In Silico Screening of the DrugBank Database to Search for Possible Drugs against SARS-CoV-2

Coronavirus desease 2019 (COVID-19) is responsible for more than 1.80 M deaths worldwide. A Quantitative Structure-Activity Relationships (QSAR) model is developed based on experimental pIC50 values reported for a structurally diverse dataset. A robust model with only five descriptors is found, with values of R2 = 0.897, Q2LOO = 0.854, and Q2ext = 0.876 and complying with all the parameters established in the validation Tropsha’s test. The analysis of the applicability domain (AD) reveals coverage of about 90% for the external test set. Docking and molecular dynamic analysis are performed on the three most relevant biological targets for SARS-CoV-2: main protease, papain-like protease, and RNA-dependent RNA polymerase. A screening of the DrugBank database is executed, predicting the pIC50 value of 6664 drugs, which are IN the AD of the model (coverage = 79%). Fifty-seven possible potent anti-COVID-19 candidates with pIC50 values > 6.6 are identified, and based on a pharmacophore modelling analysis, four compounds of this set can be suggested as potent candidates to be potential inhibitors of SARS-CoV-2. Finally, the biological activity of the compounds was related to the frontier molecular orbitals shapes.

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Molecular docking, pharmacophore modelling, MD simulation and in silico ADMET study reveals bitter cola constituents as potential inhibitors of SARS-CoV-2 main protease and RNA dependent-RNA polymerase

Journal of Biomolecular Structure and Dynamics ◽

10.1080/07391102.2021.2024883 ◽

2022 ◽

pp. 1-16

Author(s):

Abimbola Peter Oluyori ◽

Bolatito Eunice Olanipekun ◽

Oluyomi Stephen Adeyemi ◽

Godshelp Osas Egharevba ◽

Abayomi Emmanuel Adegboyega ◽

...

Keyword(s):

Molecular Docking ◽

Rna Polymerase ◽

In Silico ◽

Md Simulation ◽

Rna Dependent Rna Polymerase ◽

Pharmacophore Modelling ◽

Main Protease ◽

In Silico Admet ◽

Potential Inhibitors

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An Integrated Computational and Experimental Approach to Identifying Inhibitors for SARS-CoV-2 3CL Protease

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2021.661424 ◽

2021 ◽

Vol 8 ◽

Author(s):

Tianhua Zhai ◽

Fangyuan Zhang ◽

Shozeb Haider ◽

Daniel Kraut ◽

Zuyi Huang

Keyword(s):

Small Molecule ◽

Small Molecule Inhibitors ◽

Qsar Model ◽

Quantitative Structure Activity Relationship ◽

Ligand Docking ◽

Enzyme Activity Assay ◽

Computational Docking ◽

Main Protease ◽

Docking Approach ◽

Potential Inhibitors

The newly evolved SARS-CoV-2 has caused the COVID-19 pandemic, and the SARS-CoV-2 main protease 3CLpro is essential for the rapid replication of the virus. Inhibiting this protease may open an alternative avenue toward therapeutic intervention. In this work, a computational docking approach was developed to identify potential small-molecule inhibitors for SARS-CoV-2 3CLpro. Totally 288 potential hits were identified from a half-million bioactive chemicals via a protein-ligand docking protocol. To further evaluate the docking results, a quantitative structure activity relationship (QSAR) model of 3CLpro inhibitors was developed based on existing small molecule inhibitors of the 3CLproSARS– CoV– 1 and their corresponding IC50 data. The QSAR model assesses the physicochemical properties of identified compounds and estimates their inhibitory effects on 3CLproSARS– CoV– 2. Seventy-one potential inhibitors of 3CLpro were selected through these computational approaches and further evaluated via an enzyme activity assay. The results show that two chemicals, i.e., 5-((1-([1,1′-biphenyl]-4-yl)-2,5-dimethyl-1H-pyrrol-3-yl)methylene)pyrimidine-2,4,6(1H,3H,5H)-trione and N-(4-((3-(4-chlorophenylsulfonamido)quinoxalin-2-yl)amino)phenyl)acetamide, effectively inhibited 3CLpro SARS-CoV-2 with IC50’s of 19 ± 3 μM and 38 ± 3 μM, respectively. The compounds contain two basic structures, pyrimidinetrione and quinoxaline, which were newly found in 3CLpro inhibitor structures and are of high interest for lead optimization. The findings from this work, such as 3CLpro inhibitor candidates and the QSAR model, will be helpful to accelerate the discovery of inhibitors for related coronaviruses that may carry proteases with similar structures to SARS-CoV-2 3CLpro.

