Potential Inhibitors for Novel Coronavirus Protease Identified by Virtual Screening of 606 Million Compounds

In silico approaches to identify novel anti-diabetic type 2 agents against dipeptidyl peptidase IV from isoxazole derivatives of usnic acid

Diabetes mellitus (DM) is a serious worldwide health issue in the twenty-first century. Additionally, DM, a metabolic endocrine illness that affects the digestion of proteins, carbohydrates, and lipids, has a death rate of 4.9 million individuals globally. This study aims to find anti-diabetic inhibitor for type 2 diabetes (T2D) that inhibits the dipeptidyl peptidase IV (DPP-IV) enzyme using in silico methods. From a range of published literature sources, thirty (30) isoxazole derivatives of UA (IDUA) were selected for this study. To ascertain the possible inhibitory effects of IDUA, ADMET, molecular docking, density functional theory analyses, molecular dynamic simulation and MM/PBSA were conducted. Eleven compounds (1, 2, 3, 4, 7, 13, 18, 21, 22, 24, and 27) were selected from the ADMET study, which were subjected to perform molecular docking against the DPP-IV enzyme of T2D, and findings indicated two compounds (compound 2 and compound 3) showed comparable binding affinity with the reference compound "Linagliptin". In contrast to the reference molecule, which had a binding affinity of - 8.6 kcal/mol against DPP-IV, compound 2 and compound 3 have binding affinities of - 8.1 and - 8.0 kcal/mol, respectively. Furthermore, based on Lipinski's Rule of Five, ELUMO, EHOMO, band energy gap, drug-likeness and DFT-based studies demonstrated druggability and high reactivity for these compounds. In addition, the molecular dynamic (MD) techniques to confirm that docked complexes remained stable and that the binding orientation obtained during docking tests were accurate. These compounds may be investigated in vitro and in vivo for the development of potential DPP-IV of T2D inhibitors.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-025-04287-5.

Dihydroquercetin nanoparticles nasal gel is a promising formulation for amelioration of Alzheimer's disease

Dihydroquercetin is a natural flavonoid with anti-inflammatory, antioxidant, and neuroprotective activities. Dihydroquercetin exhibits a great neuroprotector promise in Alzheimer's disorder via preventing the aggregation of amyloid-beta-peptide-Aβ(1-42). The goal of the study was to create dihydroquercetin-loaded-chitosan nanoparticles (DHQ-CS NPs) loaded to a mucoadhesive, thermosensitive in-situ gel for direct nasal administration to cure Alzheimer's disorder. Loading drug in chitosan nanoparticles and incorporation into thermosensitive gel enhanced residence time and reduced mucociliary-clearance. Different in-vitro-physicochemical-characteristics of gels and nanoparticles-characterization were used to evaluate the formulations. The therapeutic effectiveness of DHQ-CS NPs gel was evaluated behaviorally, biochemically and histopathologically in Alzheimer's-rat-model compared to intranasal DHQ gel. The small particles-size was obtained = 235.3 nm of DHQ-CS NPs. The DHQ-CS NPs gel demonstrated a greater release rate compared to the raw DHQ gel. Additionally, the nasal-administration of the DHQ-CS NPs gel showed better In-vivo results compared to DHQ gel, through improvement of memory and learning deficits and also the exploratory behavior and new object memory in streptozotocin induced-Alzheimer rats. Biochemically, the intranasal DHQ-CS NPs gel, showed reduced both Aβ-protein formation and tau protein hyperphosphorylation, inhibition of acetylcholine esterase activity and oxidative stress in the brain with increase of total antioxidants in the brain and serum, compared to DHQ gel. Histopathologically, the DHQ-CS NPs nasal gel produced improvement in the hippocampal and cerebral cortex structures, being comparable to the normal group. Consequently, the intranasal DHQ-CS NPs loaded in-situ gel seems to be a promising therapeutic formulation for Alzheimer's disease medication.

Insights into the stereoisomerism of dihydroquercetin: analytical and pharmacological aspects

Dihydroquercetin (DHQ) is a representative of flavonoids that is available on the market as a food supplement and registered as an active pharmaceutical ingredient. The structure of this compound is characterized by the presence of two chiral centers in positions 2 and 3 of the pyranone ring. Current regulatory documentation on DHQ lacks quantitative analysis of the stereoisomers of this flavanonol. This poses potential risks for consumers of DHQ-based dietary supplements and developers of new drugs. This review was conducted to systematize data on the pharmacology of DHQ stereoisomers and the possible methods of controlling them in promising chiral drugs. We found that relying on literature data of polarimetry for the identification of DHQ stereoisomers is currently impossible due to these heterogeneities. NMR spectroscopy allows to distinguishing between trans- and cis-DHQ using chemical shifts values. Only HPLC is currently characterized by sufficient enantioselectivity. Regarding pharmacology, the most active stereoisomer of DHQ should be identified, if the substituents in chiral centers both take part in binding with the biological target. The significant impact of stereochemical structure on the pharmacokinetics of DHQ isomers was reported. The question about these toxicity of these compounds remains open. The results of the conducted review of scientific literature indicate the necessity of revising the pharmacology of DHQ taking into account its stereoisomerism.

Prediction of Binding Pose and Affinity of Nelfinavir, a SARS-CoV-2 Main Protease Repositioned Drug, by Combining Docking, Molecular Dynamics, and Fragment Molecular Orbital Calculations

A novel in silico drug design procedure is described targeting the Main protease (Mpro) of the SARS-CoV-2 virus. The procedure combines molecular docking, molecular dynamics (MD), and fragment molecular orbital (FMO) calculations. The binding structure and properties of Mpro were predicted for Nelfinavir (NFV), which had been identified as a candidate compound through drug repositioning, targeting Mpro. Several poses of the Mpro and NFV complexes were generated by docking, from which four docking poses were selected by scoring with FMO energy. Then, each pose was subjected to MD simulation, 100 snapshot structures were sampled from each of the generated MD trajectories, and the structures were evaluated by FMO calculations to rank the pose based on binding energy. Several residues were found to be important in ligand recognition, including Glu47, Asp48, Glu166, Asp187, and Gln189, all of which interacted strongly with NFV. Asn142 is presumably regarded to form hydrogen bonds or CH/π interaction with NFV; however, in the present calculation, their interactions were transient. Moreover, the tert-butyl group of NFV had no interaction with Mpro. Identifying such strong and weak interactions provides candidates for maintaining and substituting ligand functional groups and important suggestions for drug discovery using drug repositioning. Besides the interaction between NFV and the amino acid residues of Mpro, the desolvation effect of the binding pocket also affected the ranking order. A similar procedure of drug design was applied to Lopinavir, and the calculated interaction energy and experimental inhibitory activity value trends were consistent. Our approach provides a new guideline for structure-based drug design starting from a candidate compound whose complex crystal structure has not been obtained.

Compromise in Docking Power of Liganded Crystal Structures of Mpro SARS-CoV-2 Surpasses 90% Success Rate

Herein, we present the capacity of three different molecular docking programs (AutoDock, AutoDock Vina, and PLANTS) to identify and reproduce the binding modes of ligands present in 247 covalent and 169 noncovalent complex crystal structures of the severe acute respiratory syndrome coronavirus 2 main protease (Mpro). The compromise in docking power is evaluated with respect to their ability to generate poses similar to the crystal structure binding mode (heavy atoms' root-mean-square deviation < 2 Å) and their ability to recognize the native binding mode with an included compensation for the scoring function error. Noncovalently bound inhibitors are best modeled by AutoDock Vina (90.6% success rate in the active site), while the most relevant results for covalently bound inhibitors are produced by PLANTS (93.0%). AutoDock shows acceptable performance for both types of ligands, 81.1 and 76.4% for noncovalent and covalent complexes, respectively. All three programs manifest worse performance when reproducing surface-bound ligands. Comparison with other works illustrates the importance of crystal structure processing (12% of noncovalent and 26% of covalent ligands had to be manually corrected), proper sampling protocol settings, and inclusion of root-mean-square deviation (RMSD)/scoring function error compensations in crystal structure pose identification. Results are analyzed with respect to a clustering scheme of the noncovalently bound ligands and the chemical reaction type of the covalent ligand bound to the Cys145 residue. A comparison of screening power based on the docking scores of noncovalent ligands from the crystal structures with a "Directory of Useful Decoys, Enhanced" set of known decoys (6562 compounds) and ZINC15 in vivo subset (60,394 compounds) is provided. Ligand and protein input files are provided for future benchmarking purposes.

