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Journals / Journal of the Turkish Chemical Society Section A: Chemistry / 2026 / Volume: 2026 Issue: 2

QSAR Modeling of Metal Oxide Nanoparticle Toxicity Based on Atomic Properties and Molecular Topology

Zubainun Mohamed Zabidi(Universiti Teknologi MARA)0000-0001-5927-7037
Nurul Aimi Zakaria(Universiti Teknologi MARA)0000-0002-3436-5441
Ahmad Nazib Alias(Universiti Teknologi MARA)0000-0001-9263-8092
Pages
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Publication Date
2026-10-03
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Comparative analysis demonstrated that models based on simpler molecular descriptors offered improved robustness and computational efficiency compared to more complex descriptors used in other studies.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Nanomaterials, particularly metal-oxide nanoparticles (NPs), are widely used in various technological applications such as electronics, optoelectronics, pharmacy, medicine, cosmetics, and textiles due to their versatile surface functions.
📊
Key Findings & Data:These NPs can enter the human body through various routes and may have unforeseen negative effects, requiring further research into their toxicity.
💡
Conclusion & Impact:Comparative analysis demonstrated that models based on simpler molecular descriptors offered improved robustness and computational efficiency compared to more complex descriptors used in other studies.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:metal oxide nanoparticle · Quantitative structure activity relationship · toxicity

Abstract

Nanomaterials, particularly metal-oxide nanoparticles (NPs), are widely used in various technological applications such as electronics, optoelectronics, pharmacy, medicine, cosmetics, and textiles due to their versatile surface functions. These NPs can enter the human body through various routes and may have unforeseen negative effects, requiring further research into their toxicity. Developing safe and green nanomaterials requires understanding the physicochemical factors that govern their properties, which can be challenging to study experimentally. Thus, in silico methods such as Quantitative Structure-Activity Relationship (QSAR) can help reduce the burden of experimental testing while boosting the accuracy of harmful impact prediction. The use of atomic molecular descriptors in combination with other types of descriptors can improve the accuracy and comprehensiveness of QSAR models for metal-oxide nanoparticles. Comparative analysis demonstrated that models based on simpler molecular descriptors offered improved robustness and computational efficiency compared to more complex descriptors used in other studies.

🔖

Cite This Article

Zabidi, Z. M., Zakaria, N. A., Alias, A. N. (2026). QSAR Modeling of Metal Oxide Nanoparticle Toxicity Based on Atomic Properties and Molecular Topology. Journal of the Turkish Chemical Society Section A: Chemistry, Volume: 2026 Issue: 2. https://doi.org/10.18596/jotcsa.1898760

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  76. Akinola LK, Uzairu A, Shallangwa GA, Abechi SE. Development and validation of predictive quantitative structure–Activity relationship models for estrogenic activities of hydroxylated polychlorinated biphenyls. Environ Toxicol Chem. 2023 Apr 1;42(4):823–34. Available from:.
  77. Pathakoti K, Huang MJ, Watts JD, He X, Hwang HM. Using experimental data of Escherichia coli to develop a QSAR model for predicting the photo-induced cytotoxicity of metal oxide nanoparticles. J Photochem Photobiol B Biol. 2014 Jan;130:234–40. Available from:.
  78. Kar S, Gajewicz A, Puzyn T, Roy K, Leszczynski J. Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: A mechanistic QSTR approach. Ecotoxicol Environ Saf. 2014 Sep;107:162–9. Available from:.
  79. Pan Y, Li T, Cheng J, Telesca D, Zink JI, Jiang J. Nano-QSAR modeling for predicting the cytotoxicity of metal oxide nanoparticles using novel descriptors. RSC Adv. 2016;6(31):25766–75. Available from:.
  80. Zhivkova Z. Quantitative structure-pharmacokinetics relationship for plasma protein binding of neutral drugs. Int J Pharm Pharm Sci. 2018 Apr 1;10(4):88–93. Available from:.
  81. Alias AN, Zabidi ZM. QSAR studies on nitrobenzene derivatives using hyperpolarizability and conductor like screening model as molecular descriptors. J Turkish Chem Soc Sect A Chem. 2022 Aug 31;9(3):953–68. Available from:.