Alzheimer related Target prediction for Glatiramer acetate

Abstract

Introduction: According to the evidence in previous studies, it was found that GA, which is effective in the treatment of MS, has also shown positive effects on the cognitive effects of AD. Objective: Alzheimer-related Target prediction for Glatiramer acetate.Methods: Using the developed python models and the molecules in other databases, we select the desired target molecules with the Baysian finger print method, which is a method based on similarity.Then we calculate the similarity index of GA with the inhibitors and ligands of the targets in the ChEMBL25 database according to the classification method and select targets with an index greater than 0.9. We rank the selected targets based on experimental evidence and select targets with a higher rank to investigate the molecular mechanism.In order to predict the mechanism of interaction between GA and targets predicted by molecular docking method, we use Autodock software.We validate our calculation method in order to check the correctness of the data by an external method. Results: 107 targets were extracted from the KEGG database and scientific texts, and after using the developed Python model, 2 targets with proba > 0.9 named AMPA1 and GCPⅡ were selected to investigate the interaction and binding mechanism with GA.Conclusion: Using the developed models in this study, effective targets in Alzheimer's disease were identified on the drug glatiramer acetate. The data mining method used made it possible to conduct studies with a large number of targets in a short period. The results of this method can be used to predict the target and find the leading compound.