Aminopyrimidine Derivatives as Multiflavivirus Antiviral Compounds Identified from a Consensus Virtual Screening Approach

Around three billion people are at risk of infection by the dengue virus (DENV) and potentially other flaviviruses. Worldwide outbreaks of DENV, Zika virus (ZIKV), and yellow fever virus (YFV), the lack of antiviral drugs, and limitations on vaccine usage emphasize the need for novel antiviral research. Here, we propose a consensus virtual screening approach to discover potential protease inhibitors (NS3pro) against different flavivirus. We employed an in silico combination of a hologram quantitative structure-activity relationship (HQSAR) model and molecular docking on characterized binding sites followed by molecular dynamics (MD) simulations, which filtered a data set of 7.6 million compounds to 2,775 hits. Lastly, docking and MD simulations selected six final potential NS3pro inhibitors with stable interactions along the simulations. Five compounds had their antiviral activity confirmed against ZIKV, YFV, DENV-2, and DENV-3 (ranging from 4.21 ± 0.14 to 37.51 ± 0.8 μM), displaying aggregator characteristics for enzymatic inhibition against ZIKV NS3pro (ranging from 28 ± 7 to 70 ± 7 μM). Taken together, the compounds identified in this approach may contribute to the design of promising candidates to treat different flavivirus infections.

In vitro and in vivo effects of Pelargonium sidoides DC. root extract EPs® 7630 and selected constituents against SARS-CoV-2 B.1, Delta AY.4/AY.117 and Omicron BA.2

The occurrence of immune-evasive SARS-CoV-2 strains emphasizes the importance to search for broad-acting antiviral compounds. Our previous in vitro study showed that Pelargonium sidoides DC. root extract EPs® 7630 has combined antiviral and immunomodulatory properties in SARS-CoV-2-infected human lung cells. Here we assessed in vivo effects of EPs® 7630 in SARS-CoV-2-infected hamsters, and investigated properties of EPs® 7630 and its functionally relevant constituents in context of phenotypically distinct SARS-CoV-2 variants. We show that EPs® 7630 reduced viral load early in the course of infection and displayed significant immunomodulatory properties positively modulating disease progression in hamsters. In addition, we find that EPs® 7630 differentially inhibits SARS-CoV-2 variants in nasal and bronchial human airway epithelial cells. Antiviral effects were more pronounced against Omicron BA.2 compared to B.1 and Delta, the latter two preferring TMPRSS2-mediated fusion with the plasma membrane for cell entry instead of receptor-mediated low pH-dependent endocytosis. By using SARS-CoV-2 Spike VSV-based pseudo particles (VSVpp), we confirm higher EPs® 7630 activity against Omicron Spike-VSVpp, which seems independent of the serine protease TMPRSS2, suggesting that EPs® 7630 targets endosomal entry. We identify at least two molecular constituents of EPs® 7630, i.e., (-)-epigallocatechin and taxifolin with antiviral effects on SARS-CoV-2 replication and cell entry. In summary, our study shows that EPs® 7630 ameliorates disease outcome in SARS-CoV-2-infected hamsters and has enhanced activity against Omicron, apparently by limiting late endosomal SARS-CoV-2 entry.

Flavonoid as possible therapeutic targets against COVID-19: a scoping review of in silico studies

OBJECTIVES

This scoping review aims to present flavonoid compounds' promising effects and possible mechanisms of action on potential therapeutic targets in the SARS-CoV-2 infection process.

METHODS

A search of electronic databases such as PubMed and Scopus was carried out to evaluate the performance of substances from the flavonoid class at different stages of SARS-CoV-2 infection.

RESULTS

The search strategy yielded 382 articles after the exclusion of duplicates. During the screening process, 265 records were deemed as irrelevant. At the end of the full-text appraisal, 37 studies were considered eligible for data extraction and qualitative synthesis. All the studies used virtual molecular docking models to verify the affinity of compounds from the flavonoid class with crucial proteins in the replication cycle of the SARS-CoV-2 virus (Spike protein, PLpro, 3CLpro/ MPro, RdRP, and inhibition of the host's ACE II receptor). The flavonoids with more targets and lowest binding energies were: orientin, quercetin, epigallocatechin, narcissoside, silymarin, neohesperidin, delphinidin-3,5-diglucoside, and delphinidin-3-sambubioside-5-glucoside.

CONCLUSION

These studies allow us to provide a basis for in vitro and in vivo assays to assist in developing drugs for the treatment and prevention of COVID-19.

Ingestion of taxifolin-rich foods affects brain activity, mental fatigue, and the whole blood transcriptome in healthy young adults: a randomized, double-blind, placebo-controlled, crossover study

The antioxidant properties of polyphenols, which are found in most plants, have been shown to be useful for maintaining health, including enhancing brain function and alleviating stress. We aimed to investigate the effect of a single intake of taxifolin-containing foods on cognitive task performance and whole blood gene expression in healthy young adults. This study was a randomized, placebo-controlled, double-blind, crossover trial in which healthy young adults were administered a single dose of either a placebo or food containing taxifolin. Cognitive tests (serial 3s, serial 7s, and rapid visual information processing) to examine brain activity and visual analog scale questionnaires to analyze mental fatigue were applied. The set of tests was repeated four times. The findings showed that taxifolin intake improved calculation abilities and reduced mental fatigue. An analysis of whole blood gene expression before and after the test revealed that the expression of foreign substance removal-related genes increased following the ingestion of taxifolin and that most differentially expressed genes were enriched in granulocytes. Taxifolin intake was shown to affect the brain activity of healthy young adults and demonstrated an antifatigue effect, thereby reducing subjective fatigue. A single intake of taxifolin may enhance the removal of foreign substances by strengthening the innate immune system and suppressing the occurrence of injury.

Efficient virtual high-content screening using a distance-aware transformer model

Molecular similarity search is an often-used method in drug discovery, especially in virtual screening studies. While simple one- or two-dimensional similarity metrics can be applied to search databases containing billions of molecules in a reasonable amount of time, this is not the case for complex three-dimensional methods. In this work, we trained a transformer model to autoencode tokenized SMILES strings using a custom loss function developed to conserve similarities in latent space. This allows the direct sampling of molecules in the generated latent space based on their Euclidian distance. Reducing the similarity between molecules to their Euclidian distance in latent space allows the model to perform independent of the similarity metric it was trained on. While we test the method here using 2D similarity as proof-of-concept study, the algorithm will enable also high-content screening with time-consuming 3D similarity metrics. We show that the presence of a specific loss function for similarity conservation greatly improved the model's ability to predict highly similar molecules. When applying the model to a database containing 1.5 billion molecules, our model managed to reduce the relevant search space by 5 orders of magnitude. We also show that our model was able to generalize adequately when trained on a relatively small dataset of representative structures. The herein presented method thereby provides new means of substantially reducing the relevant search space in virtual screening approaches, thus highly increasing their throughput. Additionally, the distance awareness of the model causes the efficiency of this method to be independent of the underlying similarity metric.

