化学品正辛醇空气分配系数定量预测模型研究

A Quantitative Structure-Activity Relationship Model for Prediction of Octyl Alcohol/Air Partition Coefficient of Organic Chemicals

正辛醇-空气分配系数是描述化学品在空气和环境有机相之间分配的一个关键参数,对化学品分配、迁移、转化规律和生态效应评价具有重要意义。采用量子化学方法对309个化合物进行结构优化,采用遗传算法筛选最优结构描述符,运用多元线性回归和神经网络算法构建化学品正辛醇-空气分配系数预测模型。模型方程表明影响化学品正辛醇-空气分配系数的3个参数为分子中非氢原子个数(Nsk)、3D-MoRSE描述符(Mor12u)、氮原子和氧原子数目(n HDon)。拟合结果显示,多元线性回归模型决定系数R2和标准误差分别为0.911和0.880,神经网络模型决定系数R2和均方根误差分别为0.839和0.830。基于杠杆(leverage)法评价模型的应用域,结果表明模型具有较强的稳健性、预测性和拟合能力。通过定量结构-活性关系(QSAR)预测技术可弥补正辛醇-空气分配系数测试数据的缺失,减少测试费用和评估数据的不确定性。

Octyl alcohol/air partition（ KOA） is an important parameter to describe distribution of organic chemicals in the air and environment and critical to evaluation of distribution,migration,transformation and ecological effects of chemicals.A total of 309 chemicals were structurally optimized with quantum chemistry methods and screened with the genetic algorithm for optimal descriptors. A quantitative structure-activity relationship（ QSAR） model of lg KOAwas developed using the multiple linear regression（ MLR） and neural network algorithm. The developed QSAR model indicates that the three Dragon descriptors（ Nsk,Mor12 u and n HDon） were the main factors affecting octyl alcohol/air partition coefficient of chemicals. The result of fitting with the QSAR model shows that the R2 and SE（ standard error） of the multiple linear regressions model was 0. 911 and 0. 880,respectively,while the R2 and RMSE（ root mean square error） of the neural network model was 0. 839 and 0. 830,respectively. Application domain of the QSAR model was evaluated with the leverage method. The evaluation shows that the model is quite high in goodness-of-fit,robustness and predictive ability. Therefore,the QSAR model can be used to make up the missing data of lg KOA,thus lowering the cost of testing and uncertainty of data evaluation.