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Prediction of African Swine Fever Virus Inhibitors by Molecular Docking-Driven Machine Learning Models

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dc.contributor.author김남희-
dc.contributor.author김현실-
dc.contributor.author육종인-
dc.contributor.author김현실-
dc.contributor.author육종인-
dc.contributor.author최지원-
dc.date.accessioned2021-09-29T01:01:54Z-
dc.date.available2021-09-29T01:01:54Z-
dc.date.issued2021-06-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/184153-
dc.description.abstractAfrican swine fever virus (ASFV) causes a highly contagious and severe hemorrhagic viral disease with high mortality in domestic pigs of all ages. Although the virus is harmless to humans, the ongoing ASFV epidemic could have severe economic consequences for global food security. Recent studies have found a few antiviral agents that can inhibit ASFV infections. However, currently, there are no vaccines or antiviral drugs. Hence, there is an urgent need to identify new drugs to treat ASFV. Based on the structural information data on the targets of ASFV, we used molecular docking and machine learning models to identify novel antiviral agents. We confirmed that compounds with high affinity present in the region of interest belonged to subsets in the chemical space using principal component analysis and k-means clustering in molecular docking studies of FDA-approved drugs. These methods predicted pentagastrin as a potential antiviral drug against ASFVs. Finally, it was also observed that the compound had an inhibitory effect on AsfvPolX activity. Results from the present study suggest that molecular docking and machine learning models can play an important role in identifying potential antiviral drugs against ASFVs.-
dc.description.statementOfResponsibilityopen-
dc.formatapplication/pdf-
dc.languageEnglish-
dc.publisherMDPI-
dc.relation.isPartOfMOLECULES-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.titlePrediction of African Swine Fever Virus Inhibitors by Molecular Docking-Driven Machine Learning Models-
dc.typeArticle-
dc.contributor.collegeCollege of Dentistry (치과대학)-
dc.contributor.departmentResearch Institute (부설연구소)-
dc.contributor.googleauthorJiwon Choi-
dc.contributor.googleauthorJun Seop Yun-
dc.contributor.googleauthorHyeeun Song-
dc.contributor.googleauthorYong-Keol Shin-
dc.contributor.googleauthorYoung-Hoon Kang-
dc.contributor.googleauthorPalinda Ruvan Munashingha-
dc.contributor.googleauthorJeongyeon Yoon-
dc.contributor.googleauthorNam Hee Kim-
dc.contributor.googleauthorHyun Sil Kim-
dc.contributor.googleauthorJong In Yook-
dc.contributor.googleauthorDongseob Tark-
dc.contributor.googleauthorYun-Sook Lim-
dc.contributor.googleauthorSoon B Hwang-
dc.identifier.doi10.3390/molecules26123592-
dc.contributor.localIdA00360-
dc.contributor.localIdA01121-
dc.contributor.localIdA02536-
dc.contributor.localIdA01121-
dc.contributor.localIdA02536-
dc.relation.journalcodeJ03201-
dc.identifier.eissn1420-3049-
dc.identifier.pmid34208385-
dc.subject.keywordAfrican swine fever virus-
dc.subject.keywordantiviral-
dc.subject.keywordmachine learning-
dc.subject.keywordmolecular docking-
dc.contributor.alternativeNameKim, Nam Hee-
dc.contributor.affiliatedAuthor김남희-
dc.contributor.affiliatedAuthor김현실-
dc.contributor.affiliatedAuthor육종인-
dc.contributor.affiliatedAuthor김현실-
dc.contributor.affiliatedAuthor육종인-
dc.citation.volume26-
dc.citation.number12-
dc.citation.startPage3592-
dc.identifier.bibliographicCitationMOLECULES, Vol.26(12) : 3592, 2021-06-
Appears in Collections:
2. College of Dentistry (치과대학) > Research Institute (부설연구소) > 1. Journal Papers
2. College of Dentistry (치과대학) > Dept. of Oral Pathology (구강병리학교실) > 1. Journal Papers

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