ArticleRSC advances2020
Assessing potential inhibitors of SARS-CoV-2 main protease from available drugs using free energy perturbation simulations.
Article in RSC advances, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 32 citations in OpenAlex.
- Discovering Influenza Virus Neuraminidase Inhibitors via Computational and Experimental Studies.ACS omega · 2024Article
- MedChemExpress compounds prevent neuraminidase N1RSC advances · 2024Article
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- Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.Molecular diversity · 2024Article
- Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies.Molecules (Basel, Switzerland) · 2023Review
- Searching for potential inhibitors of SARS-COV-2 main protease using supervised learning and perturbation calculations.Chemical physics · 2023Article
- New Halogenated Compounds fromMolecules (Basel, Switzerland) · 2022Article
- Binding Studies and Lead Generation of Pteridin-7(8International journal of molecular sciences · 2022Article
- A computationally affordable approach for accurate prediction of the binding affinity of JAK2 inhibitors.Journal of molecular modeling · 2022Article
- Insights into the binding and covalent inhibition mechanism of PF-07321332 to SARS-CoV-2 MRSC advances · 2022Article
- DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins.Computers in biology and medicine · 2021Article
- Searching and designing potential inhibitors for SARS-CoV-2 Mpro from natural sources using atomistic and deep-learning calculations.RSC advances · 2021Article
- The interaction of the bioflavonoids with five SARS-CoV-2 proteins targets: An in silico study.Computers in biology and medicine · 2021Article
- Molecular design of anticancer drugs from marine fungi derivatives.RSC advances · 2021Article
- Computational estimation of potential inhibitors from known drugs against the main protease of SARS-CoV-2.RSC advances · 2021Article
- De novo design and bioactivity prediction of SARS-CoV-2 main protease inhibitors using recurrent neural network-based transfer learning.BMC chemistry · 2021Article
- Rapid prediction of possible inhibitors for SARS-CoV-2 main protease using docking and FPL simulations.RSC advances · 2020Article
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The main protease (Mpro) of the novel coronavirus SARS-CoV-2, which has caused the COVID-19 pandemic, is responsible for the maturation of its key proteins. Thus, inhibiting SARS-CoV-2 Mpro could prevent SARS-CoV-2 from multiplying. Because new inhibitors require thorough validation, repurposing current drugs could help reduce the validation process. Many recent studies used molecular docking to screen large databases for potential inhibitors of SARS-CoV-2 Mpro. However, molecular docking does not consider molecular dynamics and thus can be prone to error. In this work, we developed a protocol using free energy perturbation (FEP) to assess the potential inhibitors of SARS-CoV-2 Mpro. First, we validated both molecular docking and FEP on a set of 11 inhibitors of SARS-CoV-2 Mpro with experimentally determined inhibitory data. The experimentally deduced binding free energy exhibits significantly stronger correlation with that predicted by FEP (
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