The development of effective antivirals is of great importance due to the threat associated with the rapid spread of viral infections. The accumulation of data in scientific publications and in databases of biologically active compounds provides an opportunity to extract specific information about interactions between chemicals and their viral and host targets. This information can be used for elucidation of knowledge about potential antiviral activity of chemical compounds, their side effects and toxicities. Our study aims to extract knowledge about virus-host interactions and potential antiviral agents based on the mining of massive amounts of scientific publications. With a set of previously developed algorithms, we have extracted comprehensive information on virus-host interactions and chemical compounds that interact with both viral and host targets. We collected data on the interactions of several viruses, including hepatitis B and C viruses, SARS-CoV-2, influenza A and B, and herpes simplex viruses, with (1) the host (human body), (2) potential antiviral agents, and, also extracted information on the interactions between potential antiviral agents and host proteins. Based on the data analysis performed, we created a freely available knowledge base on the interaction of chemical compounds with viral proteins and their host targets, allowing the exploration of both well-studied and recently discovered novel virus-host-chemical-compound interactions.
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Keywords: virus-host interactions, chemical compounds, biological activity, antivirals, text mining
Citation:
Tarasova O.A., Biziukova N.Yu., Stolbova E.A., Stolbov L.A., Taktashov R.R., Karasev D.A., Ionov N.S., Ivanov S.M., Dmitriev A.V., Rudik A.V., Druzhilovskiy D.S., Sobolev B.N., Filimonov D.A., Poroikov V.V. (2024) Extracting information on virus-human interactions and on antiviral compounds based on automated analysis of large text collections. Biomeditsinskaya Khimiya, 70(6), 469-474.
Tarasova O.A. et al. Extracting information on virus-human interactions and on antiviral compounds based on automated analysis of large text collections // Biomeditsinskaya Khimiya. - 2024. - V. 70. -N 6. - P. 469-474.
Tarasova O.A. et al., "Extracting information on virus-human interactions and on antiviral compounds based on automated analysis of large text collections." Biomeditsinskaya Khimiya 70.6 (2024): 469-474.
Tarasova, O. A., Biziukova, N. Yu., Stolbova, E. A., Stolbov, L. A., Taktashov, R. R., Karasev, D. A., Ionov, N. S., Ivanov, S. M., Dmitriev, A. V., Rudik, A. V., Druzhilovskiy, D. S., Sobolev, B. N., Filimonov, D. A., Poroikov, V. V. (2024). Extracting information on virus-human interactions and on antiviral compounds based on automated analysis of large text collections. Biomeditsinskaya Khimiya, 70(6), 469-474.
References
Catacutan D.B., Alexander J., Arnold A., Stokes J. (2024) Machine learning in preclinical drug discovery. Nat. Chem. Biol., 20(8), 960–973. CrossRef Scholar google search
Tarasova O.A., Rudik A.V., Biziukova N.Y., Filimonov D.A., Poroikov V.V. (2022) Chemical named entity recognition in the texts of scientific publications using the naïve Bayes classifier approach. J. Cheminformatics, 14(1), 55. CrossRef Scholar google search
Biziukova N.Y., Ivanov S.M., Tarasova O.A. (2024) Identification of proteins and genes associated with Hedgehog signaling pathway involved in neoplasm formation using text-mining approach. Big Data Mining Analytics, 7(1), 107–130. CrossRef Scholar google search
Knowledge base on the interaction of chemicals and viruses with the human body. Retrieved November 28, 2024, from: https://www.way2drug.com/viruses/nlp/. Scholar google search
Weber L., Sänger M., Münchmeyer J., Habibi M., Leser U., Akbik A. (2021) HunFlair: An easy-to-use tool for state-ofthe- art biomedical named entity recognition. Bioinformatics, 37(17), 2792–2794. CrossRef Scholar google search
Tarasova O., Ivanov S., Filimonov D.A., Poroikov V. (2020) Data and text mining help identify key proteins involved in the molecular mechanisms shared by SARS-CoV-2 and HIV-1. Molecules, 25(12), 2944. CrossRef Scholar google search
Tarasova O., Biziukova N., Shemshura A., Filimonov D., Kireev D., Pokrovskaya A., Poroikov V. (2023) Identification of molecular mechanisms involved in viral infection progression based on text mining: Case study for HIV infection. Int. J. Mol. Sci., 24(2), 1465. CrossRef Scholar google search
Quenelle D.C., Collins D.J., Kern E.R. (2003) Efficacy of multiple- or single-dose cidofovir against vaccinia and cowpox virus infections in mice. Antimicrob. Agents Chemother., 47(10), 3275–3280. CrossRef Scholar google search
de Clercq E. (2001) Vaccinia virus inhibitors as a paradigm for the chemotherapy of poxvirus infections. Clin. Microbiol. Rev., 14(2), 382–397. CrossRef Scholar google search
Petersen B.L, Buchwald C., Gerstoft J., Bretlau P., Lindeberg H. (1998) An aggressive and invasive growth of juvenile papillomas involving the total respiratory tract. J. Laryngol. Otol., 112(11), 1101–1104. CrossRef Scholar google search
