title: Prediction of human skin permeability using artificial neural network (ANN) modeling.
abstract: OBJECTIVE
  To develop an artificial neural network (ANN) model for predicting skin permeability (log K(p)) of new chemical entities.
  
  
  METHODS
  A large dataset of 215 experimental data points was compiled from the literature. The dataset was subdivided 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 K(p) and Abraham descriptors were investigated.
  
  
  RESULTS
  The regression results of the MLR model were n=215, determination coefficient (R(2))=0.699, mean square error (MSE)=0.243, and F=493.556. The ANN model gave improved results with n=215, R(2)=0.832, MSE=0.136, and F=1050.653. The ANN model suggests that the relationship between log K(p) and Abraham descriptors is non-linear.
  
  
  CONCLUSIONS
  The study suggests that Abraham descriptors may be used to predict skin permeability, and the ANN model gives improved prediction of skin permeability.
authors: College of Engineering, China Agricultural University, Beijing 100083, China.
authors: Long-jian
authors: LJ
authors: Chen
authors: Guo-ping
authors: GP
authors: Lian
authors: Lu-jia
authors: LJ
authors: Han
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010539:Permeability / Q000187:drug effects; D011237:Predictive Value of Tests; D012869:Skin Absorption / Q000502:physiology*; D013358:Subject Headings