PDF(Pubmed)
Abstract：
Current treatments for Alzheimer\'s disease (AD) focus on slowing memory and cognitive decline, but none offer curative outcomes. This study aims to explore and curate the common properties of active, drug-like molecules that modulate glycogen synthase kinase 3β (GSK-3β), a well-documented kinase with increased activity in tau hyperphosphorylation and neurofibrillary tangles-hallmarks of AD pathology. Leveraging quantitative structure-activity relationship (QSAR) data from the PubChem and ChEMBL databases, we employed seven machine learning models: logistic regression (LogR), k-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), neural networks (NNs), and ensemble majority voting. Our goal was to correctly predict active and inactive compounds that inhibit GSK-3β activity and identify their key properties. Among the six individual models, the NN demonstrated the highest performance with a 79% AUC-ROC on unbalanced external validation data, while the SVM model was superior in accurately classifying the compounds. The SVM and RF models surpassed NN in terms of Kappa values, and the ensemble majority voting model demonstrated slightly better accuracy to the NN on the external validation data. Feature importance analysis revealed that hydrogen bonds, phenol groups, and specific electronic characteristics are important features of molecular descriptors that positively correlate with active GSK-3β inhibition. Conversely, structural features like imidazole rings, sulfides, and methoxy groups showed a negative correlation. Our study highlights the significance of structural, electronic, and physicochemical descriptors in screening active candidates against GSK-3β. These predictive features could prove useful in therapeutic strategies to understand the important properties of GSK-3β candidate inhibitors that may potentially benefit non-amyloid-based AD treatments targeting neurofibrillary tangles.
摘要：
目前治疗阿尔茨海默病（AD）的重点是减缓记忆和认知能力下降，但没有提供治愈的结果。本研究旨在探索和策划活性，调节糖原合成酶激酶3β（GSK-3β）的药物样分子，一种有据可查的激酶，在tau过度磷酸化和神经原纤维缠结中具有增加的活性-AD病理标志。利用PubChem和ChEMBL数据库中的定量结构-活性关系（QSAR）数据，我们使用了七个机器学习模型：逻辑回归(LogR)，k-最近邻(KNN)，随机森林(RF)，支持向量机(SVM)，极端梯度增强（XGB），神经网络(NN)，和合奏多数票。我们的目标是正确预测抑制GSK-3β活性的活性和非活性化合物，并确定其关键特性。在六个单独的模型中，在不平衡的外部验证数据上，NN表现出最高的性能，AUC-ROC为79%，而支持向量机模型在准确分类化合物方面具有优越性。SVM和RF模型在Kappa值方面超过了NN，总体多数投票模型在外部验证数据上显示出比NN略好的准确性。特征重要性分析表明，氢键，苯酚基团，和特定的电子特性是与活性GSK-3β抑制呈正相关的分子描述符的重要特征。相反，结构特征如咪唑环，硫化物，和甲氧基呈负相关。我们的研究强调了结构的重要性，电子,和物理化学描述符在筛选针对GSK-3β的活性候选物中。这些预测特征可以证明在治疗策略中有用，以了解GSK-3β候选抑制剂的重要特性，这些抑制剂可能潜在地有益于靶向神经原纤维缠结的基于非淀粉样蛋白的AD治疗。