Improving drug discovery through parallelism

Compound identification in ligand-based virtual screening is limited by two key issues: the quality and the time needed to obtain predictions. In this sense, we designed OptiPharm, an algorithm that obtained excellent results in improving the sequential methods in the literature. In this work, we go a step further and propose its parallelization. Specifically, we propose a two-layer parallelization. Firstly, an automation of the molecule distribution process between the available nodes in a cluster, and secondly, a parallelization of the internal methods (initialization, reproduction, selection and optimization). This new software, called pOptiPharm, aims to improve the quality of predictions and reduce experimentation time. As the results show, the performance of the proposed methods is good. It can find better solutions than the sequential OptiPharm, all while reducing its computation time almost proportionally to the number of processing units considered.

Paving New Roads Using Allium sativum as a Repurposed Drug and Analyzing its Antiviral Action Using Artificial Intelligence Technology

CONTEXT

The whole universe is facing a coronavirus catastrophe, and prompt treatment for the health crisis is primarily significant. The primary way to improve health conditions in this battle is to boost our immunity and alter our diet patterns. A common bulb veggie used to flavor cuisine is garlic. Compounds in the plant that are physiologically active are present, contributing to its pharmacological characteristics. Among several food items with nutritional value and immunity improvement, garlic stood predominant and more resourceful natural antibiotic with a broad spectrum of antiviral potency against diverse viruses. However, earlier reports have depicted its efficacy in the treatment of a variety of viral illnesses. Nonetheless, there is no information on its antiviral activities and underlying molecular mechanisms.

OBJECTIVES

The bioactive compounds in garlic include organosulfur (allicin and alliin) and flavonoid (quercetin) compounds. These compounds have shown immunomodulatory effects and inhibited attachment of coronavirus to the angiotensin-converting enzyme 2 (ACE2) receptor and the Mpro of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Further, we have discussed the contradictory impacts of garlic used as a preventive measure against the novel coronavirus.

METHOD

The GC/MS analysis revealed 18 active chemicals, including 17 organosulfur compounds in garlic. Using the molecular docking technique, we report for the first time the inhibitory effect of the under-consideration compounds on the host receptor ACE2 protein in the human body, providing a crucial foundation for understanding individual compound coronavirus resistance on the main protease protein of SARS-CoV-2. Allyl disulfide and allyl trisulfide, which make up the majority of the compounds in garlic, exhibit the most potent activity.

RESULTS

Conventional medicine has proven its efficiency from ancient times. Currently, our article's prime spotlight was on the activity of Allium sativum on the relegation of viral load and further highlighted artificial intelligence technology to study the attachment of the allicin compound to the SARS-CoV-2 receptor to reveal its efficacy.

CONCLUSIONS

The COVID-19 pandemic has triggered interest among researchers to conduct future research on molecular docking with clinical trials before releasing salutary remedies against the deadly malady.

Drug repositioning: A bibliometric analysis

Drug repurposing has become an effective approach to drug discovery, as it offers a new way to explore drugs. Based on the Science Citation Index Expanded (SCI-E) and Social Sciences Citation Index (SSCI) databases of the Web of Science core collection, this study presents a bibliometric analysis of drug repurposing publications from 2010 to 2020. Data were cleaned, mined, and visualized using Derwent Data Analyzer (DDA) software. An overview of the history and development trend of the number of publications, major journals, major countries, major institutions, author keywords, major contributors, and major research fields is provided. There were 2,978 publications included in the study. The findings show that the United States leads in this area of research, followed by China, the United Kingdom, and India. The Chinese Academy of Science published the most research studies, and NIH ranked first on the h-index. The Icahn School of Medicine at Mt Sinai leads in the average number of citations per study. Sci Rep, Drug Discov. Today, and Brief. Bioinform. are the three most productive journals evaluated from three separate perspectives, and pharmacology and pharmacy are unquestionably the most commonly used subject categories. Cheng, FX; Mucke, HAM; and Butte, AJ are the top 20 most prolific and influential authors. Keyword analysis shows that in recent years, most research has focused on drug discovery/drug development, COVID-19/SARS-CoV-2/coronavirus, molecular docking, virtual screening, cancer, and other research areas. The hotspots have changed in recent years, with COVID-19/SARS-CoV-2/coronavirus being the most popular topic for current drug repurposing research.

Novel Drug Design for Treatment of COVID-19: A Systematic Review of Preclinical Studies

Background

Since the beginning of the novel coronavirus (SARS-CoV-2) disease outbreak, there has been an increasing interest in discovering potential therapeutic agents for this disease. In this regard, we conducted a systematic review through an overview of drug development (in silico, in vitro, and in vivo) for treating COVID-19.

Methods

A systematic search was carried out in major databases including PubMed, Web of Science, Scopus, EMBASE, and Google Scholar from December 2019 to March 2021. A combination of the following terms was used: coronavirus, COVID-19, SARS-CoV-2, drug design, drug development, In silico, In vitro, and In vivo. A narrative synthesis was performed as a qualitative method for the data synthesis of each outcome measure.

Results

A total of 2168 articles were identified through searching databases. Finally, 315 studies (266 in silico, 34 in vitro, and 15 in vivo) were included. In studies with in silico approach, 98 article study repurposed drug and 91 studies evaluated herbal medicine on COVID-19. Among 260 drugs repurposed by the computational method, the best results were observed with saquinavir (n = 9), ritonavir (n = 8), and lopinavir (n = 6). Main protease (n = 154) following spike glycoprotein (n = 62) and other nonstructural protein of virus (n = 45) was among the most studied targets. Doxycycline, chlorpromazine, azithromycin, heparin, bepridil, and glycyrrhizic acid showed both in silico and in vitro inhibitory effects against SARS-CoV-2.

Conclusion

The preclinical studies of novel drug design for COVID-19 focused on main protease and spike glycoprotein as targets for antiviral development. From evaluated structures, saquinavir, ritonavir, eucalyptus, Tinospora cordifolia, aloe, green tea, curcumin, pyrazole, and triazole derivatives in in silico studies and doxycycline, chlorpromazine, and heparin from in vitro and human monoclonal antibodies from in vivo studies showed promised results regarding efficacy. It seems that due to the nature of COVID-19 disease, finding some drugs with multitarget antiviral actions and anti-inflammatory potential is valuable and some herbal medicines have this potential.

An in silico analysis of Ibuprofen enantiomers in high concentrations of sodium chloride with SARS-CoV-2 main protease

2020 will be remembered worldwide for the outbreak of Coronavirus disease (COVID-19), which quickly spread until it was declared as a global pandemic. The main protease (Mpro) of SARS-CoV-2, a key enzyme in coronavirus, represents an attractive pharmacological target for inhibition of SARS-CoV-2 replication. Here, we evaluated whether the anti-inflammatory drug Ibuprofen, may act as a potential SARS-CoV-2 Mpro inhibitor, using an in silico study. From molecular dynamics (MD) simulations, we also evaluated the influence of ionic strength on the affinity and stability of the Ibuprofen-Mpro complexes. The docking analysis shows that R(-)Ibuprofen and S(+)Ibuprofen isomers can interact with multiple key residues of the main protease, through hydrophobic interactions and hydrogen bonds, with favourable binding energies (-6.2 and -5.7 kcal/mol, respectively). MM-GBSA and MM-PBSA calculations confirm the affinity of these complexes, in terms of binding energies. It also demonstrates that the ionic strength modifies significantly their binding affinities. Different structural parameters calculated from the MD simulations (120 ns) reveal that these complexes are conformational stable in the different conditions analysed. In this context, the results suggest that the condition 2 (0.25 NaCl) bind more tightly the Ibuprofen to Mpro than the others conditions. From the frustration analysis, we could characterize two important regions (Cys44-Pro52 and Linker loop) of this protein involved in the interaction with Ibuprofen. In conclusion, our findings allow us to propose that racemic mixtures of the Ibuprofen enantiomers might be a potential treatment option against SARS-CoV-2 Mpro. However, further research is necessary to determinate their possible medicinal use.Communicated by Ramaswamy H. Sarma.

