AI Article Synopsis
- The study aimed to create an artificial neural network (ANN) model to predict skin permeability (log K(p)) for new chemicals.
- A dataset of 215 experimental results was used, splitting it into subsets for training and validating the ANN model and a multiple linear regression (MLR) model.
- Results showed the ANN model outperformed the MLR model, indicating a non-linear relationship between log K(p) and Abraham descriptors, confirming their potential for predicting skin permeability.
Article 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.
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| http://dx.doi.org/10.1111/j.1745-7254.2007.00528.x | DOI Listing |
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