. Etersalate, a nonsteroidal anti-inflammatory oral drug (United State Food and Drug Administration—Unique Ingredient Identifier: 653GN04T2G) was previously suggested to bind well to proto-fibrils of Aβ peptides in silico. Here, the effect of etersalate on the oligomerization of soluble Aβ_16–22 hexamer (6Aβ_16–22) were extensively investigated using temperature replica exchange molecular dynamics (REMD) simulations over ~16.8 μs in total for 48 replicas (350 ns per replica). The results reveal that etersalate can enter the inner space or bind on the surface of 6Aβ_16–22 conformations, which destabilizes the hexamer. Etersalate was predicted to able to cross the blood brain barrier using prediction of absorption, distribution, metabolism, and excretion—toxicity (preADMET) tools. Overall, although the investigation was performed with the low concentration of trial inhibitor, the obtained results indicate that etersalate is a potential drug candidate for AD through inhibiting formation of Aβ oligomers with the average binding free energy of -11.7 kcal/mol.