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3 Publications visible to you, out of a total of 3
Can artificial intelligence transform antiviral drug discovery?

Abstract (Expand)

The development of antiviral therapies is constrained by high costs and extended timelines, often insufficient to address rapidly spreading viral outbreaks. Artificial intelligence (AI) has recently … shown significant progress in identifying and optimizing therapeutic candidates. This review examines the application of AI across four domains in antiviral drug discovery: target identification via host-virus protein-protein interaction prediction and machine-learning analysis of genome-wide CRISPR screens; drug repurposing; de novo molecule design with generative AI; and resistance mutations prediction and phenotypic effects from viral sequence data. We discuss in silico and validated studies, focusing on the limited in vitro and in vivo evidence, and highlight common challenges and key limitations.

Authors: Irina Tirosyan, Yeva Gabrielyan, Vahe Petrosyan, Marco Vignuzzi, Hovakim Zakaryan

Date Published: 22nd Mar 2026

Publication Type: Journal

Multi-target computational pipeline for discovery of pan-influenza neuraminidase inhibitors.

Abstract (Expand)

The continuous evolution of influenza A and B viruses, coupled with the emergence of drug resistance, creates a pressing need for novel antiviral agents with broad-spectrum activity. The viral neuraminidase … enzyme remains a prime target, but its structural variability across different strains complicates the discovery of universal inhibitors. To address this challenge, we developed and implemented a multi-target computational pipeline designed to identify pan-influenza neuraminidase inhibitors. Our strategy involved high-precision molecular docking of a curated library containing 499,721 compounds against three structurally distinct neuraminidase representatives from influenza A (H1N1, H2N2) and influenza B viruses. Hits were prioritized using a cascade of energetic and geometric filters, followed by a rigorous two-tiered validation using extensive molecular dynamics simulations. This validation not only confirmed binding stability on the primary target but also critically assessed whether candidates maintained stable interactions across the other neuraminidase subtypes. This cross-validation approach was essential for eliminating subtype-specific binders, ultimately identifying ten compounds with robust, pan-influenza binding profiles. Notably, the successful identification of a diastereomer of the established drug zanamivir among the top candidates provides strong validation for the pipeline's ability to find biologically relevant scaffolds. Overall, this work demonstrates the integration of multi-target screening with cross-validated molecular dynamics (cross-MD) that overcame target variability and yielded ten promising hits candidates for next-generation anti-influenza therapeutics.

Authors: Smbat Gevorgyan, Marusya Ayvazyan, Levon Kharatyan, Anastasiya Shavina, Narek Abelyan, Hamlet Khachatryan, Hovakim Zakaryan

Date Published: 10th Mar 2026

Publication Type: Journal

Data-driven discovery of chemical signatures for developing new inhibitors against human influenza viruses.

Abstract (Expand)

This study presents cheminformatics analysis of the antiviral chemical space targeting human influenza A and B viruses. By curating 407,366 small molecules from ChEMBL and PubChem, we evaluated … physicochemical properties, structural motifs, and activity trends across phenotypic and target-based assays. We found that 90.6% of evaluated molecules met Lipinski's Rule of Five, with active compounds exhibiting higher topological polar surface area and hydrogen bond donor groups. Target-specific analyses revealed distinct profiles for neuraminidase (NA) and hemagglutinin (HA) inhibitors, including larger molecular weights and increased rotatable bonds. Structural characterization identified cyclohexene, dihydropyran, and pyrimidine rings as prevalent in highly active molecules, while phthalimide motifs correlated with inactivity. Clustering of phenotypic assay data highlighted four promising and unique antiviral candidates, with unexplored chemical space. We also identified five multi-target scaffolds, including the curcumin-like scaffold, demonstrating dual inhibitory potential against two viral proteins. Molecular docking experiments on molecules within one of these multi-target scaffolds indicated their potential as initial hit candidates. Combined RMSD, PDF and DCCM analyses across molecular dynamics simulations elucidated the binding behaviour of five curcumin-like candidates. Two ligands remained as stable as the reference antivirals, one showed target-specific loss of affinity, and two dissociated rapidly, indicating that the stable pair should be prioritised for subsequent in vitro validation. Overall, the findings of this study can aid computer-aided drug design efforts, contributing to the development of novel antiviral agents against human influenza viruses.

Authors: Levon Kharatyan, Smbat Gevorgyan, Hamlet Khachatryan, Anastasiya Shavina, Astghik Hakobyan, Mher Matevosyan, Hovakim Zakaryan

Date Published: 5th Jun 2025

Publication Type: Journal

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