Prediction of Neurological Enzyme Targets for Known and New Compounds with a Model using Galvez's Topological Indices

Abstract

Alzheimer's Disease (AD), Parkinson, and other neurodegenerative diseases are a major health problem nowadays. In this sense, the discovery of new drugs for neurodiseases treatment is a goal of the major importance. Public databases, like ChEMBL, contain a large amount of data about multiplexing assays of inhibitors of a group of enzymes with special relevance in central nervous system. Mono Amino Oxidases (MAOs), Acetyl Cholinesterase (AChE), Glycogen Synthase Kinase-3 (GSK-3), AChE (AChE), and 5α-reductases (5αRs). This data conform an important information source for the application of multi-target computational models. However, almost all the computational models known focus in only one target. In this work, we developed mt-QSAR for inhibitors of 8 different enzymes promising in the treatment of different neurodiseases. In so doing, we combined by the first time the software DRAGON with Moving Average parameters with this objective. The best DRAGON model found predict with very high accuracy, specificity, and sensitivity >90% a very large data set >10000 cases in training and validation series. We also report experimental results about the assay of several 7H