,Prediction of human skin permeability using artificial neural network (ANN)modeling

• 作 者: Long-jian CHEN Guo-ping LIAN Lu-jia HAN
• 机 构: College of Engineering, China Agricultural University, Beijing 100083, China“,”Unilever Corporate Re
• 出 处: 中国药理学报（英文版）
• 发表日期: 2007年4期
• 关键词: ANN model diffusion permeability quantitative structure-activity relationship sk

Aim: To develop an artificial neural network (ANN) model for predicting skin permeability (log Kp) of new chemical entities. Methods: A large dataset of 215experimental data points was compiled from the literature. The dataset was subdi-vided into 5 subsets and 4 of them were used to train and validate an ANN model.The same 4 datasets were also used to build a multiple linear regression (MLR)model. The remaining dataset was then used to test the 2 models. Abraham descriptors were employed as inputs into the 2 models. Model predictions were compared with the experimental results. In addition, the relationship between log Kp and Abraham descriptors were investigated. Results: The regression re-sults of the MLR model were n=215, determination coefficient (R2)=0.699, mean square error (MSE)=0.243, and F=493.556. The ANN model gave improved results with n=215, R2=0.832, MSE=0.136, and F=1050.653. The ANN model suggests that the relationship between log Kp and Abraham descriptors is non-linear. Conclusion:The study suggests that Abraham descriptors may be used to predict skin permeability, and the ANN model gives improved prediction of skin permeability.