Publications by authors named "H Zakaryan"

The COVID-19 pandemic, driven by the SARS-CoV-2 virus, necessitates the development of effective therapeutics. The main protease of the virus, Mpro, is a key target due to its crucial role in viral replication. Our study presents a novel approach combining ligand-based pharmacophore modeling with structure-based advanced virtual screening to identify potential inhibitors of Mpro.

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Medium-chain antimicrobial lipids are promising antiviral agents to inhibit membrane-enveloped viruses such as African swine fever virus (ASFV) and influenza A virus (IAV) in livestock applications. However, current uses are limited to feed pathogen mitigation due to low aqueous solubility and the development of water-dispersible lipid formulations is needed for broader application usage. In this study, we report a water-dispersible antimicrobial lipid mixture of monoglycerides and lactylates that can inhibit ASFV and IAV and exhibits antiviral properties in drinking water and feed matrices.

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The coronavirus disease 19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to a global health crisis with millions of confirmed cases and related deaths. The main protease (Mpro) of SARS-CoV-2 is crucial for viral replication and presents an attractive target for drug development. Despite the approval of some drugs, the search for effective treatments continues.

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Background: African swine fever virus (ASFV) is a major threat to pig production and the lack of effective vaccines underscores the need to develop robust antiviral countermeasures. Pathologically, a significant elevation in pro-inflammatory cytokine production is associated with ASFV infection in pigs and there is high interest in identifying dual-acting natural compounds that exhibit antiviral and anti-inflammatory activities.

Methods: Using the laboratory-adapted ASFV BA71V strain, we screened a library of 297 natural, anti-inflammatory compounds to identify promising candidates that protected Vero cells against virus-induced cytopathic effect (CPE).

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In this research, we employed a deep reinforcement learning (RL)-based molecule design platform to generate a diverse set of compounds targeting the neuraminidase (NA) of influenza A and B viruses. A total of 60,291 compounds were generated, of which 86.5 % displayed superior physicochemical properties compared to oseltamivir.

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