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

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

Molecules (Basel, Switzerland) 2021 Jun 11

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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.

Citation

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. Prediction of African Swine Fever Virus Inhibitors by Molecular Docking-Driven Machine Learning Models. Molecules (Basel, Switzerland). 2021 Jun 11;26(12)