Combined Deep Learning and Molecular Docking Simulations Approach Identifies Potentially Effective FDA Approved Drugs for Repurposing Against SARS-CoV-2

Anwar, Muhammad Umer; Adnan, Farjad; Abro, Asma; Khan, Rayyan Ahmad; Rehman, Asad Ur; Osama, Muhammad; Javed, Saad; Baig, Ahmadullah; Shabbir, Muhammad Raffey; Assir, Muhammad Zaman Khan

doi:10.26434/chemrxiv.12227363.v1

Cited by 12 publications

(14 citation statements)

References 16 publications

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“…The active site residues of SARS-CoV-2 nsp13 include Lys288 and Ser289 ( 106 ), and a computational analysis revealed that several drugs, including flavin adenine dinucleotide, desmopressin, glecaprevir and rifabutin, all interacted with Arg443 ( 107 ). The binding affinity of 49 with nsp13 (−6.9 kcal/mol) was significantly greater than that of citric acid (−5.5 kcal/mol).…”

Section: Discussionmentioning

confidence: 99%

A molecular modelling approach for identifying antiviral selenium-containing heterocyclic compounds that inhibit the main protease of SARS-CoV-2: an in silico investigation

Rakib¹,

Nain

Sami

et al. 2021

Briefings in Bioinformatics

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Coronavirus disease 2019 (COVID-19), an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been declared a global pandemic by the World Health Organization, and the situation worsens daily, associated with acute increases in case fatality rates. The main protease (Mpro) enzyme produced by SARS-CoV-2 was recently demonstrated to be responsible for not only viral reproduction but also impeding host immune responses. The element selenium (Se) plays a vital role in immune functions, both directly and indirectly. Thus, we hypothesised that Se-containing heterocyclic compounds might curb the activity of SARS-CoV-2 Mpro. We performed a molecular docking analysis and found that several of the selected selenocompounds showed potential binding affinities for SARS-CoV-2 Mpro, especially ethaselen (49), which exhibited a docking score of −6.7 kcal/mol compared with the −6.5 kcal/mol score for GC376 (positive control). Drug-likeness calculations suggested that these compounds are biologically active and possess the characteristics of ideal drug candidates. Based on the binding affinity and drug-likeness results, we selected the 16 most effective selenocompounds as potential anti-COVID-19 drug candidates. We also validated the structural integrity and stability of the drug candidate through molecular dynamics simulation. Using further in vitro and in vivo experiments, we believe that the targeted compound identified in this study (ethaselen) could pave the way for the development of prospective drugs to combat SARS-CoV-2 infections and trigger specific host immune responses.

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Section: Discussionmentioning

confidence: 99%

A molecular modelling approach for identifying antiviral selenium-containing heterocyclic compounds that inhibit the main protease of SARS-CoV-2: an in silico investigation

Rakib¹,

Nain

Sami

et al. 2021

Briefings in Bioinformatics

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“…The following are the three alternative combinations for training this model: The last strategy is the combined model. The combined model is used in many researches [87,91,92] for COVID-19 drug repurposing.…”

Section: Drug Developmentmentioning

confidence: 99%

Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

Alafif

Tehame

Bajaba

et al. 2021

IJERPH

113

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With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have also been instrumental in tackling the outbreak of Coronavirus (COVID-19), which has been happening around the world. The SARS-CoV-2 virus-induced COVID-19 epidemic has spread rapidly across the world, leading to international outbreaks. The COVID-19 fight to curb the spread of the disease involves most states, companies, and scientific research institutions. In this research, we look at the Artificial Intelligence (AI)-based ML and DL methods for COVID-19 diagnosis and treatment. Furthermore, in the battle against COVID-19, we summarize the AI-based ML and DL methods and the available datasets, tools, and performance. This survey offers a detailed overview of the existing state-of-the-art methodologies for ML and DL researchers and the wider health community with descriptions of how ML and DL and data can improve the status of COVID-19, and more studies in order to avoid the outbreak of COVID-19. Details of challenges and future directions are also provided.

