CAJ | 학술논문

本文建立了2个180个含苯基的羧酸类化合物酸碱解离常数(pKa)的定量预测模型.这些化合物分子量在122.12到288.34的范围内,包含H,C,N,O,S,F,Cl,Br及Ⅰ等元素.使用Cerius~2程序计算236个分子描述符来表述这些化合物,并使用统计学方法从中选择了12个描述符.分别使用多元线性回归分析(MLR)及支持向量机回归(SVM)结合10重交互检验方法来预测pKa数值.多元线性回归模型对pKa的预测结果相关系数为0.90,标准偏差为0.32;支持向量机模型结果较好,相关系数为0.91,标准偏差为0.31.
본문건립료2개180개함분기적최산류화합물산감해리상수(pKa)적정량예측모형.저사화합물분자량재122.12도288.34적범위내,포함H,C,N,O,S,F,Cl,Br급Ⅰ등원소.사용Cerius~2정서계산236개분자묘술부래표술저사화합물,병사용통계학방법종중선택료12개묘술부.분별사용다원선성회귀분석(MLR)급지지향량궤회귀(SVM)결합10중교호검험방법래예측pKa수치.다원선성회귀모형대pKa적예측결과상관계수위0.90,표준편차위0.32;지지향량궤모형결과교호,상관계수위0.91,표준편차위0.31.
Two quantitative models for the prediction of the pKa values of 180 aromatic carboxylic acids were developed. These compounds contain elements such as H, C, N, O, S, F, Cl, Br, and I with the molecular weight in the range of 122.12 to 288.34. The compounds were represented by 236 molecular descriptors calculated by using the Cerius~2 program. Twelve descriptors were selected by using the statistical methods. The pKa values were predicted by the Multilinear Regression (MLR) analysis and the Support Vector Machine (SVM) Regression in combination with 10-fold cross validation method. The model based on MLR analysis has a correlation coefficient of 0.90 for the predicted result of pKa, and the standard deviation of 0.32 pKa units, the model based on SVM regression has better result with a correlation coefficient of 0.91, and the standard deviation of 0.31 pKa units.