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Employing Supervised Algorithms for the Prediction of Nanomaterial's Antioxidant Efficiency
Mahsa Mirzaei1, Irini Furxhi1,2, Finbarr Murphy1
1Department of Accounting and Finance, Kemmy Business School, University of Limerick, V94PH93 Limerick, Ireland.
International Journal of Molecular Sciences
|February 11, 2023
Summary
Machine learning models can predict nanomaterial antioxidant efficiency, aiding in managing oxidative stress. This approach utilizes nanomaterial properties to forecast their effectiveness in scavenging free radicals.
Area of Science:
- Nanotechnology
- Computational chemistry
- Biomedical engineering
Background:
- Excessive reactive oxygen species (ROS) cause oxidative stress and cellular damage.
- Nanomaterials (NMs) show potential for scavenging free radicals and mitigating oxidative stress.
- Predicting NM antioxidant efficiency is crucial for developing new therapies.
Approach:
- A comprehensive dataset of 62 in vitro studies was compiled.
- Physico-chemical (P-chem) properties, synthesis methods, and experimental conditions were extracted as input features.
- Machine learning (ML) regression models were trained to predict antioxidant efficiency using the DPPH assay.
Key Points:
- The random forest model achieved the highest predictive accuracy with R² = 0.83.
- Nanomaterial type, core size, and dosage were identified as key predictors of antioxidant efficiency.
- This study demonstrates ML's utility in predicting functional performance of NMs.
Conclusions:
- ML models can effectively forecast NM antioxidant efficiency, complementing traditional in vitro assays.
- The study highlights the need for comprehensive databases and improved data management in nanotechnology research.
- This work expands ML applications in nanotechnology beyond safety assessments to functional performance prediction.
