Checking the STEP-Associated Trafficking and Internalization of Glutamate Receptors for Reduced Cognitive Deficits: A Machine Learning Approach-Based Cheminformatics Study and Its Application for Drug Repurposing.

摘要: BACKGROUND Alzheimer's disease, a lethal neurodegenerative disorder that leads to progressive memory loss, is the most common form of dementia. Owing complexity its root cause still remains unclear. The existing anti-Alzheimer's drugs are unable cure disease while current therapeutic options have provided only limited help in restoring moderate and remain ineffective at restricting disease's progression. striatal-enriched protein tyrosine phosphatase (STEP) has been shown be involved internalization receptor, N-methyl D-aspartate (NMDR) thus associated with disease. present study was performed using machine learning algorithms, docking protocol molecular dynamics (MD) simulations develop STEP inhibitors, which could novel molecules. METHODS deals generation computational predictive models based on chemical descriptors compounds approaches followed by substructure fragment analysis. To perform this analysis, 2D were generated algorithms (Naive Bayes, Random Forest Sequential Minimization Optimization) utilized. binding mechanisms interactions between predicted active target modelled methods. Further, stability protein-ligand complex evaluated MD simulation studies. analysis Substructure fingerprint (SubFp), further explored predefined dictionary. RESULTS demonstrates methodology used can employed examine biological activities small molecules prioritize them for experimental screening. Large unscreened libraries screened identify potential hits accelerate drug discovery process. Additionally, searched significant patterns as reported study, possibly contributing activity these