The pharmacophoric space of glycogen synthase kinase-3beta (GSK-3beta) was explored using two diverse sets of inhibitors. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select optimal combination of pharmacophores and physicochemical descriptors that access self-consistent and predictive quantitative structure-activity relationship (QSAR) against 132 training compounds ( r (2) 123 = 0.663, F = 24.6, r (2) LOO = 0.592, r (2) PRESS against 29 external test inhibitors = 0.695). Two orthogonal pharmacophores emerged in the QSAR, suggesting the existence of at least two distinct binding modes accessible to ligands within GSK-3beta binding pocket. The validity of the QSAR equation and the associated pharmacophores was established by the identification of three nanomolar GSK-3beta inhibitors retrieved from our in-house-built structural database of established drugs, namely, hydroxychloroquine, cimetidine, and gemifloxacin. Docking studies supported the binding modes suggested by the pharmacophore/QSAR analysis. In addition to being excellent leads for subsequent optimization, the anti-GSK-3beta activities of these drugs should have significant clinical implications.
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Curr Top Med Chem
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Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research (JSS AHER), Mysuru, Karnataka, India.
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January 2025
School of Life Science and Technology, ShanghaiTech University, 201210 Shanghai, China. Electronic address:
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Research and Development Center, Bioinnov Solutions LLP, Salem, India.
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Department of Electrical Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia.
Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system with an unknown etiology. While disease-modifying therapies can slow progression, there is a need for more effective treatments. Quantitative structure-activity relationship (QSAR) modeling using topological indices derived from chemical graph theory is a promising approach to rationally design new drugs for MS.
View Article and Find Full Text PDFJ Drug Target
January 2025
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A significant area of computer science called artificial intelligence (AI) is successfully applied to the analysis of intricate biological data and the extraction of substantial associations from datasets for a variety of biomedical uses. AI has attracted significant interest in biomedical research due to its features: (i) better patient care through early diagnosis and detection; (ii) enhanced workflow; (iii) lowering medical errors; (v) lowering medical costs; (vi) reducing morbidity and mortality; (vii) enhancing performance; (viii) enhancing precision; and (ix) time efficiency. Quantitative metrics are crucial for evaluating AI implementations, providing insights, enabling informed decisions, and measuring the impact of AI-driven initiatives, thereby enhancing transparency, accountability, and overall impact.
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