Literature DB >> 17376301

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

Long-jian Chen1, Guo-ping Lian, Lu-jia Han.   

Abstract

AIM: 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.
CONCLUSION: The study suggests that Abraham descriptors may be used to predict skin permeability, and the ANN model gives improved prediction of skin permeability.

Entities:  

Mesh:

Year:  2007        PMID: 17376301     DOI: 10.1111/j.1745-7254.2007.00528.x

Source DB:  PubMed          Journal:  Acta Pharmacol Sin        ISSN: 1671-4083            Impact factor:   6.150


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