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Artificial Neural Network Approach for Predicting Blood Brain Barrier Permeability Based on a Group Contribution Method by Zeyu Wu; Zhaojun Xian; Wanru Ma; Qingsong Liu; Xusheng Huang; Baoyi Xiong; Shudong He; Wencheng Zhang is a Medicine article available to read on EtoBox.
What is Artificial Neural Network Approach for Predicting Blood Brain Barrier Permeability Based on a Group Contribution Method about?
## 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.
Who reads Artificial Neural Network Approach for Predicting Blood Brain Barrier Permeability Based on a Group Contribution Method?
It is typically read by researchers, students, and practitioners in Medicine.
- Author
- Zeyu Wu; Zhaojun Xian; Wanru Ma; Qingsong Liu; Xusheng Huang; Baoyi Xiong; Shudong He; Wencheng Zhang
- Publisher
- Elsevier BV
- Published
- 2021
- Language
- EN
- Field
- Medicine (Physical Sciences)