Ant colony optimization as a feature selection method in the QSAR modeling of anti-HIV-1 activities of 3-(3,5-dimethylbenzyl)uracil derivatives using MLR, PLS and SVM regressions

作者：Goodarzi Mohammad; Freitas Matheus P^*; Jensen Richard

来源：Chemometrics and Intelligent Laboratory Systems, 2009, 98(2): 123-129.

DOI：10.1016/j.chemolab.2009.05.005

• QSAR
• Anti-HIV-1 activities
• 3-(3,5-Dimethylbenzyl)uracil derivatives
• Ant colony optimization
• Linear and nonlinear regression methods

A quantitative structure-activity relationship (QSAR) modeling was carried out for the anti-HIV-1 activities of 3-(3,5-dimethylbenzyl)uracil derivatives. The ant colony optimization (ACO) strategy was used as a feature selection (descriptor selection) and model development method. Modeling of the relationship between selected molecular descriptors and pEC(50) data was achieved by linear (multiple linear regression-MLR, and partial least squares regression-PLS) and nonlinear (support-vector machine regression; SVMR) methods. The QSAR models were validated by cross-validation, as well as through the prediction of activities of an external set of compounds. Both linear and nonlinear methods were found to be better than a PL-S-based method using forward stepwise (FS) selection, resulting in accurate predictions, especially for the SVM regression. The squared correlation coefficients of experimental versus predicted activities for the test set obtained by MLR, PLS and SVMR models using ACO feature selection were 0.942, 0.945 and 0.991, respectively.

• 出版日期2009-10-15