This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (CC BY).
ORIGINAL RESEARCH
Classification models for assessment of influenza virus A/H1N1 inhibitors
1 Institute of Biomedical Chemistry (IBMC), Moscow, Russia
2 The Ufa Institute of Chemistry of the Ufa Federal Research Centre of the Russian Academy of Sciences (UFRC RAS), Ufa, Russia
3 Saint Petersburg Pasteur Research Institute of Epidemiology and Microbiology, Saint Petersburg, Russia
Correspondence should be addressed: Leonid A. Stolbov
10 Pogodinskaya St., b. 8, Moscow, 119121, Russia; ur.xednay@alvoblots
Financing: this research, which details the structure-activity relationship models, was supported long-term by the State Program for Fundamental Scientific Research in the Russian Federation (2021–2030) (Number 124050800018-9). This database was developed as part of the state research project entitled “Kinetic, spectral, luminescent, and theoretical analysis of core intermediates in chemical and biochemical oxidation processes” of the Ufa Institute of Chemistry of the Ufa Federal Research Centre of the Russian Academy of Sciences No. 125020601626-9.
Author contribution: Stolbov LA — data analysis, model building, manuscript preparation; Borisevich SS — idea, database preparation; Gorokhov YaV — scientific literature review for database compilation; Zarubaev VV — providing up-to-date results of biological testing; Tarasova OA — the idea and methodology of research; Poroikov VV- research methodology. Every author contributed to writing and editing the paper.
- Liang Y. Pathogenicity and virulence of influenza. Virulence. 2023; 14(1): 2223057. DOI: 10.1080/21505594.2023.2223057.
- Trinh TX, Seo M, Yoon TH, Kim J. Developing random forest based QSAR models for predicting the mixture toxicity of TiO2 based nano-mixtures to Daphnia magna. NanoImpact. 2022; 25: 100383. DOI: 10.1016/j.impact.2022.100383.
- Alharthi AM, Lee MH, Algamal ZY, Al-Fakih AM. Quantitative structure-activity relationship model for classifying the diverse series of antifungal agents using ratio weighted penalized logistic regression. SAR and QSAR in Environmental Research. 2020; 31(8): 571–583. DOI: 10.1080/1062936X.2020.1782467.
- Khomenko TM, Zarubaev VV, Kireeva MV, et al. New type of anti-influenza agents based on benzo[d][1,3]dithiol core. Bioorganic & Medicinal Chemistry Letters. 2020; 30(24): 127653. DOI: 10.1016/j.bmcl.2020.127653.
- Mercader AG, Pomilio AB. QSAR study of flavonoids and biflavonoids as influenza H1N1 virus neuraminidase inhibitors. European Journal of Medicinal Chemistry. 2010; 45(5): 1724– 1730. DOI: 10.1016/j.ejmech.2010.01.005.
- Hammoudan I, Mounadi N, Khedraoui M, Yamari I, Chtita S, Benjelloun AT. QSAR-driven discovery and ranking of potential anti-H1N1 inhibitors. Scientific African. 2025; 30: e03002. DOI: 10.1016/j.sciaf.2025.e03002.
- Li C, Fang J, Lian W, Pang X, Liu A, Du G. In vitro Antiviral Effects and 3 D QSAR Study of Resveratrol Derivatives as Potent Inhibitors of Influenza H 1 N 1 Neuraminidase. Chem Biol Drug Des. 2015; 85(4): 427–438. DOI: 10.1111/cbdd.12425.
- Veerasamy R, Rajak H. QSAR Studies on Neuraminidase Inhibitors as Anti-influenza Agents. tjps. 2021; 18(2): 151–156. DOI: 10.4274/tjps.galenos.2020.45556.
- Algamal ZY, Qasim MK, Lee MH, Ali HTM. QSAR model for predicting neuraminidase inhibitors of influenza A viruses (H1N1) based on adaptive grasshopper optimization algorithm. SAR and QSAR in Environmental Research. 2020; 31(11): 803–814. DOI: 10.1080/1062936X.2020.1818616.
- Abdullahi M, Uzairu A, Shallangwa GA, Mamza PA, Ibrahim MT. Computational modelling studies of some 1,3-thiazine derivatives as anti-influenza inhibitors targeting H1N1 neuraminidase via 2D-QSAR, 3D-QSAR, molecular docking, and ADMET predictions. Beni-Suef Univ J Basic Appl Sci. 2022; 11(1): 104. DOI: 10.1186/ s43088-022-00280-6.
