Descriptor Selection Improvements for Quantitative Structure-Activity Relationships.
- Publisher:
- World Scientific Pub Co Pte Lt
- Publication Type:
- Journal Article
- Citation:
- International journal of neural systems, 2019, 29, (9), pp. 1950016
- Issue Date:
- 2019-11
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| PAC-10036055.pdf | Published version | 964.51 kB |
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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 P-values.
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