AI-Driven Nano-QSAR Framework for Predicting Carbon Nanotube Cytotoxicity: Overcoming High Dimensionality and Data Imbalance.

Authors

Ziyang Song, Fangxue Zhang, Yingying Zhou, Lizhi Shao, Jianhua Liu, Puxiang Lai, Xiazi Huang

Published in

International journal of nanomedicine. Volume 21. Pages 609758. Epub Sep 19, 2026.

Abstract

Reliable prediction of nanomaterial toxicity from physicochemical properties remains a critical challenge in nanosafety assessment, particularly when experimental datasets are small and mechanistic validation is limited. This study proposes an artificial intelligence (AI)-driven nano-QSAR framework for predicting carbon nanotube (CNT)-induced cytotoxicity by systematically linking the physicochemical descriptor patterns with experimentally measured toxicity outcomes.
A curated dataset containing cytotoxicity measurements for 80 CNTs in THP-1 cells was analyzed. Each CNT was represented by 2142 physicochemical descriptors covering structural, surface, oxidation-related, and electronic properties. Descriptor redundancy and effective dimensionality were systematically examined using correlation analysis and principal component analysis (PCA). Models were implemented in Python 3.13 using scikit-learn, CatBoost, XGBoost, Optuna, and imbalanced-learn. Multiple classifiers were benchmarked within an imbalance-aware machine-learning framework, and performance was evaluated using stratified five-fold cross-validation and an independent held-out test set.
The descriptor space was highly compressible, with the first two principal components explaining 99.25% of the total variance. Descriptor-level analyses indicated that SCNO-, CCOX-, and COOX-related descriptor families, reflecting surface chemistry, oxidation-related properties, and electronic structure, were major contributors to cytotoxicity-associated variation. Among the evaluated algorithms, CatBoost consistently exhibited the best balance between overall predictive performance and minority-class detection, achieving an F1-score of 0.9086 and a balanced accuracy of 0.9615 on the independent test set.
This study demonstrates that robust prediction of CNT cytotoxicity can be achieved through the integration of low-dimensional descriptor-space analysis, hybrid feature engineering, and imbalance-aware machine learning. The results further suggest that the apparent complexity of CNT descriptor space can be effectively reduced to a limited number of physicochemical dimensions associated with biological responses. By establishing a structured modeling framework that prioritizes hazard identification over retrospective curve-fitting, this approach functions as a conservative, early-stage screening tool. Although broader external validation remains necessary, the consistent importance of surface chemistry, oxidation-related characteristics, and electronic structure supports their central role in regulating nano-bio interactions and cytotoxic responses. These findings establish a practical foundation for AI-assisted nanosafety assessment and mechanism-informed safer-by-design development of carbon nanomaterials.

PMID:
42781618
Bibliographic data and abstract were imported from PubMed on 25 Sep 2026.

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