- Mahmoudzadeh Laki R, Pourbasheer E. Design of New Anti-Influenza Structures Based on 3D-QSAR, Molecular Docking and Molecular Dynamics Studies. Chemistry & Biodiversity. 2025;22(9): e202500587. DOI: 10.1002/cbdv.202500587.
- Lian W, Fang J, Li C, Pang X, Liu AL, Du GH. Discovery of Influenza A virus neuraminidase inhibitors using support vector machine and Naïve Bayesian models. Mol Divers. 2016; 20(2): 439–451. DOI: 10.1007/s11030-015-9641-z.
- Wang Y, Ge H, Li Y, et al. Predicting dual-targeting anti-influenza agents using multi-models. Mol Divers. 2015; 19(1): 123–134. DOI: 10.1007/s11030-014-9552-4.
- Li S, Fedorowicz A, Andrew ME. A new descriptor selection scheme for SVM in unbalanced class problem: a case study using skin sensitisation dataset. SAR and QSAR in Environmental Research. 2007; 18(5–6): 423–441. DOI: 10.1080/10629360701428474.
- Stolbov LA, Filimonov DA, Poroikov VV. SAR based on self consistent classifier. SAR and QSAR in Environmental Research. 2022; 33(10): 10. DOI: 10.1080/1062936X.2022.2139751.
- Yegorov AD, Gorokhov YaV, Kuznetsov MM, Borisevich SS. Predskazaniye indeksa selektivnosti malykh molekul v otnoshenii virusa grippa shtamma A/H1N1 s ispol’zovaniyem metodov mashinnogo obucheniya. Izvestiya Akademii Nauk. Seriya Khimicheskaya. 2025; (3): 851. Russian.
- Terry L Riss, Richard A Moravec, Andrew L Niles, Sarah Duellman, Hélène A Benink, Tracy J Worzella, and Lisa Minor. Assay Guidance Manual. https://www.ncbi.nlm.nih.gov/books/ NBK144065/
- Kharbiyev R. Rukovodstvo po eksperimental’nomu (doklinicheskomu) izucheniyu novykh farmakologicheskikh veshchestv. Published online 2005. Russian.
- Huang Z, Chen Z, Qin X, Zhu Q, Yang J. Discovery of tetrasubstituted tetrahydropyrimidines as novel inhibitors against influenza a virus. Bioorganic Chemistry. 2025;163:108785. DOI: 10.1016/j.bioorg.2025.108785.
- Liu S, Li Y, Peng B, et al. Design and Synthesis of PAN Endonuclease Inhibitors through Spirocyclization Strategy against Influenza A Virus. J Med Chem. 2025; 68(13): 13393–13407. DOI: 10.1021/acs.jmedchem.5c00042.
- Zhang C, Huang XH, Wang Z, et al. Quinazoline-based dual-target inhibitors disrupt influenza virus RNP complex: Rational design, synthesis and mechanistic validation of potent anti-influenza agents. European Journal of Medicinal Chemistry. 2026; 301: 118185. DOI: 10.1016/j.ejmech.2025.118185.
- Zhang X, Xia Y, Li P, et al. Discovery of cyperenoic acid as a potent and novel entry inhibitor of influenza A virus. Antiviral Research. 2024; 223: 105822. DOI: 10.1016/j.antiviral.2024.105822.
- Fourches D, Muratov E, Tropsha A. Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research. J Chem Inf Model. 2010; 50(7): 1189–1204. DOI: 10.1021/ci100176x.
- Fourches D, Muratov E, Tropsha A. Trust, but Verify II: A Practical Guide to Chemogenomics Data Curation. J Chem Inf Model. 2016; 56(7): 1243–1252. DOI: 10.1021/acs.jcim.6b00129.
- Filimonov DA, Zakharov AV, Lagunin AA, Poroikov VV. QNA-based ‘Star Track’ QSAR approach. SAR and QSAR in Environmental Research. 2009; 20(7–8): 679–709. DOI: 10.1080/10629360903438370.
- Zakharov AV, Peach ML, Sitzmann M, Nicklaus MC. A New Approach to Radial Basis Function Approximation and Its Application to QSAR. J Chem Inf Model. 2014; 54(3): 713–719. DOI: 10.1021/ci400704f.
- Lagunin AA, Zakharov AV, Filimonov DA, Poroikov VV. A new approach to QSAR modelling of acute toxicity†. SAR and QSAR in Environmental Research. 2007; 18(3–4): 285–298. DOI: 10.1080/10629360701304253.
- Zakharov AV, Lagunin AA, Filimonov DA, Poroikov VV. Quantitative Prediction of Antitarget Interaction Profiles for Chemical Compounds. Chem Res Toxicol. 2012; 25(11): 2378–2385. DOI:10.1021/tx300247r.