New Methods for Pathogen Risk Assessment: Machine Learning in the Analysis of Toxicity Spectrum of <i>Albifimbria verrucaria</i>
HighlightsThe use of artificial intelligence has great potential for predicting the toxic properties of new little-studied chemical compounds, reducing the time and financial costs associated with identifying the risks of possible threats.Relevance. Mycotoxins, which are secondary metabolites of mol...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | Russian |
| Published: |
27 Scientific Centre named after academician N.D. Zelinsky
2025-05-01
|
| Series: | Вестник войск РХБ защиты |
| Subjects: | |
| Online Access: | https://www.nbsprot.ru/jour/article/view/395 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850039490513469440 |
|---|---|
| author | V. T. Tkachenko M. V. Fedorov V. V. Fedorova A. V. Pozdeev E. B. Kormanovskaya A. S. Klimova P. V. Gunina |
| author_facet | V. T. Tkachenko M. V. Fedorov V. V. Fedorova A. V. Pozdeev E. B. Kormanovskaya A. S. Klimova P. V. Gunina |
| author_sort | V. T. Tkachenko |
| collection | DOAJ |
| description | HighlightsThe use of artificial intelligence has great potential for predicting the toxic properties of new little-studied chemical compounds, reducing the time and financial costs associated with identifying the risks of possible threats.Relevance. Mycotoxins, which are secondary metabolites of mold fungi, represent one of the most significant factors of chronic risk associated with food products. Their danger exceeds the threat posed by synthetic pollutants, plant toxins, food additives, and pesticide residues. However, for many mycotoxins, the full toxicological profile has not yet been established, and traditional analysis methods remain labor-intensive, costly, and insufficiently effective. This makes the search for new approaches to assess their danger and control highly relevant.Purpose of the study is to study the toxicological profile of mycotoxins produced by the pathogenic fungus Albifimbria verrucaria and to determine their level of danger using chemoinformatics and machine learning.Study base sources. Analysis of scientific literature available through open Russian and English-language Internet resources.Method. In silico methods were applied to analyze the toxicological profile of mycotoxins, enabling the identification of high-risk compounds. These methods prioritize substances for further in-depth toxicological assessment, significantly reducing the time and resources required for research.Results and Discussion. The study results showed that approximately 50% of mycotoxins produced by mold fungi belong to hazard classes I and II. At the same time, a significant portion of these compounds remains outside the control zone, despite their potential threat to living organisms. This highlights the need for more thorough study and monitoring of such substances.Conclusions. The obtained data confirm the importance of developing and implementing modern systems for monitoring and regulating mycotoxins, especially for poorly studied and new compounds. The use of chemoinformatic methods makes it possible to effectively identify the most hazardous substances and focus efforts on their research, thereby enhancing food safety and reducing risks to human and animal health. |
| format | Article |
| id | doaj-art-59ea5dfa8a7c4e5a98099f8dde80ca32 |
| institution | DOAJ |
| issn | 2587-5728 |
| language | Russian |
| publishDate | 2025-05-01 |
| publisher | 27 Scientific Centre named after academician N.D. Zelinsky |
| record_format | Article |
| series | Вестник войск РХБ защиты |
| spelling | doaj-art-59ea5dfa8a7c4e5a98099f8dde80ca322025-08-20T02:56:20Zrus27 Scientific Centre named after academician N.D. ZelinskyВестник войск РХБ защиты2587-57282025-05-0191577310.35825/2587-5728-2025-9-1-57-73334New Methods for Pathogen Risk Assessment: Machine Learning in the Analysis of Toxicity Spectrum of <i>Albifimbria verrucaria</i>V. T. Tkachenko0M. V. Fedorov1V. V. Fedorova2A. V. Pozdeev3E. B. Kormanovskaya4A. S. Klimova5P. V. Gunina6A.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of SciencesA.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences ; Skolkovo Institute of Science and TechnologySkolkovo Institute of Science and TechnologyNuclear Biological Chemical Defence Military Academy Named after Marshal of the Soviet Union S.K. Timoshenko (Kostroma) of the Ministry of Defence of the Russian FederationNuclear Biological Chemical Defence Military Academy Named after Marshal of the Soviet Union S.K. Timoshenko (Kostroma) of the Ministry of Defence of the Russian FederationNuclear Biological Chemical Defence Military Academy Named after Marshal of the Soviet Union S.K. Timoshenko (Kostroma) of the Ministry of Defence of the Russian FederationNuclear Biological Chemical Defence Military Academy Named after Marshal of the Soviet Union S.K. Timoshenko (Kostroma) of the Ministry of Defence