School of Marine and Environmental Science, Tianjin Marine Environmental Protection and Restoration Technology Engineering Center, Tianjin University of Science and Technology, Tianjin, PR China.
School of Chemical Engineering and Material Science, Tianjin University of Science and Technology, Tianjin, PR China.
SAR QSAR Environ Res. 2023 Feb;34(2):147-161. doi: 10.1080/1062936X.2023.2171478. Epub 2023 Feb 7.
Quantitative structure-activity relationship (QSAR) is important for safe, rapid and effective risk assessment of chemicals. In this study, two QSAR models were established with 1230 chemicals to predict toxicity towards using multiple linear regression (MLR) method. The topological(T)-QSAR model was developed by using topological-norm descriptors generated from the topological structure, and the spatial(S)-QSAR model were built with spatial-norm descriptors obtained from the three-dimensional structure of molecules and topological-norm descriptors. The and are 0.8304 and 0.8338 for the T-QSAR model, and 0.8485 and 0.8585 for the S-QSAR model, which means that T-QSAR model and S-QSAR model can be used to predict toxicity quickly and accurately. In addition, we also conducted validation on the developed models. Satisfying validation results and statistical parameters demonstrated that QSAR models based on the topological-norm descriptors and spatial-norm descriptors proposed in this paper could be further utilized to estimate the toxicity of chemicals towards .
定量构效关系（QSAR）对于化学品的安全、快速和有效风险评估非常重要。在这项研究中，我们使用多元线性回归（MLR）方法，建立了两个包含 1230 种化合物的 QSAR 模型，以预测对 的毒性。拓扑（T）-QSAR 模型是通过使用拓扑结构生成的拓扑范数描述符来开发的，而空间（S）-QSAR 模型则是通过使用分子的三维结构和拓扑范数描述符获得的空间范数描述符来构建的。T-QSAR 模型和 S-QSAR 模型的 和 分别为 0.8304 和 0.8338，和 分别为 0.8485 和 0.8585，这意味着 T-QSAR 模型和 S-QSAR 模型可以快速准确地预测毒性。此外，我们还对所开发的模型进行了验证。令人满意的验证结果和统计参数表明，本文提出的基于拓扑范数描述符和空间范数描述符的 QSAR 模型可以进一步用于估计化学品对 的毒性。
