Artificial neural network approach for predicting blood brain barrier permeability based on a group contribution method. (March 2021)

• Record Type: Journal Article
• Authors: Wu, Zeyu; Xian, Zhaojun; Ma, Wanru; Liu, Qingsong; Huang, Xusheng; Xiong, Baoyi; He, Shudong; Zhang, Wencheng
• Is Part Of: Computer methods and programs in biomedicine. Volume 200(2021)
• Publication Date: 2021-03
• Subjects: Blood brain barrier permeability -- Artificial neural network -- Group contribution method -- log BB -- UNIFAC; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.105943
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: QSAR models for predicting blood brain barrier permeability were proposed. The combination of ANN and UNIFAC group contribution method was applied in QSAR study. The indicators of R, RE and RMSE (0.956, 0.857, and 0.171) reflected the feasibility, robustness and accuracy of the model. Abstract: 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.