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Drugs Repurposing Using QSAR, Docking and Molecular Dynamics for Possible Inhibitors of the SARS-CoV-2 Mpro Protease

Molecules ◽

10.3390/molecules25215172 ◽

2020 ◽

Vol 25 (21) ◽

pp. 5172

Author(s):

Eduardo Tejera ◽

Cristian R. Munteanu ◽

Andrés López-Cortés ◽

Alejandro Cabrera-Andrade ◽

Yunierkis Pérez-Castillo

Keyword(s):

Molecular Dynamics ◽

Learning Strategy ◽

Drug Repurposing ◽

Qsar Model ◽

Quantitative Structure Activity Relationship ◽

Free Energies ◽

Main Protease ◽

Potential Inhibitors ◽

Dynamics Analyses ◽

Free Energy Of Binding

Wuhan, China was the epicenter of the first zoonotic transmission of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) in December 2019 and it is the causative agent of the novel human coronavirus disease 2019 (COVID-19). Almost from the beginning of the COVID-19 outbreak several attempts were made to predict possible drugs capable of inhibiting the virus replication. In the present work a drug repurposing study is performed to identify potential SARS-CoV-2 protease inhibitors. We created a Quantitative Structure–Activity Relationship (QSAR) model based on a machine learning strategy using hundreds of inhibitor molecules of the main protease (Mpro) of the SARS-CoV coronavirus. The QSAR model was used for virtual screening of a large list of drugs from the DrugBank database. The best 20 candidates were then evaluated in-silico against the Mpro of SARS-CoV-2 by using docking and molecular dynamics analyses. Docking was done by using the Gold software, and the free energies of binding were predicted with the MM-PBSA method as implemented in AMBER. Our results indicate that levothyroxine, amobarbital and ABP-700 are the best potential inhibitors of the SARS-CoV-2 virus through their binding to the Mpro enzyme. Five other compounds showed also a negative but small free energy of binding: nikethamide, nifurtimox, rebimastat, apomine and rebastinib.

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Drug repurposing and polypharmacology to fight SARS-CoV-2 through the inhibition of the main protease

10.21203/rs.3.rs-27222/v1 ◽

2020 ◽

Author(s):

Luca Pinzi ◽

Annachiara Tinivella ◽

Fabiana Caporuscio ◽

Giulio Rastelli

Keyword(s):

Crystal Structure ◽

Therapeutic Indication ◽

Severe Disease ◽

Drug Repurposing ◽

Drug Candidates ◽

Main Protease ◽

The World ◽

Novel Coronavirus ◽

Potential Inhibitors ◽

Drugbank Database

Abstract There is an urgent need to develop therapeutic options to fight the outbreak of a novel Coronavirus (SARS-CoV-2), which causes a disease named COVID-19 and is spreading rapidly around the world. Drug repurposing can significantly accelerate the identification of drug candidates suitable for clinical evaluation. Moreover, drugs with polypharmacological effects may increase antiviral activity and/or counteract severe disease complications concurrently affecting COVID-19 patients. Herein, we present the results of a computational drug repurposing campaign in search for potential inhibitors of the main protease of SARS-CoV-2. To this aim, the complete DrugBank database, including drug metabolites, was docked to the recently solved crystal structure of the SARS-CoV-2 Mpro and the results were post-processed by using our in-house tool BEAR. Here we report 32 promising drugs that could be repositioned to fight SARS-CoV-2. Some of them have already entered clinical trials against COVID-19, thus supporting our results, but the vast majority of the selected compounds is new and has never been considered before. For each repurposed compound its therapeutic relevance and the potential beneficial polypharmacological effects that may arise thanks to its original therapeutic indication are thoroughly discussed.

