Geometric Deep learning Prioritization and Validation of Cannabis Phytochemicals as Anti-HCV Non-nucleoside Direct-acting Inhibitors
Introduction: The rate of acute hepatitis C increased by 7% between 2020 and 2021, after the number of cases doubled between 2014 and 2020. With the current adoption of pan-genotypic HCV therapy, there is a need for improved availability and accessibility of this therapy. However, double and triple...
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SAGE Publishing
2024-12-01
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| Series: | Biomedical Engineering and Computational Biology |
| Online Access: | https://doi.org/10.1177/11795972241306881 |
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| author | Ssemuyiga Charles Mulumba Pius Edgar |
| author_facet | Ssemuyiga Charles Mulumba Pius Edgar |
| author_sort | Ssemuyiga Charles |
| collection | DOAJ |
| description | Introduction: The rate of acute hepatitis C increased by 7% between 2020 and 2021, after the number of cases doubled between 2014 and 2020. With the current adoption of pan-genotypic HCV therapy, there is a need for improved availability and accessibility of this therapy. However, double and triple DAA-resistant variants have been identified in genotypes 1 and 5 with resistance-associated amino acid substitutions (RAASs) in NS3/4A, NS5A, and NS5B. The role of this research was to screen for novel potential NS5B inhibitors from the cannabis compound database (CBD) using Deep Learning. Methods: Virtual screening of the CBD compounds was performed using a trained Graph Neural Network (GNN) deep learning model. Re-docking and conventional docking were used to validate the results for these ligands since some had rotatable bonds >10. About 31 of the top 67 hits from virtual screening and docking were selected after ADMET screening. To verify their candidacy, 6 random hits were taken for FEP/MD and Molecular Simulation Dynamics to confirm their candidacy. Results: The top 200 compounds from the deep learning virtual screening were selected, and the virtual screening results were validated by re-docking and conventional docking. The ADMET profiles were optimal for 31 hits. Simulated complexes indicate that these hits are likely inhibitors with suitable binding affinities and FEP energies. Phytil Diphosphate and glucaric acid were suggested as possible ligands against NS5B. |
| format | Article |
| id | doaj-art-663a92fff06f411fbdc2d8d8367db635 |
| institution | OA Journals |
| issn | 1179-5972 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | SAGE Publishing |
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| series | Biomedical Engineering and Computational Biology |
| spelling | doaj-art-663a92fff06f411fbdc2d8d8367db6352025-08-20T01:58:51ZengSAGE PublishingBiomedical Engineering and Computational Biology1179-59722024-12-011510.1177/11795972241306881Geometric Deep learning Prioritization and Validation of Cannabis Phytochemicals as Anti-HCV Non-nucleoside Direct-acting InhibitorsSsemuyiga Charles0Mulumba Pius Edgar1Department of Microbiology, Kampala International University, School of Natural and Applied Sciences (SONAS), Kansanga, Kampala, UgandaDepartment of Microbiology, Kampala International University, School of Natural and Applied Sciences (SONAS), Kansanga, Kampala, UgandaIntroduction: The rate of acute hepatitis C increased by 7% between 2020 and 2021, after the number of cases doubled between 2014 and 2020. With the current adoption of pan-genotypic HCV therapy, there is a need for improved availability and accessibility of this therapy. However, double and triple DAA-resistant variants have been identified in genotypes 1 and 5 with resistance-associated amino acid substitutions (RAASs) in NS3/4A, NS5A, and NS5B. The role of this research was to screen for novel potential NS5B inhibitors from the cannabis compound database (CBD) using Deep Learning. Methods: Virtual screening of the CBD compounds was performed using a trained Graph Neural Network (GNN) deep learning model. Re-docking and conventional docking were used to validate the results for these ligands since some had rotatable bonds >10. About 31 of the top 67 hits from virtual screening and docking were selected after ADMET screening. To verify their candidacy, 6 random hits were taken for FEP/MD and Molecular Simulation Dynamics to confirm their candidacy. Results: The top 200 compounds from the deep learning virtual screening were selected, and the virtual screening results were validated by re-docking and conventional docking. The ADMET profiles were optimal for 31 hits. Simulated complexes indicate that these hits are likely inhibitors with suitable binding affinities and FEP energies. Phytil Diphosphate and glucaric acid were suggested as possible ligands against NS5B.https://doi.org/10.1177/11795972241306881 |
| spellingShingle | Ssemuyiga Charles Mulumba Pius Edgar Geometric Deep learning Prioritization and Validation of Cannabis Phytochemicals as Anti-HCV Non-nucleoside Direct-acting Inhibitors Biomedical Engineering and Computational Biology |
| title | Geometric Deep learning Prioritization and Validation of Cannabis Phytochemicals as Anti-HCV Non-nucleoside Direct-acting Inhibitors |
| title_full | Geometric Deep learning Prioritization and Validation of Cannabis Phytochemicals as Anti-HCV Non-nucleoside Direct-acting Inhibitors |
| title_fullStr | Geometric Deep learning Prioritization and Validation of Cannabis Phytochemicals as Anti-HCV Non-nucleoside Direct-acting Inhibitors |
| title_full_unstemmed | Geometric Deep learning Prioritization and Validation of Cannabis Phytochemicals as Anti-HCV Non-nucleoside Direct-acting Inhibitors |
| title_short | Geometric Deep learning Prioritization and Validation of Cannabis Phytochemicals as Anti-HCV Non-nucleoside Direct-acting Inhibitors |
| title_sort | geometric deep learning prioritization and validation of cannabis phytochemicals as anti hcv non nucleoside direct acting inhibitors |
| url | https://doi.org/10.1177/11795972241306881 |
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