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MI-DRA 3D: New Model for Reconstruction of US FDA Drug-target Network and Theoretic-experimental Studies of Rasagiline Derivatives Inhibitors of AChE
* 1, 2 , 1 , 1 , 2 , 2 , 2 , 3
1  Department of Organic Chemistry, Faculty of Pharmacy, University of Santiago de Compostela
2  Biomedical Sciences Department, Health Science Division, University of Quintana Roo
3  Department of Microbiology and Parasitology, Faculty of Pharmacy, USC

Abstract: The Neurodegenerative diseases have been increasing in the last years. Many of the drug candidates to be used in the treatment of neurodegenerative disease present specific 3D structural features. One important protein in this sense is the acetylcholinesterase (AChE); which is the target of many Alzheimer\'s dementia drugs. Consequently, the prediction of Drug-Proteins Interactions (DPIs/nDPIs) between new drug candidates with specific 3D structure and targets it is about the major importance. For it, we can use Quantitative Structure-Activity Relationships (QSAR) models to carry out rational DPIs prediction. Unfortunately, many previous QSAR models developed to predict DPIs take into consideration only 2D structural information and codify the activity against only one target. To solve this problem we can develop one 3D multi-target QSAR (3D Mt-QSAR) models. In this communication, we introduce the technique MI-DRA 3D a new predictor for DPIs based two different well-known software. We use the software MARCH-INSIDE (MI) and DRAGON to calculate 3D structural parameters for drugs and targets respectively. Both classes of 3D parameters were used as input to train Artificial Neuronal Network (ANN) algorithms using as benchmark datasets the complex network (CN) formed by all DPIs between US FDA approved drugs and their targets. The entire dataset was downloaded from Drug Bank. The best 3D Mt-QSAR predictor found is one ANN of type Multilayer Perceptron (MLP) with profile MLP 37:37-24-1:1. This MLP classifies correctly 274 out of 321 DPIs (Sensitivity = 85.35%) and 1041 out of 1190 nDPIs (Specificity = 87.48%), corresponding to training Accuracy = 87.03%. We validated the model with external predicting series with Sensitivity = 84.16% (542/644 DPIs; Specificity = 87.51% (2039/2330 nDPIs) and Accuracy = 86.78%. The new CNs of DPIs reconstructed from US FDA can be used to explore large DPIs databases in order to discover both new drugs and/or targets. We carried out theoretic-experimental studies to illustrate the practical use of MI-DRA 3D. First, we reported the prediction and pharmacological assay of 22 different rasagiline derivatives with possible AChE inhibitory activity.
Keywords: Drug-Protein interaction complex networks; Protein Structure Networks; multi-target QSAR; Markov Model; AChE inhibitors