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Abstract

Molecular descriptor selection is an essential procedure to improve a predictive quantitative structure activity relationship (QSAR) model. However, within the QSAR model, there are a number of redundant, noisy and irrelevant descriptors. In this study, we propose a novel descriptor selection framework using self-paced learning (SPL) via sparse logistic regression (LR) with Logsum penalty (SPL-Logsum), which can simultaneously adaptively identify the simple and complex samples and avoid the over-fitting. SPL is inspired by the learning process of humans or animals gradually learned from simple and complex samples to train models, and the Logsum penalized LR helps to select a small subset of significant molecular descriptors for improving the QSAR models. Experimental results on some simulations and three public QSAR datasets show that our proposed SPL-Logsum framework outperforms other existing sparse methods regarding the area under the curve, sensitivity, specificity, accuracy, and P-values.

Item Type:	Article
Authors/Creators:	Xia, L-Y and Wang, Q-Y and Cao, Z and Liang, Y
Keywords:	quantitative structure-activity, QSAR, biological activity, descriptor selection, SPL, Logsum penalized LR
Journal or Publication Title:	International Journal of Neural Systems
Publisher:	World Scientific Publishing Co. Pte. Ltd.
ISSN:	1793-6462
DOI / ID Number:	10.1142/S0129065719500163
Copyright Information:	Copyright 2019 World Scientific Publishing Company
Related URLs:	Google Scholar: Cao, Z
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