QSAR Modeling of Metal Oxide Nanoparticle Toxicity Based on Atomic Properties and Molecular Topology
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.
Keywords
- metal oxide nanoparticle
- Quantitative structure activity relationship
- toxicity
- Molecular descriptor
- atomic properties
Ethical Statement
Thanks
References
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Details
Primary Language
English
Subjects
Theoretical Quantum Chemistry
Journal Section
Research Article
Authors
Nurul Aimi Zakaria
0000-0002-3436-5441
Malaysia
Ahmad Nazib Alias
0000-0001-9263-8092
Malaysia
Publication Date
October 3, 2026
Submission Date
March 6, 2026
Acceptance Date
August 15, 2026
Published in Issue
Year 2026 Volume: 2026 Number: 2
