Supporting Information
| Supporting Information File 1: Detailed information regarding heavy metals at different concentrations. | ||
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Cite the Following Article
Prediction of cytotoxicity of heavy metals adsorbed on nano-TiO2 with periodic table descriptors using machine learning approaches
Joyita Roy, Souvik Pore and Kunal Roy
Beilstein J. Nanotechnol. 2023, 14, 939–950.
https://doi.org/10.3762/bjnano.14.77
How to Cite
Roy, J.; Pore, S.; Roy, K. Beilstein J. Nanotechnol. 2023, 14, 939–950. doi:10.3762/bjnano.14.77
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Scholarly Works
- Pore, S.; Roy, K. "intelligent Read Across (iRA)"- A tool for read-across-based toxicity prediction of nanoparticles. Computational and structural biotechnology journal 2025, 29, 186–200. doi:10.1016/j.csbj.2025.07.032
- Kumar, A.; Roy, J.; Ojha, P. K. The first report on chronic toxicity assessment of metals towards Ceriodaphnia dubia using QSTR technique: A step towards healthier and safer human health and eco-system. Computational Toxicology 2025, 34, 100357. doi:10.1016/j.comtox.2025.100357
- Chen, S.; Fan, T.; Ren, T.; Zhang, N.; Zhao, L.; Zhong, R.; Sun, G. High-throughput prediction of oral acute toxicity in Rat and Mouse of over 100,000 polychlorinated persistent organic pollutants (PC-POPs) by interpretable data fusion-driven machine learning global models. Journal of hazardous materials 2024, 480, 136295. doi:10.1016/j.jhazmat.2024.136295
- Roy, J.; Roy, K. Insights into nanoparticle toxicity against aquatic organisms using multivariate regression, read-across, and ML algorithms: Predictive models for Daphnia magna and Danio rerio. Aquatic toxicology (Amsterdam, Netherlands) 2024, 276, 107114. doi:10.1016/j.aquatox.2024.107114
- Pore, S.; Roy, K. Insights into pharmacokinetic properties for exposure chemicals: predictive modelling of human plasma fraction unbound (fu) and hepatocyte intrinsic clearance (Clint) data using machine learning. Digital Discovery 2024, 3, 1852–1877. doi:10.1039/d4dd00082j