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Identification of Main Protease of Coronavirus SARS-CoV-2 (Mpro)Inhibitors from Melissa officinalis

Current Drug Discovery Technologies ◽

10.2174/1570163817999200918103705 ◽

2020 ◽

Vol 17 ◽

Cited By ~ 1

Author(s):

Olusola O. Elekofehinti ◽

Opeyemi Iwaloye ◽

Courage D. Famusiwa ◽

Olanrewaju Akinseye ◽

Joao B. T. Rocha

Keyword(s):

Qsar Model ◽

Computational Approach ◽

Melissa Officinalis ◽

Lead Compounds ◽

Main Protease ◽

The World ◽

Recent Outbreak ◽

Global Concern ◽

Catalytic Dyad ◽

Potential Therapeutic Intervention

Background: he recent outbreak of Coronavirus SARS-CoV-2 (Covid-19) which has rapidly spread around the world in about three months with tens of thousands of deaths recorded so far is a global concern. An urgent need for potential therapeutic intervention is of necessity. Mpro is an attractive druggable target for the development of anti-COVID-19 drug development. Compounds previously characterized from Melissa officinalis were queried against main protease of coronavirus SARS-CoV-2 using computational approach. Results: Melitric acid A and salvanolic acid A had higher affinity than lopinavir and ivermectin using both AutodockVina and XP docking algorithms. The computational approach was employed in the generation of QSAR model using automated QSAR, and in the docking of ligands from Melissa officinalis with SARS-CoV-2 Mpro inhibitors. The best model obtained was KPLS_Radial_28 (R2 = 0.8548 and Q2=0.6474, and was used in predicting the bioactivity of the lead compounds. Molecular mechanics based MM-GBSA confirmed salvanolic acid A as the compound with the highest free energy and predicted bioactivity of 4.777; it interacted with His-41 of the catalytic dyad (Cys145-His41) of SARS-CoV-2 main protease (Mpro), as this may hinder the cutting of inactive viral protein into active ones capable of replication. Conclusion: Salvanolic acid A can be further evaluated as potential Mpro inhibitor.

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Discovery of New Phosphoinositide 3-kinase Delta (PI3Kδ) Inhibitors via Virtual Screening using Crystallography-derived Pharmacophore Modelling and QSAR Analysis

Medicinal Chemistry ◽

10.2174/1573406415666190222125333 ◽

2019 ◽

Vol 15 (6) ◽

pp. 588-601 ◽

Cited By ~ 1

Author(s):

Mahmoud A. Al-Sha'er ◽

Rua'a A. Al-Aqtash ◽

Mutasem O. Taha

Keyword(s):

Virtual Screening ◽

Inflammatory Diseases ◽

Qsar Model ◽

Qsar Analysis ◽

Pharmacophore Modelling ◽

Physicochemical Descriptors ◽

Qsar Modelling ◽

Ic50 Values ◽

Phosphoinositide 3 Kinase ◽

Pharmacophore Models

Background: PI3Kδ is predominantly expressed in hematopoietic cells and participates in the activation of leukocytes. PI3Kδ inhibition is a promising approach for treating inflammatory diseases and leukocyte malignancies. Accordingly, we decided to model PI3Kδ binding. Methods: Seventeen PI3Kδ crystallographic complexes were used to extract 94 pharmacophore models. QSAR modelling was subsequently used to select the superior pharmacophore(s) that best explain bioactivity variation within a list of 79 diverse inhibitors (i.e., upon combination with other physicochemical descriptors). Results: The best QSAR model (r2 = 0.71, r2 LOO = 0.70, r2 press against external testing list of 15 compounds = 0.80) included a single crystallographic pharmacophore of optimal explanatory qualities. The resulting pharmacophore and QSAR model were used to screen the National Cancer Institute (NCI) database for new PI3Kδ inhibitors. Two hits showed low micromolar IC50 values. Conclusion: Crystallography-based pharmacophores were successfully combined with QSAR analysis for the identification of novel PI3Kδ inhibitors.

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Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis

Pharmaceuticals ◽

10.3390/ph14040357 ◽

2021 ◽

Vol 14 (4) ◽

pp. 357

Author(s):

Magdi E. A. Zaki ◽

Sami A. Al-Hussain ◽

Vijay H. Masand ◽

Siddhartha Akasapu ◽

Sumit O. Bajaj ◽

...

Keyword(s):

Molecular Dynamics ◽

Molecular Docking ◽

Virtual Screening ◽

Simulation Analysis ◽

Qsar Model ◽

Quantitative Structure Activity Relationship ◽

Dynamics Simulation ◽

Multilinear Regression ◽

Qsar Analysis ◽

Main Protease

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

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