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Descriptor selection improvements for quantitative structure-activity relationships


Xia, L-Y and Wang, Q-Y and Cao, Z and Liang, Y, Descriptor selection improvements for quantitative structure-activity relationships, International Journal of Neural Systems, 29, (9) pp. 1950016. ISSN 1793-6462 (2019) [Refereed Article]

Copyright Statement

Copyright 2019 World Scientific Publishing Company

DOI: doi:10.1142/S0129065719500163


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 Details

Item Type:Refereed Article
Keywords:quantitative structure-activity, QSAR, biological activity, descriptor selection, SPL, Logsum penalized LR
Research Division:Information and Computing Sciences
Research Group:Machine learning
Research Field:Neural networks
Objective Division:Defence
Objective Group:Defence
Objective Field:Intelligence, surveillance and space
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:132868
Year Published:2019
Web of Science® Times Cited:10
Deposited By:Information and Communication Technology
Deposited On:2019-05-23
Last Modified:2020-08-17

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