Since severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly contagious and mortal, finding a treatment is time critical. Drug repurposing is probably the quickest and safest approach in our arsenal. However, testing every drug in a brute force manner would require a lot of resources, and a more sophisticated method is required to filter possible candidates. Since several molecules have already been shown to be effective against SARS-CoV-2 in wet-lab experiments, choosing drugs with similar characteristics would increase our chances of success. In this study, we compare the molecular docking results of FDA-approved drugs from the ZINC database against the molecules with positive experimental results. AutoDock Vina was used to dock the molecules against the SARS-CoV-2 spike receptor bound to the ACE2 receptor (6M0J). Results were pre-filtered to 50 candidates according to their binding affinities and the 10 most promising molecules that have similar interactions with the experimental drugs were identified. Then, the 10 molecules were docked against B.1.1.7, B.1.351, and P.1 variants, and their inhibition potentials were discussed. According to the results, we conclude that some molecules that inhibit the wild type also have the potential to inhibit the variants as well. However, further experimental and clinical studies are needed.
","abstract_html":"<p>Since severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly contagious and mortal, finding a treatment is time critical. Drug repurposing is probably the quickest and safest approach in our arsenal. However, testing every drug in a brute force manner would require a lot of resources, and a more sophisticated method is required to filter possible candidates. Since several molecules have already been shown to be effective against SARS-CoV-2 in wet-lab experiments, choosing drugs with similar characteristics would increase our chances of success. In this study, we compare the molecular docking results of FDA-approved drugs from the ZINC database against the molecules with positive experimental results. AutoDock Vina was used to dock the molecules against the SARS-CoV-2 spike receptor bound to the ACE2 receptor (6M0J). Results were pre-filtered to 50 candidates according to their binding affinities and the 10 most promising molecules that have similar interactions with the experimental drugs were identified. Then, the 10 molecules were docked against B.1.1.7, B.1.351, and P.1 variants, and their inhibition potentials were discussed. According to the results, we conclude that some molecules that inhibit the wild type also have the potential to inhibit the variants as well. However, further experimental and clinical studies are needed.</p>","abstract_has_math":false,"creators":["Oztas, Deniz Yasar"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":"Thesis","degree_discipline":"Computational and Data Sciences","degree_department":null,"school":null,"contributors":["Dr. Gennady M. Verkhivker","Dr. Cyril Rakovski","Dr. Rakesh Tiwari"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-08-01T07:00:00Z","date_published":"2021-08-01T07:00:00Z","updated_at":"2026-07-24T01:38:16Z","subjects":["COVID-19","Molecular Docking","Drug Repurposing","Bioinformatics","Computational Biology"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.chapman.edu/cads_theses/8","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Dr. Gennady M. Verkhivker","Dr. Cyril Rakovski","Dr. Rakesh Tiwari"]},{"key":"dc:creator","label":"Author","values":["Oztas, Deniz Yasar"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational and Data Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["COVID-19","Molecular Docking","Drug Repurposing","Bioinformatics","Computational Biology"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.chapman.edu/cads_theses/8"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Since severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly contagious and mortal, finding a treatment is time critical. Drug repurposing is probably the quickest and safest approach in our arsenal. However, testing every drug in a brute force manner would require a lot of resources, and a more sophisticated method is required to filter possible candidates. Since several molecules have already been shown to be effective against SARS-CoV-2 in wet-lab experiments, choosing drugs with similar characteristics would increase our chances of success. In this study, we compare the molecular docking results of FDA-approved drugs from the ZINC database against the molecules with positive experimental results. AutoDock Vina was used to dock the molecules against the SARS-CoV-2 spike receptor bound to the ACE2 receptor (6M0J). Results were pre-filtered to 50 candidates according to their binding affinities and the 10 most promising molecules that have similar interactions with the experimental drugs were identified. Then, the 10 molecules were docked against B.1.1.7, B.1.351, and P.1 variants, and their inhibition potentials were discussed. According to the results, we conclude that some molecules that inhibit the wild type also have the potential to inhibit the variants as well. However, further experimental and clinical studies are needed.
"]},{"key":"dc:source","label":"Dc Source","values":["D. Y. Oztas, \"Drug repurposing for COVID-19 using molecular docking tools,\" M. S. thesis, Chapman University, Orange, CA, 2021. https://doi.org/10.36837/chapman.000286"]},{"key":"dc:title","label":"Title","values":["Drug Repurposing for COVID-19 Using Molecular Docking Tools"]}]}],"canonical_facts":{"dc:contributor":["Dr. Gennady M. Verkhivker","Dr. Cyril Rakovski","Dr. Rakesh Tiwari"],"dc:creator":["Oztas, Deniz Yasar"],"dc:description.abstract":["Since severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly contagious and mortal, finding a treatment is time critical. Drug repurposing is probably the quickest and safest approach in our arsenal. However, testing every drug in a brute force manner would require a lot of resources, and a more sophisticated method is required to filter possible candidates. Since several molecules have already been shown to be effective against SARS-CoV-2 in wet-lab experiments, choosing drugs with similar characteristics would increase our chances of success. In this study, we compare the molecular docking results of FDA-approved drugs from the ZINC database against the molecules with positive experimental results. AutoDock Vina was used to dock the molecules against the SARS-CoV-2 spike receptor bound to the ACE2 receptor (6M0J). Results were pre-filtered to 50 candidates according to their binding affinities and the 10 most promising molecules that have similar interactions with the experimental drugs were identified. Then, the 10 molecules were docked against B.1.1.7, B.1.351, and P.1 variants, and their inhibition potentials were discussed. According to the results, we conclude that some molecules that inhibit the wild type also have the potential to inhibit the variants as well. However, further experimental and clinical studies are needed.
"],"dc:identifier":["https://digitalcommons.chapman.edu/cads_theses/8"],"dc:source":["D. Y. Oztas, \"Drug repurposing for COVID-19 using molecular docking tools,\" M. S. thesis, Chapman University, Orange, CA, 2021. https://doi.org/10.36837/chapman.000286"],"dc:subject":["COVID-19","Molecular Docking","Drug Repurposing","Bioinformatics","Computational Biology"],"dc:title":["Drug Repurposing for COVID-19 Using Molecular Docking Tools"],"thesis:degree_discipline":["Computational and Data Sciences"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T01:38:16Z"}