Quantitative Structure‐Activity Relationship Analysis and a Combined Ligand‐Based/Structure‐Based Virtual Screening Study for Glycogen Synthase Kinase‐3. Issue 9 (2nd September 2014)

• Record Type: Journal Article
• Authors: Fu, Gang; Liu, Sheng; Nan, Xiaofei; Dale, Olivia R.; Zhao, Zhendong; Chen, Yixin; Wilkins, Dawn E.; Manly, Susan P.; Cutler, Stephen J.; Doerksen, Robert J.
• Is Part Of: Molecular informatics. Volume 33:Issue 9(2014:Sep.)
• Page Start: 627
• Page End: 640
• Publication Date: 2014-09-02
• Subjects: Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201400045
• Languages: English
• ISSNs: 1868-1743

Abstract

<abstract abstract-type="main" xml:lang="en"> <title>Abstract</title> <p>Glycogen synthase kinase‐3 (GSK‐3) is a multifunctional serine/threonine protein kinase which regulates a wide range of cellular processes, involving various signalling pathways. GSK‐3β has emerged as an important therapeutic target for diabetes and Alzheimer's disease. To identify structurally novel GSK‐3β inhibitors, we performed virtual screening by implementing a combined ligand‐based/structure‐based approach, which included quantitative structure‐activity relationship (QSAR) analysis and docking prediction. To integrate and analyze complex data sets from multiple experimental sources, we drafted and validated a hierarchical QSAR method, which adopts a two‐level structure to take data heterogeneity into account. A collection of 728 GSK‐3 inhibitors with diverse structural scaffolds was obtained from published papers that used different experimental assay protocols. Support vector machines and random forests were implemented with wrapper‐based feature selection algorithms to construct predictive learning models. The best models for each single group of compounds were then used to build the final hierarchical QSAR model, with an overall <italic>R</italic><sup>2</sup> of 0.752 for the 141 compounds in the test set. The compounds obtained from the virtual screening experiment were tested for GSK‐3β inhibition. The bioassay results confirmed that 2 hit compounds are indeed GSK‐3β inhibitors exhibiting sub‐micromolar inhibitory activity, and therefore validated our combined ligand‐based/structure‐based approach as effective for virtual screening experiments.</p> </abstract>