Background and Objective

: The purpose of this study was to develop a quantitative structure-activity relationship (QSAR) model for the prediction of blood brain barrier (BBB) permeability by using artificial neural networks (ANN) in combination with molecular structure and property descriptors.

Methods

: Using a database based on 300 compounds, the 52 structure descriptors obtained based on the universal quasichemical functional group activity coefficients (UNIFAC) group contribution method and the selected 8 molecular property descriptors were used as the network inputs, whereas logBB values of compounds constituted its output.

Results

: The correlation coefficient R of the constructed prediction model, the relative error (RE) and the root mean square error (RMSE) was 0.956, 0.857, and 0.171, respectively. These indicators reflected the feasibility, robustness and accuracy of the prediction model. Compared with the previously published results, a significant improvement in the predictions of the proposed ANN model was observed.

Conclusions

: ANN model based on the group contribution method could achieve a good performance for logBB prediction.

中文翻译：

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基于群体贡献法的人工神经网络方法预测血脑屏障通透性

背景与目的

：本研究的目的是通过使用人工神经网络（ANN）结合分子结构和特性描述符，开发定量结构-活性关系（QSAR）模型来预测血脑屏障（BBB）渗透性。

方法

：使用基于300种化合物的数据库，根据通用准化学官能团活性系数（UNIFAC）基团贡献方法和选择的8种分子特性描述符获得的52个结构描述符用作网络输入，而化合物的logBB值构成了其输出。

结果

：构建的预测模型的相关系数R，相对误差（RE）和均方根误差（RMSE）分别为0.956、0.857和0.171。这些指标反映了预测模型的可行性，鲁棒性和准确性。与以前发表的结果相比，在拟议的人工神经网络模型的预测中观察到了显着改善。

结论

：基于群体贡献法的人工神经网络模型在logBB预测中具有良好的表现。