Suggestion of active 3-chymotrypsin like protease (3CLPro) inhibitors as potential anti-SARS-CoV-2 agents using predictive QSAR model based on the combination of ALASSO with an ANN model.

The novel severe acute respiratory syndrome coronavirus (SARS CoV-2) was introduced as an epidemic in 2019 and had millions of deaths worldwide. Given the importance of this disease, the recommendation and design of new active compounds are crucial. 3-chymotrypsin-like protease (3 CLpro) inhibitors have been identified as potent compounds for treating SARS-CoV-2 disease. So, the design of new 3 CLpro inhibitors was proposed using a quantitative structure-activity relationship (QSAR) study. In this context, a powerful adaptive least absolute shrinkage and selection operator (ALASSO) penalized variable selection method with inherent advantages coupled with a nonlinear artificial neural network (ANN) modelling method were used to provide a QSAR model with high interpretability and predictability. After evaluating the accuracy and validity of the developed ALASSO-ANN model, new compounds were proposed using effective descriptors, and the biological activity of the new compounds was predicted. Ligand-receptor (LR) interactions were also performed to confirm the interaction strength of the compounds using molecular docking (MD) study. The pharmacokinetics properties and calculated Lipinski's rule of five were applied to all proposed compounds. Due to the ease of synthesis of these suggested new compounds, it is expected that they have acceptable pharmacological properties.

ADMET profile and virtual screening of plant and microbial natural metabolites as SARS-CoV-2 S1 glycoprotein receptor binding domain and main protease inhibitors.

In an attempt to search for selective inhibitors against the SARS-CoV-2 which caused devastating of lives and livelihoods across the globe, 415 natural metabolites isolated from several plants, fungi and bacteria, belonging to different classes, were investigated. The drug metabolism and safety profiles were computed in silico and the results showed seven compounds namely fusaric acid, jasmonic acid, jasmonic acid methyl ester, putaminoxin, putaminoxin B and D, and stagonolide K were predicted to having considerable absorption, metabolism, distribution and excretion parameters (ADME) and safety indices. Molecular docking against the receptor binding domain (RBD) of spike glycoprotein (S1) and the main protease (Mpro) exposed the compounds having better binding affinity to main protease as compared to the S1 receptor binding domain. The docking results were compared to an antiviral drug penciclovir reportedly of clinical significance in treating the SARS-CoV-2 infected patients. The results demonstrated the test compounds jasmonic acid, putaminoxins B and D bound to the HIS-CYS catalytic dyad as well as to other residues within the MPro active site with much greater affinity than penciclovir. The findings of the study suggest that these compounds could be explored as potential SARS-CoV-2 inhibitors, and could further be combined with the experimental investigations to develop effective therapeutics to deal with the present pandemic.

Rapid structure-based identification of potential SARS-CoV-2 main protease inhibitors.

The COVID-19 outbreak has thrown the world into an unprecedented crisis. It has posed a challenge to scientists around the globe who are working tirelessly to combat this pandemic. We herein report a set of molecules that may serve as possible inhibitors of the SARS-CoV-2 main protease. To identify these molecules, we followed a combinatorial structure-based strategy, which includes high-throughput virtual screening, molecular docking and WaterMap calculations. The study was carried out using Protein Data Bank structures 5R82 and 6Y2G. DrugBank, Enamine, Natural product and Specs databases, along with a few known antiviral drugs, were used for the screening. WaterMap analysis aided in the recognition of high-potential molecules that can efficiently displace binding-site waters. This study may help the discovery and development of antiviral drugs against SARS-CoV-2.

Systematic Search for SARS-CoV-2 Main Protease Inhibitors for Drug Repurposing: Ethacrynic Acid as a Potential Drug.

In 2019 an outbreak occurred which resulted in a global pandemic. The causative agent has been identified in a virus belonging to theCoronaviridae family, similar to the agent of SARS, referred to as SARS-CoV-2. This epidemic spread rapidly globally with high morbidity and mortality. Although vaccine development is at a very advanced stage, there are currently no truly effective antiviral drugs to treat SARS-CoV-2 infection. In this study we present systematic and integrative antiviral drug repurposing effort aimed at identifying, among the drugs already authorized for clinical use, some active inhibitors of the SARS-CoV-2 main protease. The most important result of this analysis is the demonstration that ethacrynic acid, a powerful diuretic, is revealed to be an effective inhibitor of SARS-CoV-2 main protease. Even with all the necessary cautions, given the particular nature of this drug, these data can be the starting point for the development of an effective therapeutic strategy against SARS-CoV-2.

First structure-activity relationship analysis of SARS-CoV-2 virus main protease (Mpro) inhibitors: an endeavor on COVID-19 drug discovery.

