Predicting cytotoxicity of engineered nanoparticles using regularized regression models: an in silico approach
Valeriano A. Bondaug F. Ebardo I. Almonte P. Sabugaa M.A. Bagnol J.R. Latayada M.J. Macalalag J.M. Paradero B.D. Mayes M. Balanay M. Alguno A. Capangpangan R.
2023Taylor and Francis Ltd.
SAR and QSAR in Environmental Research
2023#34Issue 7591 - 604 pp.
The widespread application of engineered nanoparticles (NPs) in various industries has demonstrated their effectiveness over the years. However, modifications to NPs’ physicochemical properties can lead to toxicological effects. Therefore, understanding the toxicity behaviour of NPs is crucial. In this paper, regularized regression models, such as ridge, LASSO, and elastic net, were constructed to predict the cytotoxicity of various engineered NPs. The dataset utilized in this study was compiled from several journals published between 2010 and 2022. Data exploration revealed missing values, which were addressed through listwise deletion and kNN imputation, resulting in two complete datasets. The ridge, LASSO, and elastic net models achieved F1 scores ranging from 91.81% to 92.65% during internal validation and 92.89% to 93.63% during external validation on Dataset 1. On Dataset 2, the models attained F1 scores between 92.16% and 92.43% during internal validation and 92% and 92.6% during external validation. These results indicate that the developed models effectively generalize to unseen data and demonstrate high accuracy in classifying cytotoxicity levels. Furthermore, the cell type, material, cell source, cell tissue, synthesis method, and coat or functional group were identified as the most important descriptors by the three models across both datasets.
classification task , cytotoxicity , in silico , Nanoparticles , regularized regression
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Research on Environment and Nanotechnology Laboratories, Research Division, Mindanao State University at Naawan, Naawan, Philippines
Department of Science and Technology, Science Education Institute, Taguig City, Philippines
Department of Mathematics and Statistics, University of Southeastern Philippines, Davao City, Philippines
Department of Mathematics, Caraga State University, Butuan City, Philippines
Information, Communication and Technology Center, Mindanao State University at Naawan, Naawan, Philippines
Department of Chemistry and Biochemistry, University of Massachusetts, Dartmouth, NH, United States
Department of Chemistry, Nazarbayev University, Nur-Sultan, Kazakhstan
Department of Physics, Mindanao State University-Iligan Institute of Technology, Iligan City, Philippines
College of Marine and Allied Sciences, Mindanao State University at Naawan, Naawan, Philippines
Research on Environment and Nanotechnology Laboratories
Department of Science and Technology
Department of Mathematics and Statistics
Department of Mathematics
Information
Department of Chemistry and Biochemistry
Department of Chemistry
Department of Physics
College of Marine and Allied Sciences
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