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

Publisher: MDPI Country of Publication: Switzerland NLM ID: 100964009 Publication Model: Electronic Cited Medium: Internet ISSN: 1420-3049 (Electronic) Linking ISSN: 14203049 NLM ISO Abbreviation: Molecules Subsets: MEDLINE

Original Publication: Basel, Switzerland : MDPI, c1995-

African Swine Fever/*drug therapy ; African Swine Fever Virus/*drug effects ; Antiviral Agents/*chemistry ; Antiviral Agents/*pharmacology ; Machine Learning/*standards; African Swine Fever/immunology ; African Swine Fever/virology ; African Swine Fever Virus/immunology ; African Swine Fever Virus/isolation & purification ; Amino Acid Sequence ; Animals ; DNA-Directed DNA Polymerase/chemistry ; DNA-Directed DNA Polymerase/metabolism ; Drug Design ; Molecular Docking Simulation ; Pentagastrin/chemistry ; Pentagastrin/pharmacology ; Swine ; Viral Proteins/chemistry ; Viral Proteins/metabolism

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 Asfv PolX 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.

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NRF-2017R1A2B3002241 National Research Foundation of Korea; NRF-2018R1D1A1B07050744 National Research Foundation of Korea; NRF-2019R1A2C2084535 National Research Foundation of Korea; NRF-2019R1A2C1086914 National Research Foundation of Korea; NRF-2021R1A2C3003496 National Research Foundation of Korea; grant no. 320053-2 to D.T. Ministry of Agriculture, Food and Rural Affairs

Keywords: African swine fever virus; antiviral; machine learning; molecular docking

0 (Antiviral Agents)
0 (Viral Proteins)
EC 2.7.7.- (DNA polymerase X)
EC 2.7.7.7 (DNA-Directed DNA Polymerase)
EF0NX91490 (Pentagastrin)

Date Created: 20210702 Date Completed: 20210728 Latest Revision: 20211217

20240628

PMC8231271

10.3390/molecules26123592

34208385