Applications of some artificial intelligence tools in the drug design of some compounds targeting the main viral protease of the Feline Infectious Peritonitis Virus (FIPV) in cats

Abstract

AbstractFeline infectious peritonitis virus (FIPV) is one of cats’ most serious viral infections. The FIPV infection induces a complicated syndrome in the affected cats, including immunosuppression and severe inflammatory conditions. Unfortunately, these vaccines cannot prevent cats from getting infected with these viral infections. There is ongoing research on preparing antiviral therapies against FIPV in cats. However, these are still in clinical trials and have not been fully approved by the drug authorities in many countries, including the USA. Targeting the main viral proteases is one of the promising trends in the drug design of many viral diseases, including coronaviruses. The main goal of the current study was to repurpose and test the efficacy of some known antiviral drugs to treat FIPV infection in cats by targeting the FIPV-main protease enzyme. To achieve these goals, we used the in-silico prediction and molecular docking tools to screen and identify some drugs targeting FIPV-MPro. We used the docking and binding energies as the main parameters for selecting target compounds (FIPV-MPro). Our results show that out of the 15 antiviral and immunomodulatory compounds, the top-ranked inhibitors for the FIPV-Mpro are (Michael acceptor inhibitors (N3), Sofosbuvir, and methotrexate).In conclusion, our results confirmed the potential applications of the predicted FIPV-Mpro inhibitors either independently or in combination with other immune-modulatory compounds. Further in vitro and in vivo studies are encouraged to test the efficacy of these identified compounds as potent inhibitors for the MPro of the FIPV in cats. This study will pave the way for the development of novel drugs that treat FIPV infection in cats.