nbsprotВестник войск РХБ защитыJournal of NBC Protection Corps2587-57283034-279127 Научный центр10.35825/2587-5728-2025-9-1-57-73aysnnqnbsprot-395Research ArticleПРИМЕНЕНИЕ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА ДЛЯ ЗАЩИТЫ ОТ ОРУЖИЯ МАССОВОГО ПОРАЖЕНИЯARTIFICIAL INTELLIGENCE FOR DEFENSE AGAINST WEAPONS OF MASS DESTRUCTIONНовые методы оценки рисков патогенов: машинное обучение в анализе спектра токсичности Albifimbria verrucariaNew Methods for Pathogen Risk Assessment: Machine Learning in the Analysis of Toxicity Spectrum of Albifimbria verrucariaТкаченкоВ. Т.TkachenkoV. T.

Ткаченко Варвара Тарасовна. Аспирант. 

127051, г. Москва, Большой Каретный пер., д. 19, стр. 1 

Varvara T. Tkachenko. Postgraduate. 

Bolshoy Karetny per., 19, bld 1, 127051 Moscow 

ФедоровМ. В.FedorovM. V.

Федоров Максим Валериевич. И.о. директора, канд. физ.-мат. наук, д-р хим. наук, член-корреспондент РАН. 

127051, г. Москва, Большой Каретный пер., д. 19, стр. 1 

121205, г. Москва, инновационный центр «Сколково», Большой бульвар, д. 30, стр. 1

Maxim V. Fedorov. Acting Director. Cand. Sci. (Phys-Math.). Dr Sci. (Chem.). Corresponding Member, Russian Academy of Sciences. 

Bolshoy Karetny per., 19, bld 1, 127051 Moscow 

121205 Moscow 

ФедороваВ. В.FedorovaV. V.

Федорова Виктория Викторовна. Научный сотрудник, канд. биол. наук. 

121205, г. Москва, инновационный центр «Сколково», Большой бульвар, д. 30, стр. 1 

Victoria V. Fedorova. Researcher. Cand. Sci. (Biol.). 

Bolshoy Boulevard, 30, bld. 1, Moscow 121205

ПоздеевА. В.PozdeevA. V.

Поздеев Александр Владимирович. Профессор, д-р биол. наук. 

156015, г. Кострома, ул. Горького, д. 16 

Aleksandr V. Pozdeev. Professor. Dr Sci. (Biol.). 

Gorky Street 16, Kostroma 156015 

varhbz@mil.ruКормановскаяЕ. Б.KormanovskayaE. B.

Кормановская Елена Борисовна. Старший научный сотрудник, канд. биол. наук, доцент. 

156015, г. Кострома, ул. Горького, д. 16 

Elena B. Kormanovskaya. Senior Researcher. Cand. Sci. (Biol.). Assistant Professor. 

Gorky Street 16, Kostroma 156015 

КлимоваА. С.KlimovaA. S.

Климова Алена Сергеевна. Младший научный сотрудник. 

156015, г. Кострома, ул. Горького, д. 16 

Alena S. Klimova. Junior Researcher

Gorkogo Street, 16, Kostroma 156015 

ГунинаП. В.GuninaP. V.

Гунина Полина Владимировна. Младший научный сотрудник. 

156015, г. Кострома, ул. Горького, д. 16 

Polina V. Gunina. Junior Researcher 

Gorky Street 16, Kostroma 156015 

Федеральное государственное бюджетное учреждение науки «Институт проблем передачи информации имени А.А. Харкевича» Российской академии наукРоссияA.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of SciencesRussian FederationФедеральное государственное бюджетное учреждение науки «Институт проблем передачи информации имени А.А. Харкевича» Российской академии наук ; Автономная некоммерческая образовательная организация высшего образования НО ВО «Сколковский институт науки и технологий»РоссияA.A. Kharkevich Institute for Information Transmission Problems, Russian Academy of Sciences ; Skolkovo Institute of Science and TechnologyRussian FederationАвтономная некоммерческая образовательная организация высшего образования НО ВО «Сколковский институт науки и технологий»РоссияSkolkovo Institute of Science and TechnologyRussian FederationФедеральное государственное казенное военное образовательное учреждение высшего образования «Военная академия радиационной, химической и биологической защиты имени Маршала Советского Союза С.К. Тимошенко (г. Кострома)» Министерства обороны Российской ФедерацииРоссияNuclear Biological Chemical Defence Military Academy Named after Marshal of the Soviet Union S.K. Timoshenko (Kostroma) of the Ministry of Defence of the Russian FederationRussian Federation202518052025915773Copyright © Ткаченко В.Т., Федоров М.В., Федорова В.В., Поздеев А.В., Кормановская Е.Б., Климова А.С., Гунина П.В., 20252025Ткаченко В.Т., Федоров М.В., Федорова В.В., Поздеев А.В., Кормановская Е.Б., Климова А.С., Гунина П.В.Tkachenko V.T., Fedorov M.V., Fedorova V.V., Pozdeev A.V., Kormanovskaya E.B., Klimova A.S., Gunina P.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.nbsprot.ru/jour/article/view/395Основные моменты

