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

Authors
 Jiwon Choi  ;  Jun Seop Yun  ;  Hyeeun Song  ;  Yong-Keol Shin  ;  Young-Hoon Kang  ;  Palinda Ruvan Munashingha  ;  Jeongyeon Yoon  ;  Nam Hee Kim  ;  Hyun Sil Kim  ;  Jong In Yook  ;  Dongseob Tark  ;  Yun-Sook Lim  ;  Soon B Hwang 
Citation
 MOLECULES, Vol.26(12) : 3592, 2021-06 
Journal Title
MOLECULES
Issue Date
2021-06
Keywords
African swine fever virus ; antiviral ; machine learning ; molecular docking
Abstract
African 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.
Files in This Item:
T202102304.pdf Download
DOI
10.3390/molecules26123592
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
Yonsei Authors
Kim, Nam Hee(김남희) ORCID logo https://orcid.org/0000-0002-3087-5276
Kim, Hyun Sil(김현실) ORCID logo https://orcid.org/0000-0003-3614-1764
Yook, Jong In(육종인) ORCID logo https://orcid.org/0000-0002-7318-6112
Choi, Jiwon(최지원)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/184153
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