A two-layer mono-objective algorithm based on guided optimization to reduce the computational cost in virtual screening

Virtual screening methods focus on searching molecules with similar properties to a given compound. Molecule databases are made up of large numbers of compounds and are constantly increasing. Therefore, fast and efficient methodologies and tools have to be designed to explore them quickly. In this context, ligand-based virtual screening methods are a well-known and helpful tool. These methods focus on searching for the most similar molecules in a database to a reference one. In this work, we propose a new tool called 2L-GO-Pharm, which requires less computational effort than OptiPharm, an efficient and robust piece of software recently proposed in the literature. The new-implemented tool maintains or improves the quality of the solutions found by OptiPharm, and achieves it by considerably reducing the number of evaluations needed. Some of the strengths that help 2L-GO-Pharm enhance searchability are the reduction of the search space dimension and the introduction of some circular limits for the angular variables. Furthermore, to ensure a trade-off between exploration and exploitation of the search space, it implements a two-layer strategy and a guided search procedure combined with a convergence test on the rotation axis. The performance of 2L-GO-Pharm has been tested by considering two different descriptors, i.e. shape similarity and electrostatic potential. The results show that it saves up to 87.5 million evaluations per query molecule.

Primer for Designing Main Protease (Mpro) Inhibitors of SARS-CoV-2

The COVID-19 outbreak has been devastating, with hundreds of millions of infections and millions of deaths reported worldwide. In response, the application of structure-activity relationships (SAR) upon experimentally validated inhibitors of SARS-CoV-2 main protease (Mpro) may provide an avenue for the identification of new lead compounds active against COVID-19. Upon the basis of information gleaned from a combination of reported crystal structures and the docking of experimentally validated inhibitors, four "rules" for designing potent Mpro inhibitors have been proposed. The aim here is to guide medicinal chemists toward the most probable hits and to provide guidance on repurposing available structures as Mpro inhibitors. Experimental examination of our own previously reported inhibitors using the four "rules" identified a potential lead compound, the cathepsin inhibitor GB111-NH2, that was 2.3 times more potent than SARS-CoV-2 Mpro inhibitor N3.

Therapeutic role of traditionally used Indian medicinal plants and spices in combating COVID-19 pandemic situation

The coronavirus disease (COVID-19) caused by SARS-CoV-2 is a big challenge and burning issue to the scientific community and doctors worldwide. Globally, COVID-19 has created a health disaster and adversely affects the economic growth. Although some vaccines have already emerged, no therapeutic medication has yet been approved by FDA for the treatment of COVID-19 patients. Traditionally, we have been using different medicinal plants like neem, tulsi, tea, and many spices like garlic, ginger, turmeric, black seed, onion, etc. for the treatment of flu-like diseases. In this paper, we are highlighting the recent research progress in the identification of natural products from the Indian medicinal plants and spices that have potential inhibition properties against SARS-CoV-2. This study will provide an initiative to stimulate further research by providing useful guidance to the medicinal chemists for designing new protease inhibitors effective against SARS-CoV-2 in future.

Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling

Computer modeling is a method that is widely used in scientific investigations to predict the biological activity, toxicity, pharmacokinetics, and synthesis strategy of compounds based on the structure of the molecule. This work is a systematic review of articles performed in accordance with the recommendations of PRISMA and contains information on computer modeling of the interaction of classical flavonoids with different biological targets. The review of used computational approaches is presented. Furthermore, the affinities of flavonoids to different targets that are associated with the infection, cardiovascular, and oncological diseases are discussed. Additionally, the methodology of bias risks in molecular docking research based on principles of evidentiary medicine was suggested and discussed. Based on this data, the most active groups of flavonoids and lead compounds for different targets were determined. It was concluded that flavonoids are a promising object for drug development and further research of pharmacology by in vitro, ex vivo, and in vivo models is required.

In Silico Screening and Testing of FDA-Approved Small Molecules to Block SARS-CoV-2 Entry to the Host Cell by Inhibiting Spike Protein Cleavage

The COVID-19 pandemic began in 2019, but it is still active. The development of an effective vaccine reduced the number of deaths; however, a treatment is still needed. Here, we aimed to inhibit viral entry to the host cell by inhibiting spike (S) protein cleavage by several proteases. We developed a computational pipeline to repurpose FDA-approved drugs to inhibit protease activity and thus prevent S protein cleavage. We tested some of our drug candidates and demonstrated a decrease in protease activity. We believe our pipeline will be beneficial in identifying a drug regimen for COVID-19 patients.

Determination of potential inhibitors based on isatin derivatives against SARS-CoV-2 main protease (mpro): a molecular docking, molecular dynamics and structure-activity relationship studies

SARS-COV-2, the novel coronavirus and root of global pandemic COVID-19 caused a severe health threat throughout the world. Lack of specific treatments raised an effort to find potential inhibitors for the viral proteins. The recently invented crystal structure of SARS-CoV-2 main protease (Mpro) and its key role in viral replication; non-resemblance to any human protease makes it a perfect target for inhibitor research. This article reports a computer-aided drug design (CADD) approach for the screening of 118 compounds with 16 distinct heterocyclic moieties in comparison with 5 natural products and 7 repurposed drugs. Molecular docking analysis against Mpro protein were performed finding isatin linked with a oxidiazoles (A2 and A4) derivatives to have the best docking scores of -11.22 kcal/mol and -11.15 kcal/mol respectively. Structure-activity relationship studies showed a good comparison with a known active Mpro inhibitor and repurposed drug ebselen with an IC50 value of -0.67 μM. Molecular Dynamics (MD) simulations for 50 ns were performed for A2 and A4 supporting the stability of the two compounds within the binding pocket, largely at the S1, S2 and S4 domains with high binding energy suggesting their suitability as potential inhibitors of Mpro for SARS-CoV-2.

Oxidative Stress-Related Mechanisms in SARS-CoV-2 Infections

The COVID-19 pandemic caused relatively high mortality in patients, especially in those with concomitant diseases (i.e., diabetes, hypertension, and chronic obstructive pulmonary disease (COPD)). In most of aforementioned comorbidities, the oxidative stress appears to be an important player in their pathogenesis. The direct cause of death in critically ill patients with COVID-19 is still far from being elucidated. Although some preliminary data suggests that the lung vasculature injury and the loss of the functioning part of pulmonary alveolar population are crucial, the precise mechanism is still unclear. On the other hand, at least two classes of medications used with some clinical benefits in COVID-19 treatment seem to have a major influence on ROS (reactive oxygen species) and RNS (reactive nitrogen species) production. However, oxidative stress is one of the important mechanisms in the antiviral immune response and innate immunity. Therefore, it would be of interest to summarize the data regarding the oxidative stress in severe COVID-19. In this review, we discuss the role of oxidative and antioxidant mechanisms in severe COVID-19 based on available studies. We also present the role of ROS and RNS in other viral infections in humans and in animal models. Although reactive oxygen and nitrogen species play an important role in the innate antiviral immune response, in some situations, they might have a deleterious effect, e.g., in some coronaviral infections. The understanding of the redox mechanisms in severe COVID-19 disease may have an impact on its treatment.

Bioavailability and Safety of Dihydroquercetin (Review)

Dihydroquercetin (DHQ) is a bioflavonoid with high antioxidant, capillary-protective, and anti-inflammatory activity. DHQ has previously been used for treating Middle East respiratory syndrome coronavirus (MERS-CoV) infection and is currently considered a potential regulator of oxidative stress as part of COVID-19 multipurpose therapy. DHQ has a high safety profile but low bioavailability that limits its use. Innovative techniques (liposomization, crystal engineering, etc.) can be used to increase its bioavailability.

