Chemistry department, Faculty of Science, Vali-e-Asr University, Rafsanjan, Iran.
Eur J Med Chem. 2010 Feb;45(2):719-26. doi: 10.1016/j.ejmech.2009.11.019. Epub 2009 Nov 23.
QSAR analysis for modeling the antileishmanial activity screening of a series of 49 nitro derivatives of Hydrazides were carried out using different Chemometrics methods. First, a large number of descriptors were calculated using Hyperchem, Mopac and Dragon softwares. Then, a suitable number of these descriptors were selected using multiple linear regression (MLR) technique. Then selected descriptors were used as inputs for artificial neural networks with three different weight update functions including Levenberg-Marquardt back propagation network (LM-ANN), resilient back propagation network (RP-ANN) and variable learning rate algorithm (GDX-ANN). The best artificial neural network model was an LM-ANN with a 5-5-1 architecture. Comparison of the results indicates that the LM-ANN method has better predictive power than the other methods.
采用不同化学计量学方法对一系列 49 种 Hydrazide 的硝基衍生物进行抗利什曼原虫活性筛选的 QSAR 分析。首先，使用 Hyperchem、Mopac 和 Dragon 软件计算了大量描述符。然后，使用多元线性回归（MLR）技术选择了适量的描述符。然后，将选定的描述符用作具有三种不同权重更新功能的人工神经网络的输入，包括 Levenberg-Marquardt 反向传播网络（LM-ANN）、弹性反向传播网络（RP-ANN）和可变学习率算法（GDX-ANN）。最佳人工神经网络模型是具有 5-5-1 架构的 LM-ANN。结果比较表明，LM-ANN 方法比其他方法具有更好的预测能力。
