Abstract
The 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.
Highlights
The quantitative structure-activity relationship (QSAR) model searches for a reliable relationship between chemical the structure and biological activities in the field of drug design and discovery [1]
In the field of drug design and discovery, only a few descriptors are of interest to the QSAR model
Descriptor selection plays an important role in the study of QSAR
Summary
The quantitative structure-activity relationship (QSAR) model searches for a reliable relationship between chemical the structure and biological activities in the field of drug design and discovery [1]. Descriptor selection techniques can be categorized into four groups in the study of QSAR: classical methods, artificial intelligence-based methods, miscellaneous methods and regularization methods. The classical methods have been proposed in the study of QSAR; as an example, forward selection adds the most significant descriptors until none improves the model to a statistically-significant extent. A coordinate descent algorithm, which uses novel univariate log-sum thresholding for updating the estimated coefficients, has been developed for the QSAR model. Experimental results on artificial and four QSAR datasets demonstrate that our proposed log-sum method has good performance among state-of-the-art methods. The structure of this paper is organized as follows: Section 2 introduces a coordinate descent algorithm, which uses novel univariate log-sum thresholding for updating the estimated coefficients and gives a detailed description of the datasets.
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