Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO2 with periodic table descriptors using machine learning approaches
Nanoparticles with their unique features have attracted researchers over the past decades. Heavy metals, upon release and emission, may interact with different environmental components, which may lead to co-exposure to living organisms. Nanoscale titanium dioxide (nano-TiO2) can adsorb heavy metals....
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| Format: | Article |
| Language: | English |
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Beilstein-Institut
2023-09-01
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| Series: | Beilstein Journal of Nanotechnology |
| Subjects: | |
| Online Access: | https://doi.org/10.3762/bjnano.14.77 |
| _version_ | 1797668564163887104 |
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| author | Joyita Roy Souvik Pore Kunal Roy |
| author_facet | Joyita Roy Souvik Pore Kunal Roy |
| author_sort | Joyita Roy |
| collection | DOAJ |
| description | Nanoparticles with their unique features have attracted researchers over the past decades. Heavy metals, upon release and emission, may interact with different environmental components, which may lead to co-exposure to living organisms. Nanoscale titanium dioxide (nano-TiO2) can adsorb heavy metals. The current idea is that nanoparticles (NPs) may act as carriers and facilitate the entry of heavy metals into organisms. Thus, the present study reports nanoscale quantitative structure–activity relationship (nano-QSAR) models, which are based on an ensemble learning approach, for predicting the cytotoxicity of heavy metals adsorbed on nano-TiO2 to human renal cortex proximal tubule epithelial (HK-2) cells. The ensemble learning approach implements gradient boosting and bagging algorithms; that is, random forest, AdaBoost, Gradient Boost, and Extreme Gradient Boost were constructed and utilized to establish statistically significant relationships between the structural properties of NPs and the cause of cytotoxicity. To demonstrate the predictive ability of the developed nano-QSAR models, simple periodic table descriptors requiring low computational resources were utilized. The nano-QSAR models generated good R2 values (0.99–0.89), Q2 values (0.64–0.77), and Q2F1 values (0.99–0.71). Thus, the present work manifests that ML in conjunction with periodic table descriptors can be used to explore the features and predict unknown compounds with similar properties. |
| first_indexed | 2024-03-11T20:31:11Z |
| format | Article |
| id | doaj.art-4bb1e22825bf40118a1b57575649a7f5 |
| institution | Directory Open Access Journal |
| issn | 2190-4286 |
| language | English |
| last_indexed | 2024-03-11T20:31:11Z |
| publishDate | 2023-09-01 |
| publisher | Beilstein-Institut |
| record_format | Article |
| series | Beilstein Journal of Nanotechnology |
| spelling | doaj.art-4bb1e22825bf40118a1b57575649a7f52023-10-02T08:42:17ZengBeilstein-InstitutBeilstein Journal of Nanotechnology2190-42862023-09-0114193995010.3762/bjnano.14.772190-4286-14-77Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO2 with periodic table descriptors using machine learning approachesJoyita Roy0Souvik Pore1Kunal Roy2Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India Drug Theoretics and Cheminformatics Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700032, India Nanoparticles with their unique features have attracted researchers over the past decades. Heavy metals, upon release and emission, may interact with different environmental components, which may lead to co-exposure to living organisms. Nanoscale titanium dioxide (nano-TiO2) can adsorb heavy metals. The current idea is that nanoparticles (NPs) may act as carriers and facilitate the entry of heavy metals into organisms. Thus, the present study reports nanoscale quantitative structure–activity relationship (nano-QSAR) models, which are based on an ensemble learning approach, for predicting the cytotoxicity of heavy metals adsorbed on nano-TiO2 to human renal cortex proximal tubule epithelial (HK-2) cells. The ensemble learning approach implements gradient boosting and bagging algorithms; that is, random forest, AdaBoost, Gradient Boost, and Extreme Gradient Boost were constructed and utilized to establish statistically significant relationships between the structural properties of NPs and the cause of cytotoxicity. To demonstrate the predictive ability of the developed nano-QSAR models, simple periodic table descriptors requiring low computational resources were utilized. The nano-QSAR models generated good R2 values (0.99–0.89), Q2 values (0.64–0.77), and Q2F1 values (0.99–0.71). Thus, the present work manifests that ML in conjunction with periodic table descriptors can be used to explore the features and predict unknown compounds with similar properties.https://doi.org/10.3762/bjnano.14.77heavy metalshk-2 cellml algorithmperiodic table descriptorsqsar |
| spellingShingle | Joyita Roy Souvik Pore Kunal Roy Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO2 with periodic table descriptors using machine learning approaches Beilstein Journal of Nanotechnology heavy metals hk-2 cell ml algorithm periodic table descriptors qsar |
| title | Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO2 with periodic table descriptors using machine learning approaches |
| title_full | Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO2 with periodic table descriptors using machine learning approaches |
| title_fullStr | Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO2 with periodic table descriptors using machine learning approaches |
| title_full_unstemmed | Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO2 with periodic table descriptors using machine learning approaches |
| title_short | Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO2 with periodic table descriptors using machine learning approaches |
| title_sort | prediction of cytotoxicity of heavy metals adsorbed on nano tio2 with periodic table descriptors using machine learning approaches |
| topic | heavy metals hk-2 cell ml algorithm periodic table descriptors qsar |
| url | https://doi.org/10.3762/bjnano.14.77 |
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