Comparative Study of QSAR/QSPR Correlations Using Support Vector Machines, Radial Basis Function Neural Networks, and Multiple Linear Regression

Support vector machines (SVMs) were used to develop QSAR models that correlate molecular structures to their toxicity and bioactivities. The performance and predictive ability of SVM are investigated and compared with other methods such as multiple linear regression and radial basis function neural...

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Bibliographic Details
Published in:Journal of Chemical Information and Computer Sciences 2004-07, Vol.44 (4), p.1257-1266
Main Authors: Yao, X. J, Panaye, A, Doucet, J. P, Zhang, R. S, Chen, H. F, Liu, M. C, Hu, Z. D, Fan, B. T
Format: Article
Language:English
Subjects:
Animals
Artificial Intelligence
Chemicals
Comparative studies
Computer science
Computer Simulation
Cyclooxygenase Inhibitors - chemistry
Cyclooxygenase Inhibitors - pharmacology
Databases, Factual
Linear Models
Molecular structure
Neural networks
Neural Networks (Computer)
Phenols - chemistry
Phenols - toxicity
Quantitative Structure-Activity Relationship
Regression analysis
Tetrahymena pyriformis - drug effects
Toxicity
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Summary:Support vector machines (SVMs) were used to develop QSAR models that correlate molecular structures to their toxicity and bioactivities. The performance and predictive ability of SVM are investigated and compared with other methods such as multiple linear regression and radial basis function neural network methods. In the present study, two different data sets were evaluated. The first one involves an application of SVM to the development of a QSAR model for the prediction of toxicities of 153 phenols, and the second investigation deals with the QSAR model between the structures and the activities of a set of 85 cyclooxygenase 2 (COX-2) inhibitors. For each application, the molecular structures were described using either the physicochemical parameters or molecular descriptors. In both studied cases, the predictive ability of the SVM model is comparable or superior to those obtained by MLR and RBFNN. The results indicate that SVM can be used as an alternative powerful modeling tool for QSAR studies.
ISSN:0095-2338
1549-9596
1549-960X
1520-5142
DOI:10.1021/ci049965i