Computational estimation of potential inhibitors from known drugs against the main protease of SARS-CoV-2

Tam, Nguyen Minh; Pham, Minh Quan; Ha, Nguyen Xuan; Nam, Pham Cam; Phung, Huong Thi Thu

doi:10.1039/d1ra02529e

Cited by 22 publications

(12 citation statements)

References 76 publications

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“…29 Besides, molecular docking calculations have been successfully applied in recent SARS-CoV-2 M pro studies. 71–75…”

Section: Resultsmentioning

confidence: 99%

“…29 Besides, molecular docking calculations have been successfully applied in recent SARS-CoV-2 M pro studies. [71][72][73][74][75] Initially, 75 of the nearly 3000 drug molecules that had the best scores in the GOLD program were selected. It is noteworthy that the selection criterion was based on the score of remdesivir, as this has a potential effect in the treatment of COVID-19.…”

Section: Virtual Screening and Molecular Docking Calculationmentioning

confidence: 99%

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Drug repurposing and computational modeling for discovery of inhibitors of the main protease (M^pro) of SARS-CoV-2

2021

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“…29 Besides, molecular docking calculations have been successfully applied in recent SARS-CoV-2 M pro studies. 71–75…”

Section: Resultsmentioning

confidence: 99%

Section: Virtual Screening and Molecular Docking Calculationmentioning

confidence: 99%

Drug repurposing and computational modeling for discovery of inhibitors of the main protease (M^pro) of SARS-CoV-2

2021

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“…Although docking had a high docking success rate, the obtained pose is probably limited because the docking approach uses many approximations to enhance the computing speed. , The unbiased MD simulations with a length of 100 ns were thus performed to turn the solvated complex into equilibrium states. The AChE + inhibitor complex reaches stable states after ca.…”

Section: Results and Discussionmentioning

confidence: 99%

Identifying Possible AChE Inhibitors from Drug-like Molecules via Machine Learning and Experimental Studies

et al. 2022

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Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer’s disease (AD) treatment. In this work, a machine learning model was trained to rapidly and accurately screen large chemical databases for the potential inhibitors of AChE. The obtained results were then validated via in vitro enzyme assay. Moreover, atomistic simulations including molecular docking and molecular dynamics simulations were then used to understand molecular insights into the binding process of ligands to AChE. In particular, two compounds including benzyl trifluoromethyl ketone and trifluoromethylstyryl ketone were indicated as highly potent inhibitors of AChE because they established IC 50 values of 0.51 and 0.33 μM, respectively. The obtained IC 50 of two compounds is significantly lower than that of galantamine (2.10 μM). The predicted log(BB) suggests that the compounds may be able to traverse the blood–brain barrier. A good agreement between computational and experimental studies was observed, indicating that the hybrid approach can enhance AD therapy.

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“…368 Steered MD Simulations. Steered MD simulations were used to dock drugs and marine compounds to Mpro, to infer the top inhibitors by Tam et al 369,370 MM/PBSA and steered MD simulations suggest that the adsorption of the ACE2 on specific silane monolayers could increase its affinity toward the S protein RBD, which could help develop biosensing tools efficient toward any variants of the SARS-CoV-2 S protein. 371 Semiempirical Free Energy Force Field Methods.…”

Section: Orf9bmentioning

confidence: 99%

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Gao¹,

Wang²,

Chen³

et al. 2022

Chem. Rev.

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Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus–host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein–protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

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Computational estimation of potential inhibitors from known drugs against the main protease of SARS-CoV-2

Abstract: Approved drugs predicted to interact with critical residues in the substrate-binding site of SARS-CoV-2 Mpro can be promising inhibitors.

Cited by 22 publications

References 76 publications

Drug repurposing and computational modeling for discovery of inhibitors of the main protease (M^pro) of SARS-CoV-2

Drug repurposing and computational modeling for discovery of inhibitors of the main protease (M^pro) of SARS-CoV-2

Identifying Possible AChE Inhibitors from Drug-like Molecules via Machine Learning and Experimental Studies

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

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Computational estimation of potential inhibitors from known drugs against the main protease of SARS-CoV-2

Abstract: Approved drugs predicted to interact with critical residues in the substrate-binding site of SARS-CoV-2 Mpro can be promising inhibitors.

Cited by 22 publications

References 76 publications

Drug repurposing and computational modeling for discovery of inhibitors of the main protease (Mpro) of SARS-CoV-2

Drug repurposing and computational modeling for discovery of inhibitors of the main protease (Mpro) of SARS-CoV-2

Identifying Possible AChE Inhibitors from Drug-like Molecules via Machine Learning and Experimental Studies

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Contact Info

Product

Resources

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Drug repurposing and computational modeling for discovery of inhibitors of the main protease (M^pro) of SARS-CoV-2

Drug repurposing and computational modeling for discovery of inhibitors of the main protease (M^pro) of SARS-CoV-2