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“… [84] Chernyshev, Anatoly. [79] Hosseini, Maryam, et al [78] 19 BPH Doxazosin (DB00590) alpha-1 antagonist treat benign prostatic hypertrophy potential SARS-CoV-2 Mpro inhibitor (validated by MD trajectory clustering) Yes Gupta, Aayush [80] 20 Impotency Tadalafil (DB00820) PDE5 inhibitor treat erectile dysfunction Potential against nsp1 (DeepDTA method) 2’-O-methyltransferase potential inhibitor Yes Anwar, Muhamma [114] Sharma, [81] 21 Anti psychotic Fluspirilene (DB04842) Neurotransmitter inhibitor Used for chronic schizophrenia An antagonist for Dopamine D2 receptor and 5-HT receptor 2 A Inhibits Voltage-dependent calcium channel gamma-1 Active against SARS-CoV-2 in-vitro in Vero E6 cell line Activity against SARS-CoV and MERS-CoV in-vitro Vero E6 cell line Weston S. et al [115] Dyall J. et al [116] 22 Anti psychotic Pimozide (DB01100) Diphenylbutylpiperidine Suppress vocal and motor tics in Tourette syndrome An antagonist of Dopamine D2, D3 receptor Inhibits Potassium voltage-gated channel subfamily H member two and Calmodulin Binding affinity against 3C-like protease Is expected to raise endosomal pH. probably lowers the viral entry an IC50 in inhibiting MPro below 100 μM Yes Yes Vatansever EC.…”

Section: Resultsmentioning

confidence: 99%

Identification of FDA approved drugs against SARS-CoV-2 RNA dependent RNA polymerase (RdRp) and 3-chymotrypsin-like protease (3CLpro), drug repurposing approach

Molavi

Razi

Mirmotalebisohi

et al. 2021

Biomedicine & Pharmacotherapy

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The RNA-dependent RNA polymerase (RdRp) and 3C-like protease (3CLpro) from SARS-CoV-2 play crucial roles in the viral life cycle and are considered the most promising targets for drug discovery against SARS-CoV-2. In this study, FDA-approved drugs were screened to identify the probable anti-RdRp and 3CLpro inhibitors by molecular docking approach. The number of ligands selected from the PubChem database of NCBI for screening was 1760. Ligands were energy minimized using Open Babel. The RdRp and 3CLpro protein sequences were retrieved from the NCBI database. For Homology Modeling predictions, we used the Swiss model server. Their structure was then energetically minimized using SPDB viewer software and visualized in the CHIMERA UCSF software. Molecular dockings were performed using AutoDock Vina, and candidate drugs were selected based on binding affinity (∆G). Hydrogen bonding and hydrophobic interactions between ligands and proteins were visualized using Ligplot and the Discovery Studio Visualizer v3.0 software. Our results showed 58 drugs against RdRp, which had binding energy of -8.5 or less, and 69 drugs to inhibit the 3CLpro enzyme with a binding energy of -8.1 or less. Six drugs based on binding energy and number of hydrogen bonds were chosen for the next step of molecular dynamics (MD) simulations to investigate drug-protein interactions (including Nilotinib, Imatinib and dihydroergotamine for 3clpro and Lapatinib, Dexasone and Relategravir for RdRp). Except for Lapatinib, other drugs-complexes were stable during MD simulation. Raltegravir, an anti-HIV drug, was observed to be the best compound against RdRp based on docking binding energy (-9.5 kcal/mole) and MD results. According to the MD results and binding energy, dihydroergotamine is a suitable candidate for 3clpro inhibition (−9.6 kcal/mol). These drugs were classified into several categories, including antiviral, antibacterial, anti-inflammatory, anti-allergic, cardiovascular, anticoagulant, BPH and impotence, antipsychotic, antimigraine, anticancer, and so on. The common prescription-indications for some of these medication categories appeared somewhat in line with manifestations of COVID-19. We hope that they can be beneficial for patients with certain specific symptoms of SARS-CoV-2 infection, but they can also probably inhibit viral enzymes. We recommend further experimental evaluations in vitro and in vivo on these FDA-approved drugs to assess their potential antiviral effect on SARS-CoV-2.

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Combined Deep Learning and Molecular Docking Simulations Approach Identifies Potentially Effective FDA Approved Drugs for Repurposing Against SARS-CoV-2

Cited by 12 publications

References 16 publications

A molecular modelling approach for identifying antiviral selenium-containing heterocyclic compounds that inhibit the main protease of SARS-CoV-2: an in silico investigation

A molecular modelling approach for identifying antiviral selenium-containing heterocyclic compounds that inhibit the main protease of SARS-CoV-2: an in silico investigation

Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions

Identification of FDA approved drugs against SARS-CoV-2 RNA dependent RNA polymerase (RdRp) and 3-chymotrypsin-like protease (3CLpro), drug repurposing approach

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