Descriptor Selection via Log-Sum Regularization for the Biological Activities of Chemical Structure
AbstractThe quantitative structure-activity relationship (QSAR) model searches for a reliable relationship between the chemical structure and biological activities in the field of drug design and discovery. (1) Background: In the study of QSAR, the chemical structures of compounds are encoded by a substantial number of descriptors. Some redundant, noisy and irrelevant descriptors result in a side-effect for the QSAR model. Meanwhile, too many descriptors can result in overfitting or low correlation between chemical structure and biological bioactivity. (2) Methods: We use novel log-sum regularization to select quite a few descriptors that are relevant to biological activities. In addition, a coordinate descent algorithm, which uses novel univariate log-sum thresholding for updating the estimated coefficients, has been developed for the QSAR model. (3) Results: Experimental results on artificial and four QSAR datasets demonstrate that our proposed log-sum method has good performance among state-of-the-art methods. (4) Conclusions: Our proposed multiple linear regression with log-sum penalty is an effective technique for both descriptor selection and prediction of biological activity. View Full-Text
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Xia, L.-Y.; Wang, Y.-W.; Meng, D.-Y.; Yao, X.-J.; Chai, H.; Liang, Y. Descriptor Selection via Log-Sum Regularization for the Biological Activities of Chemical Structure. Int. J. Mol. Sci. 2018, 19, 30.
Xia L-Y, Wang Y-W, Meng D-Y, Yao X-J, Chai H, Liang Y. Descriptor Selection via Log-Sum Regularization for the Biological Activities of Chemical Structure. International Journal of Molecular Sciences. 2018; 19(1):30.Chicago/Turabian Style
Xia, Liang-Yong; Wang, Yu-Wei; Meng, De-Yu; Yao, Xiao-Jun; Chai, Hua; Liang, Yong. 2018. "Descriptor Selection via Log-Sum Regularization for the Biological Activities of Chemical Structure." Int. J. Mol. Sci. 19, no. 1: 30.
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