Wu Zeyu, Xian Zhaojun, Ma Wanru, Liu Qingsong, Huang Xusheng, Xiong Baoyi, He Shudong, Zhang Wencheng
School of Food and Biological Engineering, Hefei University of Technology, Hefei 230601, China; Engineering Research Center of Bio-Process, Ministry of Education, Hefei University of Technology, Hefei 230601, China.
School of Food and Biological Engineering, Hefei University of Technology, Hefei 230601, China; Engineering Research Center of Bio-Process, Ministry of Education, Hefei University of Technology, Hefei 230601, China.
Comput Methods Programs Biomed. 2021 Mar;200:105943. doi: 10.1016/j.cmpb.2021.105943. Epub 2021 Jan 15.
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.
Using a database composed of 300 compounds, 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.
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.
ANN model based on the group contribution method could achieve a satisfactory performance for logBB prediction.
本研究的目的是通过结合人工神经网络（ANN）与分子结构及性质描述符，开发一种用于预测血脑屏障（BBB）通透性的定量构效关系（QSAR）模型。
使用一个由300种化合物组成的数据库，基于通用准化学官能团活度系数（UNIFAC）基团贡献法获得的52个结构描述符以及选定的8个分子性质描述符用作网络输入，而化合物的logBB值构成其输出。
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、稳健性和准确性。与先前发表的结果相比，所提出的ANN模型的预测有显著改进。
基于基团贡献法的ANN模型在logBB预测方面可取得令人满意的性能。
