Melagraki Georgia, Afantitis Antreas, Makridima Kalliopi, Sarimveis Haralambos, Igglessi-Markopoulou Olga
School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechniou Str., Zografou Campus, Athens, 15780, Greece.
J Mol Model. 2006 Feb;12(3):297-305. doi: 10.1007/s00894-005-0032-8. Epub 2005 Nov 8.
A neural network methodology based on the radial basis function (RBF) architecture is introduced in order to establish quantitative structure-toxicity relationship models for the prediction of toxicity. The dataset used consists of 221 phenols and their corresponding toxicity values to Tetrahymena pyriformis. Physicochemical parameters and molecular descriptors are used to provide input information to the models. The performance and predictive abilities of the RBF models are compared to standard multiple linear regression (MLR) models. The leave-one-out cross validation procedure and validation through an external test set produce statistically significant R2 and RMS values for the RBF models, which prove considerably more accurate than the MLR models. [Figure: see text].
为了建立用于预测毒性的定量构效关系模型，引入了一种基于径向基函数（RBF）架构的神经网络方法。所使用的数据集由221种酚及其对梨形四膜虫的相应毒性值组成。物理化学参数和分子描述符被用于为模型提供输入信息。将RBF模型的性能和预测能力与标准多元线性回归（MLR）模型进行比较。留一法交叉验证程序和通过外部测试集进行的验证为RBF模型产生了具有统计学意义的R2和RMS值，这证明RBF模型比MLR模型准确得多。[图：见正文]
