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

Chen, LJ, Lian, GP and Han, LJ (2007) Prediction of human skin permeability using artificial neural network (ANN) modeling. Acta Pharmacol Sin, 28 (4). pp. 591-600.

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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.

Item Type: Article
Authors :
NameEmailORCID
Chen, LJUNSPECIFIEDUNSPECIFIED
Lian, GPg.lian@surrey.ac.ukUNSPECIFIED
Han, LJUNSPECIFIEDUNSPECIFIED
Date : April 2007
Identification Number : https://doi.org/10.1111/j.1745-7254.2007.00528.x
Uncontrolled Keywords : Algorithms, Databases, Factual, Humans, Models, Statistical, Neural Networks (Computer), Permeability, Predictive Value of Tests, Skin Absorption, Subject Headings
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 17 May 2017 13:05
Last Modified : 17 May 2017 15:08
URI: http://epubs.surrey.ac.uk/id/eprint/837899

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