Department of Physical Chemistry, Pharmacy Faculty, Collegium Medicum of Bydgoszcz, Nicolaus Copernicus University in Toruń, Kurpińskiego 5, 85-950 Bydgoszcz, Poland.
Molecules. 2020 Dec 15;25(24):5942. doi: 10.3390/molecules25245942.
Beta-glucosidase inhibitors play important medical and biological roles. In this study, simple two-variable artificial neural network (ANN) classification models were developed for beta-glucosidase inhibitors screening. All bioassay data were obtained from the ChEMBL database. The classifiers were generated using 2D molecular descriptors and the data miner tool available in the STATISTICA package (STATISTICA Automated Neural Networks, SANN). In order to evaluate the models' accuracy and select the best classifiers among automatically generated SANNs, the Matthews correlation coefficient (MCC) was used. The application of the combination of maxHBint3 and SpMax8_Bhs descriptors leads to the highest predicting abilities of SANNs, as evidenced by the averaged test set prediction results (MCC = 0.748) calculated for ten different dataset splits. Additionally, the models were analyzed employing receiver operating characteristics (ROC) and cumulative gain charts. The thirteen final classifiers obtained as a result of the model development procedure were applied for a natural compounds collection available in the BIOFACQUIM database. As a result of this beta-glucosidase inhibitors screening, eight compounds were univocally classified as active by all SANNs.
β-葡萄糖苷酶抑制剂具有重要的医学和生物学作用。本研究建立了简单的双变量人工神经网络（ANN）分类模型，用于筛选β-葡萄糖苷酶抑制剂。所有生物测定数据均来自 ChEMBL 数据库。使用 2D 分子描述符和 STATISTICA 软件包（STATISTICA 自动神经网络，SANN）中的数据挖掘工具生成分类器。为了评估模型的准确性并从自动生成的 SANN 中选择最佳分类器，使用了马修斯相关系数（MCC）。最大 HBint3 和 SpMax8_Bhs 描述符的组合的应用导致 SANN 具有最高的预测能力，这可以从为十个不同数据集划分计算的平均测试集预测结果（MCC=0.748）中得到证明。此外，还使用接收者操作特征（ROC）和累积增益图对模型进行了分析。通过模型开发过程获得的 13 个最终分类器应用于 BIOFACQUIM 数据库中提供的天然化合物集合。通过这种β-葡萄糖苷酶抑制剂筛选，所有 SANN 都将 8 种化合物一致分类为活性化合物。
