Predicting the toxicity of nanoparticles using artificial intelligence tools: a systematic review

Abstract

Nanoparticles have been used extensively in different scientific fields. Due to the possible destructive effects of nanoparticles on the environment or the biological systems, their toxicity evaluation is a crucial phase for studying nanomaterial safety. In the meantime, experimental approaches for toxicity assessment of various nanoparticles are expensive and time-consuming. Thus, an alternative technique, such as artificial intelligence (AI), could be valuable for predicting nanoparticle toxicity. Therefore, in this review, the AI tools were investigated for the toxicity assessment of nanomaterials. To this end, a systematic search was performed on PubMed, Web of Science, and Scopus databases. Articles were included or excluded based on pre-defined inclusion and exclusion criteria, and duplicate studies were excluded. Finally, twenty-six studies were included. The majority of the studies were conducted on metal oxide and metallic nanoparticles. In addition, Random Forest (RF) and Support Vector Machine (SVM) had the most frequency in the included studies. Most of the models demonstrated acceptable performance. Overall, AI could provide a robust, fast, and low-cost tool for the evaluation of nanoparticle toxicity.

Keywords:

• Nanoparticles
• nanomaterials
• artificial intelligence
• toxicity
• safety

Acknowledgements

The authors thank all the participants in the study.

Ethics approval and consent to participate

Not applicable.

Author contributions

ABY, SMA and MA conceived the idea for the review. ABY, SMA, HM, and MG were involved in the study selection, quality assessment, and data extraction. ABY, and SMA conducted the statistical analysis. ABY, SMA, HM, MA, and NM wrote the first draft of the manuscript. All authors reviewed the manuscript, contributed to critical changes, and approved the final version of the manuscript for submission.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All data generated or analyzed during this study are included in this published article.