非离子表面活性剂
数量结构-活动关系
肺表面活性物质
云计算
化学
计算机科学
立体化学
生物化学
操作系统
作者
Yaoyao Ren,Baowei Zhao,Qing Chang,Xiaojun Yao
标识
DOI:10.1016/j.jcis.2011.02.042
摘要
Quantitative structure–property relationship (QSPR) models for the cloud points of nonionic surfactants were developed based on CODESSA descriptors. Essentials accounting for a reliable model were considered carefully. Four descriptors were selected by a generic algorithm (GA) method to link the structures of nonionic surfactants to their corresponding cloud-point values. The descriptors were also analyzed using principal component analysis (PCA). Nonlinear models based on support vector machine (SVM) and projection pursuit regression (PPR) were also developed. All models were validated in two ways, i.e., internal cross-validation (CV) and a test set. The results are discussed in light of the main factors that influence the property under investigation and its modeling. In addition, an independent external data set of 16 nonionic surfactants was used to check the generalization ability of the optimum model.
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