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.

摘要

纳米粒子已广泛应用于不同的科学领域。由于纳米粒子可能对环境或生物系统造成破坏性影响，因此其毒性评价是研究纳米材料安全性的关键阶段。与此同时，用于评估各种纳米粒子毒性的实验方法既昂贵又耗时。因此，人工智能 (AI) 等替代技术可能对预测纳米粒子毒性很有价值。因此，在这篇综述中，对人工智能工具进行了纳米材料毒性评估的研究。为此，对 PubMed、Web of Science 和 Scopus 数据库进行了系统检索。根据预先定义的纳入和排除标准纳入或排除文章，并排除重复研究。最后，纳入了 26 项研究。大多数研究是针对金属氧化物和金属纳米粒子进行的。此外，随机森林 (RF) 和支持向量机 (SVM) 在纳入的研究中使用频率最高。大多数模型都表现出可接受的性能。总的来说，人工智能可以为评估纳米粒子的毒性提供一个强大、快速和低成本的工具。