BACKGROUND Alzheimer's disease is characterized by a progressive pattern of cognitive and functional impairment, which ultimately leads to death. Computational approaches have played an important role in the context of drug discovery for anti-Alzheimer's therapies. However, most of the computational models reported to date have been focused on only one protein associated with Alzheimer's, while relying on small datasets of structurally related molecules. OBJECTIVE We introduce the first model combining perturbation theory and machine learning based on artificial neural networks (PTML-ANN) for simultaneous prediction and design of inhibitors of three Alzheimer's disease-related proteins, namely glycogen synthase kinase 3 beta (GSK3B), histone deacetylase 1 (HDAC1), and histone deacetylase 6 (HDAC6). METHODS The PTML-ANN model was obtained from a dataset retrieved from ChEMBL, and it relied on a classification approach to predict chemicals as active or inactive. RESULTS The PTML-ANN model displayed sensitivity and specificity higher than 85% in both training and test sets. The physicochemical and structural interpretation of the molecular descriptors in the model permitted the direct extraction of fragments suggested to favorably contribute to enhancing the multitarget inhibitory activity. Based on this information, we assembled ten molecules from several fragments with positive contributions. Seven of these molecules were predicted as triple target inhibitors while the remaining three were predicted as dual-target inhibitors. The estimated physicochemical properties of the designed molecules complied with Lipinski's rule of five and its variants. CONCLUSION This work opens new horizons toward the design of multi-target inhibitors for anti- Alzheimer's therapies.

中文翻译：

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阿尔茨海默氏病的PTML建模：GSK3B，HDAC1和HDAC6的虚拟多靶标抑制剂的设计和预测。

背景技术阿尔茨海默氏病的特征在于认知和功能障碍的进行性模式，其最终导致死亡。计算方法在抗阿尔茨海默氏病药物研发中发挥了重要作用。但是，迄今为止报道的大多数计算模型都只关注与阿尔茨海默氏症相关的一种蛋白质，同时依赖于结构相关分子的小型数据集。目的我们引入第一个结合了扰动理论和基于人工神经网络（PTML-ANN）的机器学习的模型，用于同时预测和设计三种阿尔茨海默氏病相关蛋白的抑制剂，即糖原合酶激酶3 beta（GSK3B），组蛋白脱乙酰基酶1 （HDAC1）和组蛋白脱乙酰基酶6（HDAC6）。方法PTML-ANN模型是从ChEMBL检索的数据集中获得的，它依靠分类方法来预测化学物质的活性或惰性。结果PTML-ANN模型在训练和测试集中均显示出高于85％的敏感性和特异性。该模型中分子描述符的物理化学和结构解释允许直接提取建议的片段，从而有利于增强多靶点抑制活性。根据这些信息，我们从几个片段中组装了十个分子，并做出了积极的贡献。这些分子中有七个被预测为三靶标抑制剂，其余三个被预测为双靶标抑制剂。设计分子的估计理化性质符合Lipinski' 的五个规则及其变体。结论这项工作为抗阿尔茨海默氏病治疗的多靶点抑制剂的设计开辟了新的视野。