Machine learning prediction of 3CLpro SARS-CoV-2 docking scores

Molecular docking results of two training sets containing 866 and 8,696 compounds were used to train three different machine learning (ML) approaches. Neural network approaches according to Keras and TensorFlow libraries and the gradient boosted decision trees approach of XGBoost were used with DScribe’s Smooth Overlap of Atomic Positions molecular descriptors. In addition, neural networks using the SchNetPack library and descriptors were used. The ML performance was tested on three different sets, including compounds for future organic synthesis. The final evaluation of the ML predicted docking scores was based on the ZINC in vivo set, from which 1,200 compounds were randomly selected with respect to their size. The results obtained showed a consistent ML prediction capability of docking scores, and even though compounds with more than 60 atoms were found slightly overestimated they remain valid for a subsequent evaluation of their drug repurposing suitability.

Evaluation of Xa inhibitors as potential inhibitors of the SARS-CoV-2 Mpro protease

Based on previous large-scale in silico screening several factor Xa inhibitors were proposed to potentially inhibit SARS-CoV-2 Mpro. In addition to their known anticoagulants activity this potential inhibition could have an additional therapeutic effect on patients with COVID-19 disease. In this study we examined the binding of the Apixaban, Betrixaban and Rivaroxaban to the SARS-CoV-2 Mpro with the use of the MicroScale Thermophoresis technique. Our results indicate that the experimentally measured binding affinity is weak and the therapeutic effect due to the SARS-CoV-2 Mpro inhibition is rather negligible.

Non-Specific Targets for Correction of Pneumonia Caused by Aerosols Containing Damaging Factors of Various Nature

This review article provides data on the current state of the pathogenesis peculiarities of body and lung inflammation (pneumonia) under the influence of damaging factors of various nature: infectious agents, chemical toxicants, as well as incorporated radionuclides, etc. The peculiarities of inflammation itself, as a typical pathological process, are considered. Information on mediators that induce the so-called pro-resolving phase of inflammation manifestations is given. Approaches to the neuroimmune correction of non-specific inflammation are substantiated. Data on the following alternative approaches to the correction of nonspecific inflammation are summarized: factors of the coagulation system, modulators of the integrated stress response, and modulators of sigma-1 receptors. Based on the data presented, general directions for the treatment of nonspecific pneumonia are formulated, including reflexogenic and anti-inflammatory therapy in combination with multimodal drugs, as well as pro-resolving therapy in combination with drugs that prevent fibrosis.

Mechanistic Insights into SARS-CoV-2 Main Protease Inhibition Reveals Hotspot Residues

The main protease (M^pro) is a key enzyme responsible for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication that causes the spread of the global pandemic novel coronavirus (nCOVID-19) infection. In the present study, multiple computational approaches such as docking, long-range molecular dynamics (MD) simulations, and binding free-energy (BFE) estimation techniques were employed to investigate the mechanistic basis of the high-affinity inhibitors─GC-376, Calpain XII, and Calpain II (hereafter Calpain as Cal) from the literature─binding to M^pro. Redocking GC-376 and docking Cal XII and Cal II inhibitors to M^pro were able to reproduce all crucial interactions like the X-ray conformation. Subsequently, the apo (ligand-free) and three holo (ligand-bound) complexes were subjected to extensive MD simulations, which revealed that the ligand binding did not alter the overall M^pro structural features, whereas the heatmap analysis showed that the residues located in subsites S1 and S2, the catalytic dyad, and the ⁴⁵TSEDMLN⁵¹ loop in M^pro exhibit a conformational deviation. Moreover, the BFE estimation method was used to elucidate the crucial thermodynamic properties, which revealed that Coulomb, solvation surface accessibility (Solv_SA), and lipophilic components contributed significant energies for complex formation. The decomposition of the total BFE to per-residue showed that H41, H163, M165, Q166, and Q189 residues contributed maximum energies. The overall results from the current investigation might be valuable for designing novel anti-M^pro inhibitors.

A computational biology approach for the identification of potential SARS-CoV-2 main protease inhibitors from natural essential oil compounds

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has fomented a climate of fear worldwide due to its rapidly spreading nature, and high mortality rate. The World Health Organization (WHO) declared it as a global pandemic on 11th March, 2020. Many endeavors have been made to find appropriate medications to restrain the SARS CoV-2 infection from spreading but there is no specific antiviral therapy to date. However, a computer-aided drug design approach can be an alternative to identify probable drug candidates within a short time. SARS-CoV-2 main protease is a proven drug target, and it plays a pivotal role in viral replication and transcription. Methods: In this study, we identified a total of 114 essential oil compounds as a feasible anti-SARS-CoV-2 agent from several online reservoirs. These compounds were screened by incorporating ADMET profiling, molecular docking, and 50 ns of molecular dynamics simulation to identify potential drug candidates against the SARS-CoV-2 main protease. The crystallized SARS-CoV-2 main protease structure was collected from the RCSB PDB database (PDB ID 6LU7). Results: According to the results of the ADMET study, none of the compounds have any side effects that could reduce their druglikeness or pharmacokinetic properties. Out of 114 compounds, we selected bisabololoxide B, eremanthin, and leptospermone as our top drug candidates based on their higher binding affinity scores, and strong interaction with the Cys 145-His 41 catalytic dyad. Finally, the molecular dynamics simulation was implemented to evaluate the structural stability of the ligand-receptor complex. MD simulations disclosed that all the hits showed conformational stability compared to the positive control α-ketoamide. Conclusions: Our study showed that the top three hits might work as potential anti-SARS-CoV-2 agents, which can pave the way for discovering new drugs, but for experimental validation, they will require more in vivo trials.

Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules

Recent explosive growth of 'make-on-demand' chemical libraries brought unprecedented opportunities but also significant challenges to the field of computer-aided drug discovery. To address this expansion of the accessible chemical universe, molecular docking needs to accurately rank billions of chemical structures, calling for the development of automated hit-selecting protocols to minimize human intervention and error. Herein, we report the development of an artificial intelligence-driven virtual screening pipeline that utilizes Deep Docking with Autodock GPU, Glide SP, FRED, ICM and QuickVina2 programs to screen 40 billion molecules against SARS-CoV-2 main protease (Mpro). This campaign returned a significant number of experimentally confirmed inhibitors of Mpro enzyme, and also enabled to benchmark the performance of twenty-eight hit-selecting strategies of various degrees of stringency and automation. These findings provide new starting scaffolds for hit-to-lead optimization campaigns against Mpro and encourage the development of fully automated end-to-end drug discovery protocols integrating machine learning and human expertise.

3CLpro and PLpro affinity, a docking study to fight COVID19 based on 900 compounds from PubChem and literature. Are there new drugs to be found?

The spread of a novel coronavirus SARS-CoV-2 and a resulting COVID19 disease in late 2019 has transformed into a worldwide pandemic and has effectively brought the world to a halt. Proteases 3CLpro and PLpro, responsible for proteolysis of new virions, represent vital inhibition targets for the COVID19 treatment. Herein, we report an in silico docking study of more than 860 COVID19-related compounds from the PubChem database. Molecular dynamic simulations were carried out to validate the conformation stability of compound-ligand complexes with best docking scores. The MM-PBSA approach was employed to calculate binding free energies. The comparison with ca. 50 previously reported potential SARS-CoV-2's proteases inhibitors show a number of new compounds with excellent binding affinities. Anti-inflammatory drugs Montelukast, Ebastine and Solumedrol, the anti-migraine drug Vazegepant or the anti-MRSA pro-drug TAK-599, among many others, all show remarkable affinities to 3CLpro and with known side effects present candidates for immediate clinical trials. This study reports thorough docking scores summary of COVID19-related compounds found in the PubChem database and illustrates the asset of computational screening methods in search for possible drug-like candidates. Several yet-untested compounds show affinities on par with reported inhibitors and warrant further attention. Furthermore, the submitted work provides readers with ADME data, ZINC and PubChem IDs, as well as docking scores of all studied compounds for further comparisons.

DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins

An unprecedented research effort has been undertaken in response to the ongoing COVID-19 pandemic. This has included the determination of hundreds of crystallographic structures of SARS-CoV-2 proteins, and numerous virtual screening projects searching large compound libraries for potential drug inhibitors. Unfortunately, these initiatives have had very limited success in producing effective inhibitors against SARS-CoV-2 proteins. A reason might be an often overlooked factor in these computational efforts: receptor flexibility. To address this issue we have implemented a computational tool for ensemble docking with SARS-CoV-2 proteins. We have extracted representative ensembles of protein conformations from the Protein Data Bank and from in silico molecular dynamics simulations. Twelve pre-computed ensembles of SARS-CoV-2 protein conformations have now been made available for ensemble docking via a user-friendly webserver called DINC-COVID (dinc-covid.kavrakilab.org). We have validated DINC-COVID using data on tested inhibitors of two SARS-CoV-2 proteins, obtaining good correlations between docking-derived binding energies and experimentally-determined binding affinities. Some of the best results have been obtained on a dataset of large ligands resolved via room temperature crystallography, and therefore capturing alternative receptor conformations. In addition, we have shown that the ensembles available in DINC-COVID capture different ranges of receptor flexibility, and that this diversity is useful in finding alternative binding modes of ligands. Overall, our work highlights the importance of accounting for receptor flexibility in docking studies, and provides a platform for the identification of new inhibitors against SARS-CoV-2 proteins.

Ligand-based quantitative structural assessments of SARS-CoV-2 3CLpro inhibitors: An analysis in light of structure-based multi-molecular modeling evidences

Due to COVID-19, the whole world is undergoing a devastating situation, but treatment with no such drug candidates still has been established exclusively. In that context, 69 diverse chemicals with potential SARS-CoV-2 3CL^pro inhibitory property were taken into consideration for building different internally and externally validated linear (SW-MLR and GA-MLR), non-linear (ANN and SVM) QSAR, and HQSAR models to identify important structural and physicochemical characters required for SARS-CoV-2 3CL^pro inhibition. Importantly, 2-oxopyrrolidinyl methyl and benzylester functions, and methylene (hydroxy) sulphonic acid warhead group, were crucial for retaining higher SARS-CoV-2 3CL^pro inhibition. These GA-MLR and HQSAR models were also applied to predict some already repurposed drugs. As per the GA-MLR model, curcumin, ribavirin, saquinavir, sepimostat, and remdesivir were found to be the potent ones, whereas according to the HQSAR model, lurasidone, saquinavir, lopinavir, elbasvir, and paritaprevir were the highly effective SARS-CoV-2 3CL^pro inhibitors. The binding modes of those repurposed drugs were also justified by the molecular docking, molecular dynamics (MD) simulation, and binding energy calculations conducted by several groups of researchers. This current work, therefore, may be able to find out important structural parameters to accelerate the COVID-19 drug discovery processes in the future.

Both Baicalein and Gallocatechin Gallate Effectively Inhibit SARS-CoV-2 Replication by Targeting Mpro and Sepsis in Mice

The emergence of severe acute syndrome coronavirus 2 (SARS-CoV-2) in December 2019 has led to the global COVID-19 pandemic. Although the symptoms of most COVID-19 patients are mild or self-curable, most of severe patients have sepsis caused by cytokine storms, which greatly increases the case fatality rate. Moreover, there is no effective drug that can limit the novel coronavirus thus far, so it is more needed to develop antiviral drugs for the SARS-CoV-2. In our research, we employed the techniques of molecular docking to screen 35 flavonoid compounds among which 29 compounds have Z-scores lower than − 6. Then, ( −)-gallocatechin gallate, ( +)-gallocatechin and baicalein were identified to have potent inhibitory activity against SARS-CoV-2 M^pro with IC₅₀ values of 5.774 ± 0.805 μM, 13.14 ± 2.081 μM and 5.158 ± 0.928 μM respectively by FRET assay. Molecular docking results also showed that ( −)-gallocatechin gallate, ( +)-gallocatechin and baicalein can non-covalently bind to M^pro through π-π stacking and hydrogen bonds in the Cys145 catalytic site. We further evaluated the effect of ( −)-gallocatechin gallate and baicalein on cytokine storms using a mouse model of sepsis. ( −)-Gallocatechin gallate and baicalein significantly reduced sepsis of mouse models on weight, murine sepsis score, and survival rate and reduced the inflammatory factor levels, such as TNF-α, IL-1α, IL-4, and IL-10. Overall, ( −)-gallocatechin gallate and baicalein show certain potential of treatment against COVID-19.

An integrated method for optimized identification of effective natural inhibitors against SARS-CoV-2 3CLpro

The current severe situation of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has not been reversed and posed great threats to global health. Therefore, there is an urgent need to find out effective antiviral drugs. The 3-chymotrypsin-like protease (3CLpro) in SARS-CoV-2 serve as a promising anti-virus target due to its essential role in the regulation of virus reproduction. Here, we report an improved integrated approach to identify effective 3CLpro inhibitors from effective Chinese herbal formulas. With this approach, we identified the 5 natural products (NPs) including narcissoside, kaempferol-3-O-gentiobioside, rutin, vicenin-2 and isoschaftoside as potential anti-SARS-CoV-2 candidates. Subsequent molecular dynamics simulation additionally revealed that these molecules can be tightly bound to 3CLpro and confirmed effectiveness against COVID-19. Moreover, kaempferol-3-o-gentiobioside, vicenin-2 and isoschaftoside were first reported to have SARS-CoV-2 3CLpro inhibitory activity. In summary, this optimized integrated strategy for drug screening can be utilized in the discovery of antiviral drugs to achieve rapid acquisition of drugs with specific effects on antiviral targets.

In Silico End-to-End Protein-Ligand Interaction Characterization Pipeline: The Case of SARS-CoV-2

SARS-CoV-2 triggered a worldwide pandemic disease, COVID-19, for which an effective treatment has not yet been settled. Among the most promising targets to fight this disease is SARS-CoV-2 main protease (Mpro), which has been extensively studied in the last few months. There is an urgency for developing effective computational protocols that can help us tackle these key viral proteins. Hence, we have put together a robust and thorough pipeline of in silico protein-ligand characterization methods to address one of the biggest biological problems currently plaguing our world. These methodologies were used to characterize the interaction of SARS-CoV-2 Mpro with an α-ketoamide inhibitor and include details on how to upload, visualize, and manage the three-dimensional structure of the complex and acquire high-quality figures for scientific publications using PyMOL (Protocol 1); perform homology modeling with MODELLER (Protocol 2); perform protein-ligand docking calculations using HADDOCK (Protocol 3); run a virtual screening protocol of a small compound database of SARS-CoV-2 candidate inhibitors with AutoDock 4 and AutoDock Vina (Protocol 4); and, finally, sample the conformational space at the atomic level between SARS-CoV-2 Mpro and the α-ketoamide inhibitor with Molecular Dynamics simulations using GROMACS (Protocol 5). Guidelines for careful data analysis and interpretation are also provided for each Protocol.

Investigation of Thiocarbamates as Potential Inhibitors of the SARS-CoV-2 Mpro

In the present study we tested, using the microscale thermophoresis technique, a small library of thionocarbamates, thiolocarbamates, sulfide and disulfide as potential lead compounds for SARS-CoV-2 Mpro drug design. The successfully identified binder is a representative of the thionocarbamates group with a high potential for future modifications aiming for higher affinity and solubility. The experimental analysis was extended by computational studies that show insufficient accuracy of the simplest and widely applied approaches and underline the necessity of applying more advanced methods to properly evaluate the affinity of potential SARS-CoV-2 Mpro binders.

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2

The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1– 5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.

