Application of support vector regression to Quantitative Structure-Activity Relationships(QSAR)

被引：0

作者：

Lu, HM ^{[1
]}

Huang, L ^{[1
]}

Dai, Y ^{[1
]}

机构：

[1] Univ Illinois, Dept Bioengn MC063, Chicago, IL 60612 USA

来源：

PROCEEDINGS OF THE 7TH JOINT CONFERENCE ON INFORMATION SCIENCES | 2003年

关键词：

QSAR; support vector regression; feature selection; grid search; linear programs;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Predicting the biological activity of a compound from its chemical structure is a fundamental problem in drug design. The Support Vector (SV) Machine regression is one of the powerful machine learning methods developed for this purpose in Quantitative Structure-Activity Relationships (QSAR) Analysis. A procedure based on linear programming is proposed for feature selection of SV regression. The method can build models reflecting linear and quadratic relations between features. This new approach demonstrates favorable behavior.

引用

页码：942 / 945

页数：4

共 10 条

[1]

APPELL M, 2002, 3DQSAR

[2] Bagging predictors [J].

Breiman, L .

MACHINE LEARNING, 1996, 24 (02) :123-140

[3]

Cristianini N, 2000, Intelligent Data Analysis: An Introduction

[4]

DEMIRIZ A, IN PRESS COMPUTING S

[5]

HUANG L, 2003, P 2003 INT C MATH EN

[6] APPLICATION OF GENETIC FUNCTION APPROXIMATION TO QUANTITATIVE STRUCTURE-ACTIVITY-RELATIONSHIPS AND QUANTITATIVE STRUCTURE-PROPERTY RELATIONSHIPS [J].

ROGERS, D ;

HOPFINGER, AJ .

JOURNAL OF CHEMICAL INFORMATION AND COMPUTER SCIENCES, 1994, 34 (04) :854-866

[7] STRUCTURE-ACTIVITY-RELATIONSHIPS OF ANTIFILARIAL ANTIMYCIN ANALOGS - A MULTIVARIATE PATTERN-RECOGNITION STUDY [J].

SELWOOD, DL ;

LIVINGSTONE, DJ ;

COMLEY, JCW ;

ODOWD, AB ;

HUDSON, AT ;

JACKSON, P ;

JANDU, KS ;

ROSE, VS ;

STABLES, JN .

JOURNAL OF MEDICINAL CHEMISTRY, 1990, 33 (01) :136-142

[8]

Smola A. J., 1998, NEUROCOLT2 TECHNICAL

[9] Evolutionary optimization in quantitative structure-activity relationship: An application of genetic neural networks [J].

So, SS ;

Karplus, M .

JOURNAL OF MEDICINAL CHEMISTRY, 1996, 39 (07) :1521-1530

[10] THE USE OF NEURAL NETWORKS FOR VARIABLE SELECTION IN QSAR [J].

WIKEL, JH ;

DOW, ER .

BIOORGANIC & MEDICINAL CHEMISTRY LETTERS, 1993, 3 (04) :645-651

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