One of the most challenging issues when facing a Quantitative structure-activity relationship (QSAR) classification model is to deal with the descriptor selection. Penalized methods have been adapted and have gained popularity as a key for simultaneously performing descriptor selection and QSAR classification model estimation. However, penalized methods have drawbacks such as having biases and inconsistencies that make they lack the oracle properties. This paper proposes an adaptive penalized logistic regression (APLR) to overcome these drawbacks. This is done by employing a ratio (BWR) of the descriptors between-groups sum of squares (BSS) to the within-groups sum of squares (WSS) for each descriptor as a weight inside the L₁-norm. The proposed method was applied to one dataset that consists of a diverse series of antimicrobial agents with their respective bioactivities against Candida albicans. By experimental study, it has been shown that the proposed method (APLR) was more efficient in the selection of descriptors and classification accuracy than the other competitive methods that could be used in developing QSAR classification models. Another dataset was also successfully experienced. Therefore, it can be concluded that the APLR method had significant impact on QSAR analysis and studies.

面对定量构效关系（QSAR）分类模型时，最具挑战性的问题之一是处理描述符的选择。作为同时执行描述符选择和QSAR分类模型估计的关键，惩罚性方法已得到改编并获得普及。但是，惩罚方法具有诸如偏见和不一致之类的缺点，这使得它们缺乏预言性。本文提出了一种自适应惩罚逻辑回归（APLR）来克服这些缺点。这是通过将每个描述符的组间平方和（BSS）与组内平方和（WSS）的比率（BWR）作为L ₁内部的权重来完成的-规范。所提出的方法被应用于一个数据集，该数据集由一系列的抗微生物剂组成，它们分别具有对念珠菌的生物活性。通过实验研究，已表明，所提出的方法（APLR）在描述符的选择和分类准确性方面比在开发QSAR分类模型中可以使用的其他竞争方法更有效。另一个数据集也获得了成功的体验。因此，可以得出结论，APLR方法对QSAR分析和研究具有重大影响。