使用人工神经网络（ANN）模型预测人体皮肤渗透性。

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

作者信息

Chen Long-jian, Lian Guo-ping, Han Lu-jia

机构信息

College of Engineering, China Agricultural University, Beijing 100083, China.

出版信息

Acta Pharmacol Sin. 2007 Apr;28(4):591-600. doi: 10.1111/j.1745-7254.2007.00528.x.

DOI:10.1111/j.1745-7254.2007.00528.x

PMID:17376301

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.

摘要

目的

开发一种人工神经网络（ANN）模型，用于预测新化学实体的皮肤渗透性（log K(p)）。

方法

从文献中收集了一个包含215个实验数据点的大型数据集。该数据集被细分为5个子集，其中4个子集用于训练和验证一个ANN模型。同样的4个数据集也用于构建多元线性回归（MLR）模型。然后使用剩余的数据集来测试这两个模型。将亚伯拉罕描述符用作这两个模型的输入。将模型预测结果与实验结果进行比较。此外，还研究了log K(p)与亚伯拉罕描述符之间的关系。

结果

MLR模型的回归结果为n = 215，决定系数（R(2)）= 0.699，均方误差（MSE）= 0.243，F = 493.556。ANN模型给出了更好的结果，n = 215，R(2) = 0.832，MSE = 0.136，F = 1050.653。ANN模型表明log K(p)与亚伯拉罕描述符之间的关系是非线性的。