Main protease (Mpro) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) intervenes in the replication and transcription processes of the virus. Hence, it is a lucrative target for anti-viral drug development. In this study, molecular modeling analyses were performed on the structure activity data of recently reported diverse SARS-CoV-2 Mpro inhibitors to understand the structural requirements for higher inhibitory activity. The classification-based quantitative structure-activity relationship (QSAR) models were generated between SARS-CoV-2 Mpro inhibitory activities and different descriptors. Identification of structural fingerprints to increase or decrease in the inhibitory activity was mapped for possible inclusion/exclusion of these fingerprints in the lead optimization process. Challenges in ADME properties of protease inhibitors were also discussed to overcome the problems of oral bioavailability. Further, depending on the modeling results, we have proposed novel as well as potent SARS-CoV-2 Mpro inhibitors.

Structure-Based Screening to Discover New Inhibitors for Papain-like Proteinase of SARS-CoV-2: An In Silico Study.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) expresses a multifunctional papain-like proteinase (PLpro), which mediates the processing of the viral replicase polyprotein. Inhibition of PLpro has been shown to suppress the viral replication. This study aimed to explore new anti-PLpro candidates by applying virtual screening based on GRL0617, a known PLpro inhibitor of SARS coronavirus (SARS-CoV). The three-dimensional (3D) structure of SARS-CoV-2 PLpro was built by homology modeling, using SARS-CoV PLpro as the template. The model was refined and studied through molecular dynamic simulation. AutoDock Vina was then used to perform virtual screening where 50 chemicals with at least 65% similarity to GRL0617 were docked with the optimized SARS-CoV-2 PLpro. In this screening, 5-(aminomethyl)-2-methyl-N-[(1R)-1-naphthalen-1-ylethyl]benzamide outperformed GRL0617 in terms of binding affinity (-9.7 kcal/mol). Furthermore, 2-(4-fluorobenzyl)-5-nitro-1H-isoindole-1,3(2H)-dione (previously introduced as an inhibitor of cyclooxygenase-2), 3-nitro-N-[(1r)-1-phenylethyl]-5-(trifluoromethyl)benzamide (inhibitor against Mycobacterium tuberculosis), as well as the recently introduced SARS-CoV-2 PLpro inhibitor 5-acetamido-2-methyl-N-[(1S)-1-naphthalen-1-ylethyl]benzamide showed promising affinity for the viral proteinase. All of the identified compounds demonstrated an acceptable pharmacokinetic profile. In conclusion, our findings represent rediscovery of analgesic, anti-inflammatory, antibacterial, or antiviral drugs as promising pharmaceutical candidates against the ongoing coronavirus.

An in-silico evaluation of COVID-19 main protease with clinically approved drugs.

A novel strain of coronavirus, namely, SARS-CoV-2 identified in Wuhan city of China in December 2019, continues to spread at a rapid rate worldwide. There are no specific therapies available and investigations regarding the treatment of this disease are still lacking. In order to identify a novel potent inhibitor, we performed blind docking studies on the main virus protease Mpro with eight approved drugs belonging to four pharmacological classes such as: anti-malarial, anti-bacterial, anti-infective and anti-histamine. Among the eight studied compounds, Lymecycline and Mizolastine appear as potential inhibitors of this protease. When docked against Mpro crystal structure, these two compounds revealed a minimum binding energy of -8.87 and -8.71 kcal/mol with 168 and 256 binding modes detected in the binding substrate pocket, respectively. Further, to study the interaction mechanism and conformational dynamics of protein-ligand complexes, Molecular dynamic simulation and MM/PBSA binding free calculations were performed. Our results showed that both Lymecycline and Mizolastine bind in the active site. And exhibited good binding affinities towards target protein. Moreover, the ADMET analysis also indicated drug-likeness properties. Thus it is suggested that the identified compounds can inhibit Chymotrypsin-like protease (3CLpro) of SARS-CoV-2.

Quantum chemical insight into molecular structure, NBO analysis of the hydrogen-bonded interactions, spectroscopic (FT-IR, FT-Raman), drug likeness and molecular docking of the novel anti COVID-19 molecule 2-[(4,6-diaminopyrimidin-2-yl)sulfanyl]-N-(4-fluorophenyl)acetamide - dimer.

