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Predicting Nanotoxicity across Materials Using Interpretable Machine Learning and Descriptors Based on the Periodic

Fang Liu^1,2,3, Jimin Zhu¹, Jing Zhang⁴

¹College of Animal Science, South China Agricultural University, Guangzhou 510642, China.

ACS Applied Materials & Interfaces

|December 24, 2025

Summary

Predicting engineered nanomaterial toxicity across different core compositions is challenging. This study introduces a machine learning framework using periodic table descriptors for accurate cross-material nanotoxicity prediction, aiding safer material design.

Keywords:

cross-material relationships elemental properties nanobio interactions nanodescriptors quantitative nanostructure−activity relationships

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Area of Science:

Nanotechnology
Materials Science
Computational Toxicology

Background:

Biological effects of engineered nanomaterials depend on physicochemical properties.
Predicting nanotoxicity across diverse material compositions is a significant challenge.
Existing models often fail to generalize to new nanomaterials.

Purpose of the Study:

Develop an interpretable machine learning framework for cross-material nanotoxicity prediction.
Utilize periodic table-based descriptors for enhanced predictive accuracy.
Establish a computational tool for safer nanomaterial design and risk assessment.

Main Methods:

Constructed a dataset of 1206 metal oxide nanoparticle entries with cytotoxicity data.
Evaluated machine learning models using within-material and cross-material validation.
Incorporated elemental descriptors (electronegativity, ionization energy, atomic radius, oxidation state).

Main Results:

Elemental descriptors significantly improved cross-material prediction performance (R² 0.35–0.65 for unseen materials).
Experimental validation confirmed model reliability for NiO and Cr₂O₃ nanoparticles.
Identified material- and cell-type-specific toxic responses and mechanistic insights (oxidative stress).

Conclusions:

Cross-material nanotoxicity prediction is feasible using elemental descriptors.
The developed framework is scalable and interpretable for nanomaterial safety.
This approach supports informed design and risk assessment of engineered nanomaterials.