物理化学学报Issue(5)：803-810,8.DOI:10.3866/PKU.WHXB201403181
基于迭代自组织数据分析算法与蚁群算法建立有机物黏度的QSPR模型
QSPR Models of Compound Viscosity Based on Iterative Self-Organizing Data Analysis Technique and Ant Colony Algorithm
时静洁 1陈利平 1陈网桦1
作者信息
- 1. 南京理工大学化工学院安全工程系，南京210094
- 折叠
摘要
Abstract
The aim of this study was to construct a quantitative structure-property relationship model to identify relationships between the molecular structures and viscosities of 310 compounds, as wel as specific structural factors that could affect the viscosities of the compounds. Using an iterative self-organizing data analysis technique, the sample set was preliminarily classified into two sets, including a training set and a test set. The molecular structure descriptors of 310 compounds were calculated using version 2.1 of the Dragon software and subsequently sifted using an ant colony algorithm (ACO), which resulted in the selection of five parameters. Multiple linear regression (MLR) and the support vector machine (SVM) techniques were then used to establish ACO-MLR and ACO-SVM models, respectively. The results showed that the performance of the non-linear ACO-SVM model (correlation coefficient R 2train= 0.9013, R 2test= 0.9026) was superior to the linearACO-MLR model ( R 2train=0.7680, R 2test= 0.8725). The correlation coefficients between the experimental and predicted values of the ACO-MLR and ACO-SVM models for the test set were 0.934 and 0.950, respectively. The predictive properties of the two models were therefore determined to be satisfying. The application domain of the model was also studied using a Wil iams graph, which demonstrated that the models established in this study provide effective methods for predicting the viscosities of specific compounds based on their molecular structure.关键词
黏度/ISODATA/蚁群算法/多元线性回归/支持向量机Key words
Viscosity/ISODATA/Ant colony algorithm/Multiple linear regression/Support vector machine分类
化学化工引用本文复制引用
时静洁,陈利平,陈网桦..基于迭代自组织数据分析算法与蚁群算法建立有机物黏度的QSPR模型[J].物理化学学报,2014,(5):803-810,8.
