Artificial neural network approach for predicting blood brain barrier permeability based on a group contribution method

Autor: Baoyi Xiong, Wanru Ma, Wencheng Zhang, Zhaojun Xian, Shudong He, Xusheng Huang, Zeyu Wu, Qingsong Liu
Rok vydání: 2021
Předmět:
Zdroj: Computer Methods and Programs in Biomedicine. 200:105943
ISSN: 0169-2607
DOI: 10.1016/j.cmpb.2021.105943
Popis: 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 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. 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 satisfactory performance for logBB prediction.
Databáze: OpenAIRE
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