论文部分内容阅读
基于量子化学方法建立了多溴代联苯醚(PBDEs)代谢产物的正辛醇-水分配系数(logKow)的预测模型,整个数据集包括19个羟基多溴代联苯醚(HO-PBDEs)和15个甲氧基多溴代联苯醚(MeO-PBDEs),logKow的数值跨越3个数量级(4.63~7.67)范围.所有化合物应用密度泛函理论(DFT)进行结构优化,在最优构型的基础之上计算分子极化率等6个量子化学描述符,并采用多元线性回归(MLR)建立模型.结果表明,分子极化率和氢原子最正净电荷是影响化合物在正辛醇相和水相之间分配的主要因素.模型具有良好的统计学性能,相关系数的平方r2=0.941,均方根误差rms=0.198.模拟外部验证和交叉验证表明模型具有良好的稳健性和预测能力,可用于同系列化合物logKow的预测.
Based on the quantum chemistry method, a prediction model of n-octanol-water partition coefficient (logKow) of metabolites of polybrominated diphenyl ethers (PBDEs) was established. The whole dataset includes 19 hydroxyl polybrominated diphenyl ethers (HO-PBDEs) And 15 methoxy polybrominated diphenyl ethers (MeO-PBDEs), the logKow values ranged from 3 orders of magnitude (4.63 ~ 7.67) .All the compounds were optimized by density functional theory (DFT) Based on which six quantum chemical descriptors such as molecular polarizability were calculated and multivariate linear regression (MLR) was used to establish the model.The results showed that the molecular polarizability and the most positive net charge of hydrogen atom were the most important factors influencing the activity of compounds in n-octanol Phase and aqueous phase.The model has a good statistical performance, the correlation coefficient of the square r2 = 0.941, root mean square error rms = 0.198. Simulated external verification and cross-validation show that the model has good robustness and prediction The ability to predict logKow for the same series of compounds.