Descriptor Selection Improvements for Quantitative Structure-Activity Relationships

Author:

Xia Liang-Yong1,Wang Qing-Yong2,Cao Zehong3,Liang Yong4

Affiliation:

1. Faculty of Information Technology, Macau University of Science and Technology, Macau, P. R. China

2. Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, P. R. China

3. Discipline of ICT, School of Technology, Environments and Design, College of Sciences and Engineering, University of Tasmania, TAS, Australia

4. University of Science and Technology, Macau, P. R. China

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 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 [Formula: see text]-values.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Networks and Communications,General Medicine

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