Modeling the antileishmanial activity screening of 5-nitro-2-heterocyclic benzylidene hydrazides using different chemometrics methods

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.

QSAR analysis for modeling the antileishmanial activity of Benzylidene Hydrazides was carried out using chemometrics methods. This paper focuses on investigating the role of weight update function in artificial neural networks. Figure optionsDownload as PowerPoint slide