The outbreak of novel coronavirus disease (COVID-19) caused by SARS-CoV-2 poses a serious threat to human health and world economic activity. There is no specific drug for the treatment of COVID-19 patients at this moment. Traditionally, people have been using spices like ginger, fenugreek and onion, etc. for the remedy of a common cold. This work identifies the potential inhibitors of the main protease (M pro) and spike (S) receptor of SARS-CoV-2 from 10 readily available spices. These two proteins, S and M pro , play an important role during the virus entry into the host cell, and replication and transcription processes of the virus, respectively. To identify potential molecules an in-house databank containing 1040 compounds was built-up from the selected spices. Structure-based virtual screening of this databank was performed with two important SARS-CoV-2 proteins using Glide. Top hits resulted from virtual screening (VS) were subjected to molecular docking using AutoDock 4.2 and AutoDock Vina to eliminate false positives. The top six hits against M pro and top five hits against spike receptor subjected to 130 ns molecular dynamic simulation using GROMACS. Finally, the compound 1-, 2-, 3and 5-M pro complexes, and compound 17-, 18-, 19-, 20and 21spike receptor complexes showed stability throughout the simulation time. The ADME values also supported the drug-like nature of the selected hits. These nine compounds are available in onion, garlic, ginger, peppermint, chili and fenugreek. All the spices are edible and might be used as home remedies against COVID-19 after proper biological evaluation.

The recent outbreak of the 2019 novel coronavirus disease (COVID-19) has been proved as a global threat. No particular drug or vaccine has not yet been discovered which may act specifically against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and causes COVID-19. For this highly infectious virus, 3CL-like main protease (3CL pro) plays a key role in the virus life cycle and can be considered as a pivotal drug target. Structure-based virtual screening of DrugBank database resulted in 20 hits against 3CL pro. Atomistic 100 ns molecular dynamics of five top hits and binding energy calculation analyses were performed for main protease-hit complexes. Among the top five hits, Nafarelin and Icatibant affirmed the binding energy (g_MMPBSA) of-712.94 kJ/mol and-851.74 kJ/mol, respectively. Based on binding energy and stability of protein-ligand complex; the present work reports these two drug-like hits against SARS-CoV-2 main protease.

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A series of 2-substituted and 2,3-substituted quinazolin -4(3H)-one derivatives were designed and synthesized based on the structure of febrifugine. The structures of the new compounds were confirmed by spectral analysis. The in vivo biological activity test results indicated that those compounds exhibited antimalarial activities against Plasmodium berghei in mice, at a dose of 5 mg/kg. Compared to Chloroquine and Artemisinin, these compounds have the advantages of shorter synthetic routes and consequently are highly cost effective in nature.

Febrifugine and its derivatives are effective against Plasmodium falciparum. Using PHASE algorithm, a five-point pharmacophore model with two hydrogen bond acceptor (A), one positively ionizable (P) and two aromatic rings (R), was developed to derive a predictive ligand-based statistically significant 3D-quantitative structure-activity relationship (QSAR) model (r2 = 0.972, SD = 0.3, F = 173.4, Q2 = 0.712, RMSE = 0.3, Person-R = 0.94, and r2pred = 0.8) to explicate the structural attributes crucial for antimalarial activity. The developed pharmacophore model and 3D QSAR model can be a substantial tool for virtual screening and related antimalarial drug discovery research.

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) is highly pathogenic to humans and has created health care threats worldwide. This urgent situation has focused the researchers worldwide towards the development of novel vaccine or small molecule therapeutics for SARS-CoV-2. Although several vaccines have already been discovered and are in use for the masses, no therapeutic medication has yet been approved by FDA for the treatment of COVID-19. Keeping this in view, in the present study, we have identified promising hits against the main protease (Mpro) of SARS-CoV-2 from edible mushrooms. Structure-based virtual screening (VS) of 2433 compounds derived from mushrooms was performed with Mpro protein (6LU7). Four promising hits, namely, Kynapcin-12 (M_78), Kynapcin-28 (M_82), Kynapcin-24 (M_83), and Neonambiterphenyls-A (M_366) were identified based on the result of docking, Lipinski’s rule, 100 ns molecular dynamics (MD) simulation and MM/PBSA binding free energy calculations. Finally, the inhibitory properties of these hits were compared with three known inhibitors, baicalein (1), baicalin (2), and biflavonoid (3). Data indicated that M_78, M_82 and M_83 compounds present in edible mushroom Polyozellus multiplex were potent inhibitors of Mproprotein (6LU7). It could be concluded that edible mushroom Polyozellus multiplex has potential activity against SARS-CoV-2 infection and identified molecules could be further explored as therapeutic inhibitors against SARS-CoV-2.

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We present a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images. We demonstrate a successful use of our method with the time series generated from high content thermal imaging videos of patients suffering from the aqueous deficient dry eye (ADDE) disease. Remarkably, network analyses of thermal imaging time series of contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis (Lasik) surgery has been conducted, exhibit pronounced similarity with results obtained from ADDE patients. We also propose a general framework for the transformation of multidimensional images to networks for futuristic biometry. Our approach is general and scalable to other fluctuation-based devices where network parameters derived from fluctuations, act as effective discriminators and diagnostic markers.