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.

中文翻译：

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定量构效关系的描述符选择改进

分子描述符选择是改进预测定量构效关系 (QSAR) 模型的必要程序。然而，在 QSAR 模型中，存在许多冗余、嘈杂和不相关的描述符。在这项研究中，我们提出了一种新颖的描述符选择框架，该框架使用自定进度学习 (SPL)，通过带有 Logsum 惩罚 (SPL-Logsum) 的稀疏逻辑回归 (LR)，可以同时自适应地识别简单和复杂样本并避免过度拟合. SPL 的灵感来自人类或动物从简单和复杂的样本中逐渐学习来训练模型的学习过程，而 Logsum 惩罚 LR 有助于选择一小部分重要的分子描述符来改进 QSAR 模型。