Literature DB >> 28950235

A physicochemical descriptor based method for effective and rapid screening of dual inhibitors against BACE-1 and GSK-3β as targets for Alzheimer's disease.

Akhil Kumar1, Gaurava Srivastava2, Ashok Sharma3.   

Abstract

Due to multifactorial nature of Alzheimer's disease one target-one ligand hypothesis often looks insufficient. BACE-1 and GSK-3β are well established therapeutic drug targets and interaction between BACE-1 and GSK-3β pathways has also been established. Thus, designing of dual inhibitor for these two targets seems rational and may provide effective therapeutic strategies against AD. Recent studies revealed that only two scaffolds i.e. triazinone and curcumin act as a dual inhibitor against BACE-1 and GSK-3β. Thus, this discovery set the path to screen new chemical entities from a vast chemical space (∼1060 compounds) that inhibit both the targets. However, small part of the large chemical space will only show biological activity for specific targets. Virtual screening of large libraries is impractical and computational expensive especially in case of dual inhibitor design. In the case of dual or multi target inhibitor designing, we screened the database for each target that further increases time and resources. In this study we have done physicochemical descriptor based profiling to know the biological relevant chemical space for BACE-1 and GSK-3β inhibitors and proposed the suitable range of important physicochemical properties, occurrence of functional groups. We generated scaffolds tree of known inhibitors of BACE-1 and GSK-3β suggesting the common structure/fragment that can be used to design dual inhibitors. This approach can filter the potential dual inhibitor candidates of BACE-1 and GSK-3β from non inhibitors.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Alzheimer’s disease; BACE-1; GSK-3β; Physicochemical properties; Virtual screening

Mesh:

Substances:

Year:  2017        PMID: 28950235     DOI: 10.1016/j.compbiolchem.2017.09.001

Source DB:  PubMed          Journal:  Comput Biol Chem        ISSN: 1476-9271            Impact factor:   2.877


  3 in total

Review 1.  Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

Authors:  Vertika Gautam; Anand Gaurav; Neeraj Masand; Vannajan Sanghiran Lee; Vaishali M Patil
Journal:  Mol Divers       Date:  2022-07-11       Impact factor: 3.364

Review 2.  Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review.

Authors:  Kristy A Carpenter; Xudong Huang
Journal:  Curr Pharm Des       Date:  2018       Impact factor: 3.116

Review 3.  The Roles of the NLRP3 Inflammasome in Neurodegenerative and Metabolic Diseases and in Relevant Advanced Therapeutic Interventions.

Authors:  Rameez Hassan Pirzada; Nasir Javaid; Sangdun Choi
Journal:  Genes (Basel)       Date:  2020-01-27       Impact factor: 4.096

  3 in total

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