Repurposing drug molecule against SARS-Cov-2 (COVID-19) through molecular docking and dynamics: a quick approach to pick FDA-approved drugs

A novel coronavirus known as severe acute respiratory syndrome is rapidly spreading worldwide. The international health authorities are putting all their efforts on quick diagnosis and placing the patients in quarantine. Although different vaccines have come for quick use as prophylactics, drug repurposing seems to be of paramount importance because of inefficient therapeutic options and clinical trial limitations. Here, we used structure-based drug designing approach to find and check the efficacy of the possible drug that can inhibit coronavirus main protease which is involved in polypeptide processing to functional protein. We performed virtual screening, molecular docking and molecular dynamics simulations of the FDA-approved drugs against the main protease of SARS-CoV-2. Using well-defined computational methods, we identified amprenavir, cefoperazone, riboflavin, diosmin, nadide and troxerutin approved for human therapeutic uses, as COVID-19 main protease inhibitors. These drugs bind to the SARS-CoV-2 main protease conserved residues of substrate-binding pocket and formed a remarkable number of non-covalent interactions. We have found diosmin as an inhibitor which binds covalently to the COVID-19 main protease. This study provides enough evidences for therapeutic use of these drugs in controlling COVID-19 after experimental validation and clinical demonstration.

Interactive Mechanism of Potential Inhibitors with Glycosyl for SARS-CoV-2 by Molecular Dynamics Simulation

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a type of Ribonucleic Acid (RNA) coronavirus and it has infected and killed many people around the world. It is reported that the receptor binding domain of the spike protein (S_RBD) of the SARS-CoV-2 virus is responsible for attachment to human angiotensin converting enzyme II (ACE2). Many researchers are attempting to search potential inhibitors for fighting SARS-CoV-2 infection using theoretical or experimental methods. In terms of experimental and theoretical research, Cefuroxime, Erythromycin, Lincomycin and Ofloxacin are the potential inhibitors of SARS-CoV-2. However, the interactive mechanism of the protein SARS-CoV-2 and the inhibitors are still elusive. Here, we investigated the interactions between S_RBD and the inhibitors using molecular dynamics (MD) simulations. Interestingly, we found that there are two binding sites of S_RBD for the four small molecules. In addition, our analysis also illustrated that hydrophobic and π-π stacking interactions play crucial roles in the interactions between S_RBD and the small molecules. In our work, we also found that small molecules with glycosyl group have more effect on the conformation of S_RBD than other inhibitors, and they are also potential inhibitors for the genetic variants of SARS-CoV-2. This study provides in silico-derived mechanistic insights into the interactions of S_RBD and inhibitors, which may provide new clues for fighting SARS-CoV-2 infection.

Comparative MD Study of Inhibitory Activity of Opaganib and Adamantane-Isothiourea Derivatives toward COVID-19 Main Protease Mpro

In this study, the inhibitory potency of four adamantly- isothiourea derivatives (compounds 1 [4-bromobenzyl (Z)-N'-(adamantan-1-yl)-4-phenylpiperazine-1-carbothioimidate], 2 [3,5-bis(trifluoromethyl)benzyl (Z)-N'-(adamantan-1-yl)-4-phenylpiperazine-1-carbothioimidate], 3 [4-bromobenzyl (Z)-N-(adamantan-1-yl)morpholine-4-carbothioimidate] and 4 [3,5-bis(trifluoromethyl)benzyl (Z)-N-(adamantan-1-yl)morpholine-4-carbothioimidate]) was evaluated against SARS-CoV-2 targeted proteins. The investigated compounds 1-4 possess a similar structure to opaganib, which is used in studies like a potential drug for COVID-19 treatment. Since examined adamantly-isothiourea derivatives (1-4) shown broad-spectrum of antibacterial activity and significant in vitro cytotoxic effects against five human tumor cell lines and shown similarity in structure with opaganib, it was of interest to study their inhibitory potency toward some SARS-CoV-2 proteins such as SARS-CoV-2 main protease Mpro and mutation of SARS-CoV-2 Spike (S) Protein D614G. The inhibitory potency of studied compounds is examined using molecular docking and molecular dynamic simulations. The results of molecular docking simulations indicate compound 1 as the most prominent candidate of inhibition of SARS-CoV-2 main protease Mpro (▵Gbind=11.24 kcal/mol), while almost the same inhibition potency of all studied compounds is exhibited toward D614G. Regarding the results obtained by molecular dynamic simulations, compounds 1 and 4 possess similar inhibitory potency toward SARS-CoV-2 main protease Mpro as opaganib (▵Gbind ≈ 40 kcal/mol).

Pathomechanisms, therapeutic targets and potent inhibitors of some beta-coronaviruses from bench-to-bedside

Since the emergence of their primitive strains, the complexity surrounding their pathogenesis, constant genetic mutation and translation are contributing factors to the scarcity of a successful vaccine for coronaviruses till moment. Although, the recent announcement of vaccine breakthrough for COVID-19 renews the hope, however, there remains a major challenge of accessibility to urgently match the rapid global therapeutic demand for curtailing the pandemic, thereby creating an impetus for further search. The reassessment of results from a stream of experiments is of enormous importance in identifying bona fide lead-like candidates to fulfil this quest. This review comprehensively highlights the common pathomechanisms and pharmacological targets of HCoV-OC43, SARS-CoV-1, MERS-CoV and SARS-CoV-2, and potent therapeutic potentials from basic and clinical experimental investigations. The implicated targets for the prevention and treatment include the viral proteases (M^pro, PL^pro, 3CL^pro), viral structural proteins (S- and N-proteins), non-structural proteins (nsp 3, 8, 10, 14, 16), accessory protein (ns12.9), viroporins (3a, E, 8a), enzymes (RdRp, TMPRSS2, ADP-ribosyltransferase, MTase, 2'-O-MTase, TATase, furin, cathepsin, deamidated human triosephosphate isomerase), kinases (MAPK, ERK, PI3K, mTOR, AKT, Abl2), interleukin-6 receptor (IL-6R) and the human host receptor, ACE2. Notably among the 109 overviewed inhibitors include quercetin, eriodictyol, baicalin, luteolin, melatonin, resveratrol and berberine from natural products, GC373, NP164 and HR2P-M2 from peptides, 5F9, m336 and MERS-GD27 from specific human antibodies, imatinib, remdesivir, ivermectin, chloroquine, hydroxychloroquine, nafamostat, interferon-β and HCQ from repurposing libraries, some iron chelators and traditional medicines. This review represents a model for further translational studies for effective anti-CoV therapeutic designs.

Determination of the interaction between the receptor binding domain of 2019-nCoV spike protein, TMPRSS2, cathepsin B and cathepsin L, and glycosidic and aglycon forms of some flavonols

The novel coronavirus (COVID-19, SARS-CoV-2) is a rapidly spreading disease with a high mortality. In this research, the interactions between specific flavonols and the 2019-nCoV receptor binding domain (RBD), transmembrane protease, serine 2 (TMPRSS2), and cathepsins (CatB and CatL) were analyzed. According to the relative binding capacity index (RBCI) calculated based on the free energy of binding and calculated inhibition constants, it was determined that robinin (ROB) and gossypetin (GOS) were the most effective flavonols on all targets. While the binding free energy of ROB with the spike glycoprotein RBD, TMPRSS2, CatB, and CatL were -5.02, -7.57, -10.10, and -6.11 kcal/mol, the values for GOS were -4.67, -5.24, -8.31, and -6.76, respectively. Furthermore, both compounds maintained their stability for at least 170 ns on respective targets in molecular dynamics simulations. The molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculations also corroborated these data. Considering Lipinski's rule of five, ROB and GOS exhibited 3 (MW>500, N or O>10, NH or OH>5), and 1 (NH or OH>5) violations, respectively. Neither ROB nor GOS showed AMES toxicity or hepatotoxicity. The LD50 of these compounds in rats were 2.482 and 2.527 mol/kg, respectively. Therefore, we conclude that these compounds could be considered as alternative therapeutic agents in the treatment of COVID-19. However, the possible inhibitory effects of these compounds on cytochromes (CYPs) should be verified by in vitro or in vivo tests and their adverse effects on cellular energy metabolism should be minimized by performing molecular modifications if necessary.

