Descriptor Selection via Self-Paced Learning for Bioactivity of Molecular Structure in QSAR Classification
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Quantitative structure-activity relationship (QSAR) modelling is effective 'bridge' to search the reliable relationship related biological activities to chemical structure. A QSAR classification model contains a lager number of redundant, noisy and irrelevant descriptors. To solve this problem, various of methods have been proposed for descriptor selection. Generally, they can be grouped into three categories: filters, wrappers, and embedded methods. Regularization method is an important embedded technology, which can be used for continuous shrinkage and automatic descriptors selection. In recent years, the interest of researchers in the application of regularization techniques is increasing in descriptors selection , such as, logistic regression(LR) with L_1 penalty. In this paper, we proposed a novel descriptor selection method based on self-paced learning(SPL) with Logsum penalized LR for classifying the biological activities of molecular structure. SPL inspired by the learning process of humans and animals that gradually learns from easy samples(smaller losses) to hard samples(bigger losses) samples into training and Logsum regularization has capacity to select few meaningful and significant molecular descriptors, respectively. Experimental results on artificial and three public QSAR datasets show that our proposed SPL-Logsum method is superior to other commonly used sparse methods in terms of classification performance and model interpretation.
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