of the Russian FederationHighlightsThe use of artificial intelligence has great potential for predicting the toxic properties of new little-studied chemical compounds, reducing the time and financial costs associated with identifying the risks of possible threats.Relevance. Mycotoxins, which are secondary metabolites of mold fungi, represent one of the most significant factors of chronic risk associated with food products. Their danger exceeds the threat posed by synthetic pollutants, plant toxins, food additives, and pesticide residues. However, for many mycotoxins, the full toxicological profile has not yet been established, and traditional analysis methods remain labor-intensive, costly, and insufficiently effective. This makes the search for new approaches to assess their danger and control highly relevant.Purpose of the study is to study the toxicological profile of mycotoxins produced by the pathogenic fungus Albifimbria verrucaria and to determine their level of danger using chemoinformatics and machine learning.Study base sources. Analysis of scientific literature available through open Russian and English-language Internet resources.Method. In silico methods were applied to analyze the toxicological profile of mycotoxins, enabling the identification of high-risk compounds. These methods prioritize substances for further in-depth toxicological assessment, significantly reducing the time and resources required for research.Results and Discussion. The study results showed that approximately 50% of mycotoxins produced by mold fungi belong to hazard classes I and II. At the same time, a significant portion of these compounds remains outside the control zone, despite their potential threat to living organisms. This highlights the need for more thorough study and monitoring of such substances.Conclusions. The obtained data confirm the importance of developing and implementing modern systems for monitoring and regulating mycotoxins, especially for poorly studied and new compounds. The use of chemoinformatic methods makes it possible to effectively identify the most hazardous substances and focus efforts on their research, thereby enhancing food safety and reducing risks to human and animal health.https://www.nbsprot.ru/jour/article/view/395aflotoxinalbifimbria verrucariachemoinformaticsin silico toxicity assessmentmachine learningmycotoxinsmyrothecium verrucariapredictive toxicologyroridintrichothecene mycotoxinverrucarin |
| spellingShingle | V. T. Tkachenko M. V. Fedorov V. V. Fedorova A. V. Pozdeev E. B. Kormanovskaya A. S. Klimova P. V. Gunina New Methods for Pathogen Risk Assessment: Machine Learning in the Analysis of Toxicity Spectrum of <i>Albifimbria verrucaria</i> Вестник войск РХБ защиты aflotoxin albifimbria verrucaria chemoinformatics in silico toxicity assessment machine learning mycotoxins myrothecium verrucaria predictive toxicology roridin trichothecene mycotoxin verrucarin |
| title | New Methods for Pathogen Risk Assessment: Machine Learning in the Analysis of Toxicity Spectrum of <i>Albifimbria verrucaria</i> |
| title_full | New Methods for Pathogen Risk Assessment: Machine Learning in the Analysis of Toxicity Spectrum of <i>Albifimbria verrucaria</i> |
| title_fullStr | New Methods for Pathogen Risk Assessment: Machine Learning in the Analysis of Toxicity Spectrum of <i>Albifimbria verrucaria</i> |
| title_full_unstemmed | New Methods for Pathogen Risk Assessment: Machine Learning in the Analysis of Toxicity Spectrum of <i>Albifimbria verrucaria</i> |
| title_short | New Methods for Pathogen Risk Assessment: Machine Learning in the Analysis of Toxicity Spectrum of <i>Albifimbria verrucaria</i> |
| title_sort | new methods for pathogen risk assessment machine learning in the analysis of toxicity spectrum of i albifimbria verrucaria i |
| topic | aflotoxin albifimbria verrucaria chemoinformatics in silico toxicity assessment machine learning mycotoxins myrothecium verrucaria predictive toxicology roridin trichothecene mycotoxin verrucarin |
| url | https://www.nbsprot.ru/jour/article/view/395 |
| work_keys_str_mv | AT vttkachenko newmethodsforpathogenriskassessmentmachinelearningintheanalysisoftoxicityspectrumofialbifimbriaverrucariai AT mvfedorov newmethodsforpathogenriskassessmentmachinelearningintheanalysisoftoxicityspectrumofialbifimbriaverrucariai AT vvfedorova newmethodsforpathogenriskassessmentmachinelearningintheanalysisoftoxicityspectrumofialbifimbriaverrucariai AT avpozdeev newmethodsforpathogenriskassessmentmachinelearningintheanalysisoftoxicityspectrumofialbifimbriaverrucariai AT ebkormanovskaya newmethodsforpathogenriskassessmentmachinelearningintheanalysisoftoxicityspectrumofialbifimbriaverrucariai AT asklimova newmethodsforpathogenriskassessmentmachinelearningintheanalysisoftoxicityspectrumofialbifimbriaverrucariai AT pvgunina newmethodsforpathogenriskassessmentmachinelearningintheanalysisoftoxicityspectrumofialbifimbriaverrucariai |