The Quantitative Structure-Activity Relationship (QSAR) has been used to investigate organic mixtures but QSAR in the nanomaterial field (QNAR) is still new. Toxicity is a result of the interaction of many substances. QNAR research focuses on a single nanomaterial in the long-term. It is difficult to find an appropriate descriptor to build a model due to the complexity of the mixture. Here, we attempt to build a QNAR model to predict cell viability for HK-2 cells exposed to a mixture containing nano-TiO₂ and heavy metals. HK-2 cells were exposed to four groups of mixtures containing heavy-metals and nanomaterials and CCK8 was added to obtain the number of living cells. At the same time, ROS was investigated to study this mechanism. Each descriptor of the components and mixtures were obtained using the formula D_mix=$\sum _{i=1}^n{D_{i}x}_i$ respectively. We used the Multiple Partial Least Squares Regression (PLS) and Random Forest Regression (RF) to build a QNAR model. Both models reliably predict and assess viability of HK-2 cells exposed to the mixture. The RF model showed greater stability and higher precision in toxicity predictability and can be applied to environmental nano-toxicology.

定量结构-活性关系（QSAR）已用于研究有机混合物，但纳米材料领域（QNAR）中的QSAR仍然是新的。毒性是许多物质相互作用的结果。QNAR的研究长期集中于单一纳米材料。由于混合物的复杂性，很难找到合适的描述符来建立模型。在这里，我们试图建立一个QNAR模型来预测暴露于含有纳米TiO ₂和重金属的混合物的HK-2细胞的细胞活力。将HK-2细胞暴露于四组含有重金属和纳米材料的混合物中，并加入CCK8以获得活细胞的数量。同时，对ROS进行了研究以研究这种机理。使用以下公式获得组分和混合物的每个描述符D_混合=$\sum _{\mathrm{一世}=\mathrm{1个}}^{ñ}{d_{\mathrm{一世}}X}_{\mathrm{一世}}$分别。我们使用多元偏最小二乘回归（PLS）和随机森林回归（RF）来建立QNAR模型。两种模型都能可靠地预测和评估暴露于混合物中的HK-2细胞的活力。RF模型在毒性可预测性方面显示出更高的稳定性和更高的精度，可以应用于环境纳米毒理学。