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Izmailyan R, Matevosyan M, Khachatryan H, Shavina A, Gevorgyan S, Ghazaryan A, Tirosyan I, Gabrielyan Y, Ayvazyan M, Martirosyan B, Harutyunyan V, Zakaryan H. Discovery of new antiviral agents through artificial intelligence: In vitro and in vivo results. Antiviral Res 2024; 222:105818. [PMID: 38280564 DOI: 10.1016/j.antiviral.2024.105818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/12/2024] [Accepted: 01/18/2024] [Indexed: 01/29/2024]
Abstract
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. After narrowing down the selection through computational filters, nine compounds with non-sialic acid-like structures were selected for in vitro experiments. We identified two compounds, DS-22-inf-009 and DS-22-inf-021 that effectively inhibited the NAs of both influenza A and B viruses (IAV and IBV), including H275Y mutant strains at low micromolar concentrations. Molecular dynamics simulations revealed a similar pattern of interaction with amino acid residues as oseltamivir. In cell-based assays, DS-22-inf-009 and DS-22-inf-021 inhibited IAV and IBV in a dose-dependent manner with EC50 values ranging from 0.29 μM to 2.31 μM. Furthermore, animal experiments showed that both DS-22-inf-009 and DS-22-inf-021 exerted antiviral activity in mice, conferring 65 % and 85 % protection from IAV (H1N1 pdm09), and 65 % and 100 % protection from IBV (Yamagata lineage), respectively. Thus, these findings demonstrate the potential of RL to generate compounds with promising antiviral properties.
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Affiliation(s)
- Roza Izmailyan
- Laboratory of Antiviral Drug Discovery, Institute of Molecular Biology of NAS, Hasratyan 7, 0014, Yerevan, Armenia
| | | | - Hamlet Khachatryan
- Laboratory of Antiviral Drug Discovery, Institute of Molecular Biology of NAS, Hasratyan 7, 0014, Yerevan, Armenia; Denovo Sciences Inc., 0060, Yerevan, Armenia
| | - Anastasiya Shavina
- Laboratory of Antiviral Drug Discovery, Institute of Molecular Biology of NAS, Hasratyan 7, 0014, Yerevan, Armenia; Denovo Sciences Inc., 0060, Yerevan, Armenia
| | - Smbat Gevorgyan
- Laboratory of Antiviral Drug Discovery, Institute of Molecular Biology of NAS, Hasratyan 7, 0014, Yerevan, Armenia; Denovo Sciences Inc., 0060, Yerevan, Armenia
| | - Artur Ghazaryan
- Laboratory of Antiviral Drug Discovery, Institute of Molecular Biology of NAS, Hasratyan 7, 0014, Yerevan, Armenia
| | | | | | | | | | | | - Hovakim Zakaryan
- Laboratory of Antiviral Drug Discovery, Institute of Molecular Biology of NAS, Hasratyan 7, 0014, Yerevan, Armenia; Denovo Sciences Inc., 0060, Yerevan, Armenia.
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Matevosyan M, Harutyunyan V, Abelyan N, Khachatryan H, Tirosyan I, Gabrielyan Y, Sahakyan V, Gevorgyan S, Arakelov V, Arakelov G, Zakaryan H. Design of new chemical entities targeting both native and H275Y mutant influenza a virus by deep reinforcement learning. J Biomol Struct Dyn 2023; 41:10798-10812. [PMID: 36541127 DOI: 10.1080/07391102.2022.2158936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 12/10/2022] [Indexed: 12/24/2022]
Abstract
Influenza virus remains a major public health challenge due to its high morbidity and mortality and seasonal surge. Although antiviral drugs against the influenza virus are widely used as a first-line defense, the virus undergoes rapid genetic changes, resulting in the emergence of drug-resistant strains. Thus, new antiviral drugs that can outwit resistant strains are of significant importance. Herein, we used deep reinforcement learning (RL) algorithm to design new chemical entities (NCEs) that are able to bind to the native and H275Y mutant (oseltamivir-resistant) neuraminidases (NAs) of influenza A virus with better binding energy than oseltamivir. We generated more than 66211 NCEs, which were prioritized based on the filtering rules, structural alerts, and synthetic accessibility. Then, 18 NCEs with better MM/PBSA scores than oseltamivir were further analyzed in molecular dynamics (MD) simulations conducted for 100 ns. The MD experiments showed that 8 NCEs formed very stable complexes with the binding pocket of both native and H275Y mutant NAs of H1N1. Furthermore, most NCEs demonstrated much better binding affinity to group 2 (N2, N3, and N9) and influenza B virus NAs than oseltamivir. Although all 8 NCEs have non-sialic acid-like structures, they showed a similar binding mode as oseltamivir, indicating that it is possible to find new scaffolds with better binding and antiviral properties than sialic acid-like inhibitors. In conclusion, we have designed potential compounds as antiviral candidates for further synthesis and testing against wild and mutant influenza virus.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Vahram Arakelov
- Denovo Sciences Inc, Yerevan, Armenia
- Institute of Molecular Biology of National Academy of Sciences, Yerevan, Armenia
| | - Grigor Arakelov
- Denovo Sciences Inc, Yerevan, Armenia
- Institute of Molecular Biology of National Academy of Sciences, Yerevan, Armenia
| | - Hovakim Zakaryan
- Denovo Sciences Inc, Yerevan, Armenia
- Institute of Molecular Biology of National Academy of Sciences, Yerevan, Armenia
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