Novel antiviral active molecule 2- [(4,6-diaminopyrimidin-2-yl)sulfanyl]-N-(4-fluoro- phenyl)acetamide has been synthesised and characterized by FT-IR and FT-Raman spectra. The equilibrium geometry, natural bond orbital calculations and vibrational assignments have been carried out using density functional B3LYP method with the 6-311G++(d,p) basis set. The complete vibrational assignments for all the vibrational modes have been supported by normal coordinate analysis, force constants and potential energy distributions. A detailed analysis of the intermolecular interactions has been performed based on the Hirshfeld surfaces. Drug likeness has been carried out based on Lipinski's rule and the absorption, distribution, metabolism, excretion and toxicity of the title molecule has been calculated. Antiviral potency of 2- [(4,6-diaminopyrimidin-2-yl)sulfanyl]-N-(4-fluoro-phenyl) acetamide has been investigated by docking against SARS-CoV-2 protein. The optimized geometry shows near-planarity between the phenyl ring and the pyrimidine ring. Differences in the geometries due to the substitution of the most electronegative fluorine atom and intermolecular contacts due to amino pyrimidine were analyzed. NBO analysis reveals the formation of two strong stable hydrogen bonded N-H···N intermolecular interactions and weak intramolecular interactions C-H···O and N-H···O. The Hirshfeld surfaces and consequently the 2D-fingerprint confirm the nature of intermolecular interactions and their quantitative contributions towards the crystal packing. The red shift in N-H stretching frequency exposed from IR substantiate the formation of N-H···N intermolecular hydrogen bond. Drug likeness and absorption, distribution, metabolism, excretion and toxicity properties analysis gives an idea about the pharmacokinetic properties of the title molecule. The binding energy -8.7 kcal/mol of the nonbonding interaction present a clear view that 2- [(4,6-diaminopyrimidin-2-yl)sulfanyl]-N-(4-fluoro- phenyl) acetamide can irreversibly interact with SARS-CoV-2 protease.

Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation.

BACKGROUND: The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases have overwhelmed health and public health services. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have extended their major role in tracking disease patterns, and in identifying possible treatments. OBJECTIVE: This study aims to identify potential COVID-19 protease inhibitors through shape-based Machine Learning assisted by Molecular Docking and Molecular Dynamics simulations. METHODS: 31 Repurposed compounds have been selected targeting the main coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from the 3D shape to the pharmacophoric features of their seed compound. Ligand-Receptor Docking was performed with Optimized Potential for Liquid Simulations (OPLS) algorithms to identify highaffinity compounds from the list of selected candidates for 6LU7, which were subjected to Molecular Dynamic Simulations followed by ADMET studies and other analyses. RESULTS: Shape-based Machine learning reported remdesivir, valrubicin, aprepitant, and fulvestrant as the best therapeutic agents with the highest affinity for the target protein. Among the best shape-based compounds, a novel compound identified was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorv-EMBS'. Further, toxicity analysis showed nCorv-EMBS to be suitable for further consideration as the main protease inhibitor in COVID-19. CONCLUSION: Effective ACE-II, GAK, AAK1, and protease 3C blockers can serve as a novel therapeutic approach to block the binding and attachment of the main COVID-19 protease (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 receptors in the lung. The novel compound nCorv- EMBS herein proposed stands as a promising inhibitor to be evaluated further for COVID-19 treatment.

Class A G Protein-Coupled Receptor Antagonist Famotidine as a Therapeutic Alternative Against SARS-CoV2: An In Silico Analysis.

The pandemic associated with Severe Acute Respiratory Syndrome Coronavirus type 2 (SARS-CoV2) and its disease named COVID-19 challenged the scientific community to discover effective therapeutic solutions in a short period. Repurposing existing drugs is one viable approach that emphasizes speed during these urgent times. Famotidine, a class A G protein-coupled receptor antagonist used for the treatment of gastroesophageal reflux was recently identified in an in silico screening. Additionally, a recent retrospective clinical report showed that the treatment with famotidine provided a good outcome in patients infected with SARS-CoV2. A clinical trial testing effectiveness of famotidine in combination with hydroxychloroquine is currently ongoing in the United States (US). In the 1990s, famotidine was described as an antiviral agent against human immunodeficiency virus (HIV). Interestingly, some HIV protease inhibitors are presently being used against SARS-CoV2. However, it is not clear if famotidine could be effective against SARS-CoV2. Thus, by using a computational analysis, we aimed to examine if the antiviral effect of famotidine could be related to the inhibition of proteases involved in the virus replication. Our results showed that famotidine could interact within the catalytic site of the three proteases associated with SARS-CoV2 replication. However, weak binding affinity of famotidine to these proteases suggests that a successful famotidine therapy could likely be achieved only in combination with other antiviral drugs. Finally, analysis of famotidine's pharmacokinetic parameters indicated that its effect against SARS-CoV2 infection could be reached only upon intravenous administration. This work will contribute to the pharmacological knowledge of famotidine as an antiviral agent against SARS-CoV2.

Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors.

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) is a global health emergency. An attractive drug target among coronaviruses is the main protease (Mpro, also called 3CLpro) because of its essential role in processing the polyproteins that are translated from the viral RNA. We report the x-ray structures of the unliganded SARS-CoV-2 Mpro and its complex with an α-ketoamide inhibitor. This was derived from a previously designed inhibitor but with the P3-P2 amide bond incorporated into a pyridone ring to enhance the half-life of the compound in plasma. On the basis of the unliganded structure, we developed the lead compound into a potent inhibitor of the SARS-CoV-2 Mpro The pharmacokinetic characterization of the optimized inhibitor reveals a pronounced lung tropism and suitability for administration by the inhalative route.