支持向量回归用于偶氮染料的亲和力预测
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- 英文论文题名：Support Vector Regression Applied to the Prediction of Affinity of Azo Dyestuff
- 论文作者：肖林辉 ; 纪晓波 ; 刘太昂 ; 陆文聪
- 英文论文作者：Linhui Xiao ; Xiaobo Ji ; Taiang Liu ; Wencong Lu ; Department of Chemistry ; College of Sciences ; Shanghai University
- 年：2016
- 作者机构：上海大学理学院化学系;
- 论文关键词：支持向量回归(SVR) ; 偶氮染料 ; 亲和力(A)
- 英文论文关键词：Support Vector Regression ; Azo dyestuff ; Affinity(A)
- 会议召开时间：2016-07-01
- 会议录名称：中国化学会第30届学术年会摘要集-第二十五分会：化学信息学与化学计量学
- 语种：中文
- 分类号：TQ613.1
- 学会代码：ZGHY
- 会议名称：中国化学会第30届学术年会-第二十五分会 ; 化学信息学与化学计量学
- 会议地点：中国辽宁大连
- 主办单位：中国化学会
- 学会名称：中国化学会
- 页数：1
- 文件大小：386k
- 原文格式：D
- 会议级别：全国
摘要
采用支持向量回归(SVR)算法~([1,2]),构建了偶氮染料的亲和力(A)与被筛选出的四个分子参数(n:染料中共轭键的长度;a,c,λ_3:笛卡尔坐标中的空间参数)之间的非线性关系。留一法交叉验证的结果表明该模型具有很好的泛化能力,其均方根误差(RMSE)为1.38。本工作所用的方法为该类染料提供了亲和力(A)预报的有效方法。
Support Vector Regression with Gaussian kernel function was applied to construct the relationship between the affinities of azo dyestuff molecules and their structural parameters including the length of the conjugated chain of the dye molecule(n) and Steric parameters(a,c,λ_3) derived from Cartesian coordinates.The result of LOOCV test suggest that the QSPR model has good generalization ability with the RMSE is 1.38.Therefore,the method can be used to predict the affinity in computer aided molecular design of azo dyestuff.
Support Vector Regression with Gaussian kernel function was applied to construct the relationship between the affinities of azo dyestuff molecules and their structural parameters including the length of the conjugated chain of the dye molecule(n) and Steric parameters(a,c,λ_3) derived from Cartesian coordinates.The result of LOOCV test suggest that the QSPR model has good generalization ability with the RMSE is 1.38.Therefore,the method can be used to predict the affinity in computer aided molecular design of azo dyestuff.
引文
[1]Vapnik Vladimir N,The Nature of statistical Learning Theory.Berlin,Springer,1995
[2]Wencong Lu,XiaoboJi,Minjie Li,Liang Liu.Advances in Manufacturing.2013,1(2):151-159
[2]Wencong Lu,XiaoboJi,Minjie Li,Liang Liu.Advances in Manufacturing.2013,1(2):151-159