In silico the Ames mutagenicity predictive modelof environment
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Дата
2025
Науковий керівник
Назва журналу
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Назва тому
Видавець
Igor Sikorsky Kyiv Polytechnic Institute
Анотація
Background.The classical in vitroand in vivo methods developed and widelyused in the past decades to as-sess the genetic effects of environmental factors are complex in view of their implementation, are expensive, long-lasting, have the problem of reproducibility of the results of experiment in different laboratories and may face ethical problems of using warm-blooded animals in experiments. Objective. Development, optimisation and testing of effective in silicomodels for assessment of Ames muta-genicity of environmental factors.Methods. The genetic assessment of the impact of environmental factors was carried out in accordance with a set of chemical compounds for which information on potential mutagenic activity was obtained experimen-tally, using the in vitroAmes Salmonella/microsome test.Four machine learning models were developed to solve the problem of binary classification to form two classes of xenobiotics (mutagen/non-mutagen).The total sampleis represented by a set of 8,083 xenobiotics. Results. We developed four machine learning models with 85% accuracy, matchingthe reproducibility of Ames test data across laboratories. In addition, we have proposed a binary classifier that subject to dimen-sionality reduction of the input data, taking into account the qualitative composition of molecular descrip-tors, allows us toimprove the accuracy of in silicoprediction of genotoxicity of chemicals. Conclusions.The necessity of updating and expanding the list of effective and more productive methods and approaches for assessing the genotoxic effects of environmental factors is substantiated, which allows avoi-ding the use of warm-blooded animals in the experiment, saving time and reducing the number of false-negative and false-positive results. The possibility of increase the accuracy of predictive machine learning models forassessing the genotoxic potential of environmental factors in conditions of dimensionality reduc-tion of the data set is presented.
Опис
Ключові слова
mutation, genotoxicity, QSAR model, molecular descriptors, machine learning models, мутація, генотоксичність, QSAR-модель, молекулярні дескриптори, моделі машинного навчання
Бібліографічний опис
In silico the Ames mutagenicity predictive modelof environment / S. V. Kislyak, O. M. Duhan, R. V. Yesypenko, D. B. Starosyla, О. I. Yalovenko // Innovative Biosystems and Bioengineering : international scientific journal. – 2025. – Vol. 9, No. 2. – P. 42-52. – Bibliogr.: 51 ref.