title: Theoretical study of GSK-3α: neural networks QSAR studies for the design of new inhibitors using 2D descriptors.
abstract: Glycogen synthase kinase-3 (GSK-3) targets encompass proteins implicated in AD and neurological disorders. The functions of GSK-3 and its implication in various human diseases have triggered an active search for potent and selective GSK-3 inhibitors. In this sense, QSAR could play an important role in studying these GSK-3 inhibitors. For this reason, we developed QSAR models for GSK-3α, linear discriminant analysis (LDA), and artificial neural networks (ANNs) from nearly 50,000 cases with more than 700 different GSK-3α inhibitors obtained from ChEMBL database server; in total we used more than 20,000 different molecules to develop the QSAR models. The model correctly classified 237 out of 275 active compounds (86.2%) and 14,870 out of 15,970 non-active compounds (93.2%) in the training series. The overall training performance was 93.0%. Validation of the model was carried out using an external predicting series. In these series, the model classified correctly 458 out of 549 (83.4%) compounds and 29,637 out of 31,927 non-active compounds (83.4%). The overall predictability performance was 92.7%. In this study, we propose three types of non-linear ANN as alternative to already existing models, such as LDA. Linear neural network: LNN: 236:236-1-1:1 which had an overall training performance of 96% proved to be the best model. In addition, we did a study of the different fragments of the molecules of the database to see which fragments had more influence in the activity. This can help design new inhibitors of GSK-3α. This study reports the attempts to calculate, within a unified framework probabilities of GSK-3α inhibitors against different molecules found in the literature.
authors: Faculty of Chemistry, Department of Organic Chemistry, University of Vigo, Vigo, Spain. iselapintos@yahoo.es
authors: Isela
authors: I
authors: García
authors: Yagamare
authors: Y
authors: Fall
authors: Xerardo
authors: X
authors: García-Mera
authors: Francisco
authors: F
authors: Prado-Prado
mesh_terms: D016208:Databases, Factual; D016002:Discriminant Analysis; D015195:Drug Design; D038362:Glycogen Synthase Kinase 3 / Q000037:antagonists & inhibitors*; D008958:Models, Molecular; D008968:Molecular Conformation; D016571:Neural Networks, Computer; D011336:Probability; D047428:Protein Kinase Inhibitors / Q000737:chemistry / Q000494:pharmacology*; D021281:Quantitative Structure-Activity Relationship
chemical_list: D047428:Protein Kinase Inhibitors; D038362:Glycogen Synthase Kinase 3; C448021:glycogen synthase kinase 3 alpha