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Descriptor selection improvements for quantitative structure-activity relationships
journal contribution
posted on 2023-05-20, 03:55 authored by Xia, L-Y, Wang, Q-Y, Cao, Z, Liang, YMolecular 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.
History
Publication title
International Journal of Neural SystemsVolume
29Issue
9Pagination
1950016ISSN
1793-6462Department/School
School of Information and Communication TechnologyPublisher
World Scientific Publishing Co. Pte. Ltd.Place of publication
SingaporeRights statement
Copyright 2019 World Scientific Publishing CompanyRepository Status
- Restricted