基于SPL-Logsum的分子结构生物活性的QSAR分类建模
QSAR Classification Modeling for Bioactivity of Molecular Structure via SPL-Logsum
摘要：
定量构效关系（QSAR）是寻找生物活性与分子结构之间可靠关系的有效“桥梁”。QSAR分类模型包含大量冗余、噪声和不相关的描述符。为了解决这个问题，已经提出了用于描述符选择的各种方法。通常，它们可以分为三类：过滤器、包装器和嵌入式方法。正则化方法是一种重要的嵌入技术，可以实现图像的连续收缩和描述子的自动选取。近年来，研究人员对正则化技术在描述符选择中的应用越来越感兴趣，例如具有$L_1$惩罚的逻辑回归（LR）。本文提出了一种新的描述子选择方法，用于预测分子结构的生物活性。SPL受到人类和动物学习过程的启发，逐渐从简单样本（较小的损失）学习到硬样本（较大的损失）样本进入训练，Logsum正则化能够分别选择少数有意义和重要的分子描述符。在模拟数据集和三个公开QSAR数据集上的实验结果表明，我们提出的SPL-Logsum方法在分类性能和模型解释方面优于其他常用的稀疏方法。
英文摘要：
Quantitative structure-activity relationship (QSAR) modelling is effective 'bridge' to search the reliable relationship related bioactivity to molecular structure. A QSAR classification model contains a lager number of redundant, noisy and irrelevant descriptors. To address this problem, various of methods have been proposed for descriptor selection. Generally, they can be grouped into three categories: filters, wrappers, and embedded methods. Regularization method is an important embedded technology, which can be used for continuous shrinkage and automatic descriptors selection. In recent years, the interest of researchers in the application of regularization techniques is increasing in descriptors selection , such as, logistic regression(LR) with $L_1$ penalty. In this paper, we proposed a novel descriptor selection method based on self-paced learning(SPL) with Logsum penalized LR for predicting the bioactivity of molecular structure. SPL inspired by the learning process of humans and animals that gradually learns from easy samples(smaller losses) to hard samples(bigger losses) samples into training and Logsum regularization has capacity to select few meaningful and significant molecular descriptors, respectively. Experimental results on simulation and three public QSAR datasets show that our proposed SPL-Logsum method outperforms other commonly used sparse methods in terms of classification performance and model interpretation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
