A novel method named two-level group contribution （GC-K） method for the estimation of octanol-water partition coefficient （Kow） of chloride hydrocarbon is presented. The equation includes only normal boiling point...A novel method named two-level group contribution （GC-K） method for the estimation of octanol-water partition coefficient （Kow） of chloride hydrocarbon is presented. The equation includes only normal boiling points and molecular weight of compounds. Group contribution parameters of 12 first-level groups and 7 second-level groups for Kow are obtained by correlating experimental data of three types including 57 compounds. By comparing the estimation results of the first-level with that of the two-level groups, it was observed that the latter is better with the addition of the modification of proximity effects. When compared with Marrero＇s three-level group contribution approach and atom-fragment contribution method （AFC）, the accuracy of the average relative error of GC-K by first-level groups is 7.20% and is preferred to other methods.展开更多
文摘A novel method named two-level group contribution （GC-K） method for the estimation of octanol-water partition coefficient （Kow） of chloride hydrocarbon is presented. The equation includes only normal boiling points and molecular weight of compounds. Group contribution parameters of 12 first-level groups and 7 second-level groups for Kow are obtained by correlating experimental data of three types including 57 compounds. By comparing the estimation results of the first-level with that of the two-level groups, it was observed that the latter is better with the addition of the modification of proximity effects. When compared with Marrero＇s three-level group contribution approach and atom-fragment contribution method （AFC）, the accuracy of the average relative error of GC-K by first-level groups is 7.20% and is preferred to other methods.
文摘采用定量结构性质相关(QSPR)方法,利用SEDs(Steric and Electronic Descriptors)建立了预测GC-RRT,Kow和Sw的QSPR模型,进行了交叉验证(包括Leave-one-out方法和Leave-more-out方法),并且对缺乏性质数据的PCBs进行了预测.