Основные моменты

Применение искусственного интеллекта имеет большой потенциал для прогнозирования токсических свойств новых малоизученных химических соединений, позволяет сократить время и финансовые затраты, связанные с определением рисков возможных угроз.

Актуальность

Актуальность. Микотоксины, являющиеся вторичными метаболитами плесневых грибов, представляют собой один из наиболее значимых факторов хронического риска, связанного с пищевыми продуктами. Их опасность при заражении может превышать угрозу, исходящую от синтетических загрязнителей, растительных токсинов и остатков агрохимикатов и удобрений. Однако для многих микотоксинов до сих пор не установлен полный токсикологический профиль в силу того, что традиционные методы экспериментального анализа остаются трудоемкими, дорогостоящими и, порой, малоэффективными. Это делает актуальным поиск новых подходов для оценки их опасности и контроля.

Цель исследования – оценка рисков патогенов путем машинного обучения в анализе спектра токсичности Albifimbria verrucaria.

Источниковая база исследования. Научная литература, доступная через открытые отечественные и англоязычные ресурсы сети Интернет.

Метод исследования

Метод исследования. Для анализа токсикологического профиля микотоксинов были применены методы in silico, основанные на машинном обучении, позволяющие идентифицировать соединения высокого класса опасности. Эти методы обеспечивают приоритезацию веществ для дальнейшей углубленной токсикологической оценки, что значительно сокращает время и ресурсы, необходимые для исследований.

Результаты и обсуждение

Результаты и обсуждение. Проводилось изучение токсикологического профиля микотоксинов, продуцируемых патогенным грибом Albifimbria verrucaria, и определение уровня их опасности с использованием хемоинформатики и машинного обучения. Результаты исследования показали, что около 50 % микотоксинов, вырабатываемых плесневым грибом, можно отнести к I и II классам опасности. При этом значительная часть этих соединений остается сейчас вне зоны контроля, несмотря на их потенциальную угрозу для людей и животных. Это подчеркивает необходимость более тщательного изучения и мониторинга таких веществ.

Выводы

Выводы. Полученные данные подтверждают важность разработки и внедрения современных систем мониторинга и регулирования микотоксинов, особенно в отношении малоизученных и новых соединений. Использование хемоинформатических методов позволяет эффективно выявлять наиболее опасные вещества и сосредоточить усилия на их исследовании, что способствует повышению безопасности пищевых продуктов и снижению рисков для здоровья человека и животных.

Highlights

Highlights

The 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

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

Study base sources. Analysis of scientific literature available through open Russian and English-language Internet resources.

Method

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

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

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.

Albifimbria verrucariaMyrothecium verrucariaафлатоксинверрукаринмашинное обучениемикотоксиныоценка токсичности in silicoпредиктивная токсикологияроридинтрихотеценовые микотоксиныхемоинформатикаAflotoxinAlbifimbria verrucariachemoinformaticsin silico toxicity assessmentmachine learningmycotoxinsMyrothecium verrucariapredictive toxicologyRoridintrichothecene mycotoxinVerrucarinReferencesRestrepo G. Chemical space: limits, evolution and modelling of an object bigger than our universal library. Digital Discovery. 2022;1(5):568–85. EDN:GZRUYT https://doi.org/10.1039/D2DD00030JRestrepo G. Chemical space: limits, evolution and modelling of an object bigger than our universal library. Digital Discovery. 2022;1(5):568–85. EDN:GZRUYT https://doi.org/10.1039/D2DD00030JLeal W, Llanos Eugenio J, Bernal A, Restrepo G. The expansion of chemical space in 1826 and in the 1840s prompted the convergence to the periodic system. Proceedings of the National Academy of Sciences. 2022;119(30):e2119083119. 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The authors declare that there are no conflicts of interest present.