ArticleComputers in biology and medicine2021
Deep learning model for virtual screening of novel 3C-like protease enzyme inhibitors against SARS coronavirus diseases.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 33 citations in OpenAlex.
- Organoids in respiratory virus research: advances and perspectives.Molecular biomedicine · 2025Review
- In Silico Screening of Natural Flavonoids against 3-Chymotrypsin-like Protease of SARS-CoV-2 Using Machine Learning and Molecular Modeling.Molecules (Basel, Switzerland) · 2023Article
- Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management.BioDrugs : clinical immunotherapeutics, biopharmaceuticals and gene therapy · 2023Review
- Discovery of novel JAK1 inhibitors through combining machine learning, structure-based pharmacophore modeling and bio-evaluation.Journal of translational medicine · 2023Article
- A Random Forest Model for Peptide Classification Based on Virtual Docking Data.International journal of molecular sciences · 2023Article
- Drug discovery through Covid-19 genome sequencing with siamese graph convolutional neural network.Multimedia tools and applications · 2023Article
- In-silico approaches for identification of compounds inhibiting SARS-CoV-2 3CL protease.PloS one · 2023Article
- A new glimpse on the active site of SARS-CoV-2 3CLpro, coupled with drug repurposing study.Molecular diversity · 2022Article
- Targeting SARS-CoV-2 endoribonuclease: a structure-based virtual screening supported by in vitro analysis.Scientific reports · 2022Article
- Artificial Intelligence in Vaccine and Drug Design.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Deep learning-based molecular dynamics simulation for structure-based drug design against SARS-CoV-2.Computational and structural biotechnology journal · 2022Review
- Application of random forest based on semi-automatic parameter adjustment for optimization of anti-breast cancer drugs.Frontiers in oncology · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the context of the recently emerging COVID-19 pandemic, we developed a deep learning model that can be used to predict the inhibitory activity of 3CLpro in severe acute respiratory syndrome coronavirus (SARS-CoV) for unknown compounds during the virtual screening process. This paper proposes a novel deep learning-based method to implement virtual screening with convolutional neural network (CNN) architecture. The descriptors represent chemical molecules, and these descriptors are input into the CNN framework to train a model and predict active compounds. When compared to other machine learning methods, including random forest, naive Bayes, decision tree, and support vector machine, the proposed CNN model's evaluation of the test set showed an accuracy of 0.86, a sensitivity of 0.45, a specificity of 0.96, a precision of 0.73, a recall of 0.45, an F-measure of 0.55, and a ROC of 0.71. The CNN model screened 17 out of 918 phytochemical compounds; 60 out of 423 from the natural product NCI divset IV; 17,831 out of 112,267 from the ZINC natural product database; and 315 out of 1556 FDA-approved drugs as anti-SARS-CoV agents. Further, to prioritize drug-like compounds, Lipinski's rule of five was applied to screen anti-SARS-CoV compounds (excluding FDA-approved drugs), resulting in 10, 59, and 14,025 hit molecules. Out of 10 phytochemical compounds, 9 anti-SARS-CoV agents belonged to the flavonoid group. In conclusion, the proposed CNN model can prove useful for developing novel target-specific anti-SARS-CoV compounds.
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