An overview of potential inhibitors targeting non-structural proteins 3 (PLpro and Mac1) and 5 (3CLpro/Mpro) of SARS-CoV-2

There is an urgent need to develop effective treatments for coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The rapid spread of SARS-CoV-2 has resulted in a global pandemic that has not only affected the daily lives of individuals but also had a significant impact on the global economy and public health. Although extensive research has been conducted to identify inhibitors targeting SARS-CoV-2, there are still no effective treatment strategies to combat COVID-19. SARS-CoV-2 comprises two important proteolytic enzymes, namely, the papain-like proteinase, located within non-structural protein 3 (nsp3), and nsp5, both of which cleave large replicase polypeptides into multiple fragments that are required for viral replication. Moreover, a domain within nsp3, known as the macrodomain (Mac1), also plays an important role in viral replication. Inhibition of their functions should be able to significantly interfere with the replication cycle of the virus, and therefore these key proteins may serve as potential therapeutic targets. The functions of the above viral targets and their corresponding inhibitors have been summarized in the current review. This review provides comprehensive updates of nsp3 and nsp5 inhibitor development and would help advance the discovery of novel anti-viral therapeutics against SARS-CoV-2.

Alzheimer's Disease and β-Secretase Inhibition: An Update With a Focus on Computer-Aided Inhibitor Design

INTRODUCTION

Alzheimer's disease (AD) is an intensifying neurodegenerative illness due to its irreversible nature. Identification of β-site amyloid precursor protein (APP) cleaving enzyme1 (BACE1) has been a significant medicinal focus towards AD treatment, and this has opened ground for several investigations. Despite the numerous works in this direction, no BACE1 inhibitor has made it to the final approval stage as an anti-AD drug.

METHOD

We provide an introductory background of the subject with a general overview of the pathogenesis of AD. The review features BACE1 inhibitor design and development with a focus on some clinical trials and discontinued drugs. Using the topical keywords BACE1, inhibitor design, and computational/theoretical study in the Web of Science and Scopus database, we retrieved over 49 relevant articles. The search years are from 2010 and 2020, with analysis conducted from May 2020 to March 2021.

RESULTS AND DISCUSSION

Researchers have employed computational methodologies to unravel potential BACE1 inhibitors with a significant outcome. The most used computer-aided approach in BACE1 inhibitor design and binding/interaction studies are pharmacophore development, quantitative structure-activity relationship (QSAR), virtual screening, docking, and molecular dynamics (MD) simulations. These methods, plus more advanced ones including quantum mechanics/molecular mechanics (QM/MM) and QM, have proven substantial in the computational framework for BACE1 inhibitor design. Computational chemists have embraced the incorporation of in vitro assay to provide insight into the inhibition performance of identified molecules with potential inhibition towards BACE1. Significant IC₅₀ values up to 50 nM, better than clinical trial compounds, are available in the literature.

CONCLUSION

The continuous failure of potent BACE1 inhibitors at clinical trials is attracting many queries prompting researchers to investigate newer concepts necessary for effective inhibitor design. The considered properties for efficient BACE1 inhibitor design seem enormous and require thorough scrutiny. Lately, researchers noticed that besides appreciable binding affinity and blood-brain barrier (BBB) permeation, BACE1 inhibitor must show low or no affinity for permeability-glycoprotein. Computational modeling methods have profound applications in drug discovery strategy. With the volume of recent in silico studies on BACE1 inhibition, the prospect of identifying potent molecules that would reach the approved level is feasible. Investigators should try pushing many of the identified BACE1 compounds with significant anti-AD properties to preclinical and clinical trial stages. We also advise computational research on allosteric inhibitor design, exosite modeling, and multisite inhibition of BACE1. These alternatives might be a solution to BACE1 drug discovery in AD therapy.

Impaired immune response mediated by prostaglandin E2 promotes severe COVID-19 disease

The SARS-CoV-2 coronavirus has led to a pandemic with millions of people affected. The present study finds that risk-factors for severe COVID-19 disease courses, i.e. male sex, older age and sedentary life style are associated with higher prostaglandin E2 (PGE2) serum levels in blood samples from unaffected subjects. In COVID-19 patients, PGE2 blood levels are markedly elevated and correlate positively with disease severity. SARS-CoV-2 induces PGE2 generation and secretion in infected lung epithelial cells by upregulating cyclo-oxygenase (COX)-2 and reducing the PG-degrading enzyme 15-hydroxyprostaglandin-dehydrogenase. Also living human precision cut lung slices (PCLS) infected with SARS-CoV-2 display upregulated COX-2. Regular exercise in aged individuals lowers PGE2 serum levels, which leads to increased Paired-Box-Protein-Pax-5 (PAX5) expression, a master regulator of B-cell survival, proliferation and differentiation also towards long lived memory B-cells, in human pre-B-cell lines. Moreover, PGE2 levels in serum of COVID-19 patients lowers the expression of PAX5 in human pre-B-cell lines. The PGE2 inhibitor Taxifolin reduces SARS-CoV-2-induced PGE2 production. In conclusion, SARS-CoV-2, male sex, old age, and sedentary life style increase PGE2 levels, which may reduce the early anti-viral defense as well as the development of immunity promoting severe disease courses and multiple infections. Regular exercise and Taxifolin treatment may reduce these risks and prevent severe disease courses.

Discovery of potent Covid-19 main protease inhibitors using integrated drug-repurposing strategy

The emergence and rapid spreading of novel SARS-CoV-2 across the globe represent an imminent threat to public health. Novel antiviral therapies are urgently needed to overcome this pandemic. Given the significant role of the main protease of Covid-19 for virus replication, we performed a drug-repurposing study using the recently deposited main protease structure, 6LU7. For instance, pharmacophore- and e-pharmacophore-based hypotheses such as AARRH and AARR, respectively, were developed using available small molecule inhibitors and utilized in the screening of the DrugBank repository. Further, a hierarchical docking protocol was implemented with the support of the Glide algorithm. The resultant compounds were then examined for their binding free energy against the main protease of Covid-19 by means of the Prime-MM/GBSA algorithm. Most importantly, the machine learning-based AutoQSAR algorithm was used to predict the antiviral activities of resultant compounds. The hit molecules were also examined for their drug-likeness and toxicity parameters through the QikProp algorithm. Finally, the hit compounds activity against the main protease was validated using molecular dynamics simulation studies. Overall, the present analysis yielded two potential inhibitors (DB02986 and DB08573) that are predicted to bind with the main protease of Covid-19 better than currently used drug molecules such as N3 (cocrystallized native ligand), lopinavir, and ritonavir.

Computational guided identification of a citrus flavonoid as potential inhibitor of SARS-CoV-2 main protease

Although vaccine development is being undertaken at a breakneck speed, there is currently no effective antiviral drug for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19. Therefore, the present study aims to explore the possibilities offered by naturally available and abundant flavonoid compounds, as a prospective antiviral drug to combat the virus. A library of 44 citrus flavonoids was screened against the highly conserved Main Protease (Mpro) of SARS-CoV-2 using molecular docking. The compounds which showed better CDocker energy than the co-crystal inhibitor of Mpro were further revalidated by flexible docking within the active site; followed by assessment of drug likeness and toxicity parameters. The non-toxic compounds were further subjected to molecular dynamics simulation and predicted activity (IC50) using 3D-QSAR analysis. Subsequently, hydrogen bonds and dehydration analysis of the best compound were performed to assess the binding affinity to Mpro. It was observed that out of the 44 citrus flavonoids, five compounds showed lower binding energy with Mpro than the co-crystal ligand. Moreover, these compounds also formed H-bonds with two important catalytic residues His41 and Cys145 of the active sites of Mpro. Three compounds which passed the drug likeness filter showed stable conformation during MD simulations. Among these, the lowest predicted IC50 value was observed for Taxifolin. Therefore, this study suggests that Taxifolin, could be a potential inhibitor against SARS-CoV-2 main protease and can be further analysed by in vitro and in vivo experiments for management of the ongoing pandemic.