The toxicity assessment of ionic liquids (ILs) towards the environment and living organisms has received great attention. Nevertheless, the huge number of ILs makes the toxicity data collection expensive and time-consuming, which motivates modeling development to fill data gaps of ILs toxicity. The group contribution (GC) method has been extensively applied for the estimation of various properties of ionic liquids. This study proposed a novel method, named the non-integer group contribution (NGC) method, which creatively utilizes the atomic electrostatic potential descriptors for modeling. Specifically, the average values of electrostatic potential (AV_EP) and the electrostatic potential surface area (S_EP) of atoms in the cations and anions of ILs were calculated and used to obtain the group descriptors in this work. Two NGC models were developed to predict the toxicity of ILs. Results show that both proposed models have satisfactory predictability. In contrast, the NGC-2 model based on S_EP descriptors exhibits better predictability due to its higher coefficient of determination (R²=0.927), the lower average absolute relative deviation (AARD =11.257%) and root mean square error (RMSE=0.261) for the entire dataset. The NGC-2 model also shows better performance than the traditional GC method, demonstrating its advanced superiority. Therefore, the proposed approach has high potential in terms of generalization and applicability for predicting the property of compounds.

离子液体（ILs）对环境和生物体的毒性评估受到了极大的关注。然而，大量的 ILs 使得毒性数据收集既昂贵又耗时，这促使建模开发来填补 ILs 毒性的数据空白。基团贡献（GC）方法已广泛应用于离子液体各种性质的估计。本研究提出了一种新颖的方法，称为非整数群贡献（NGC）方法，该方法创造性地利用原子静电势描述符进行建模。具体而言，静电势 ( AV _EP ) 和静电势表面积 ( S _EP )的平均值) 计算 IL 的阳离子和阴离子中的原子数，并用于获得本工作中的基团描述符。开发了两个 NGC 模型来预测 ILs 的毒性。结果表明，两种提出的模型都具有令人满意的可预测性。相比之下，基于S _EP描述符的 NGC-2 模型由于其较高的决定系数 ( R ²=0.927)，整个数据集的平均绝对相对偏差 (AARD =11.257%) 和均方根误差 (RMSE=0.261) 较低。NGC-2 模型也表现出比传统 GC 方法更好的性能，显示了其先进的优越性。因此，所提出的方法在预测化合物性质的泛化和适用性方面具有很高的潜力。