Neural network based drug design for diabetes mellitus using QSAR with 2D and 3D descriptors
Jagdish C. Patra, Kenny H. K. Chua · 2010
In this paper, we propose an artificial neural network approach to determine the quantitative structure-activity relationship (QSAR) among known aldose reductase inhibitors (ARI). In order to accurately describe the structural properties of ARIs, besides the popularly used 2-dimensional (2D) descriptors, we have used 3-dimensional (3D) molecular descriptors which are obtained through the DRAGON software. Multi-layer perceptrons (MLPs) with LM learning algorithm are used to determine the QSAR of the ARIs to predict two bioactivities, i.e., IC50and PI. We have shown that the performance of the proposed MLP-based model is much better in terms of RMSE and R-value than previous studies, which have used only two molecular descriptors, molar volume and electronegativity.