Evidence mapPaperPMID 39350548Full record

ArticleAnti-inflammatory & anti-allergy agents in medicinal chemistry2025

Molecular Docking, Pharmacophore Modeling and ADMET Prediction of Novel Heterocyclic Leads as Glucokinase Activators.

Anuradha Mehra, Amit Mittal, Shivangi Singh

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Article in Anti-inflammatory & anti-allergy agents in medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1 citing paper in PubMed.

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5 · Who and what money

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3 authors.

Anuradha MehraDepartment of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Lovely Professional University, Jalandhar, Delhi G.T. Road (NH-1), Phagwara, Punjab, 144411, India.ORCID 0000-0002-7405-8809
Amit MittalDepartment of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Lovely Professional University, Jalandhar, Delhi G.T. Road (NH-1), Phagwara, Punjab, 144411, India.ORCID 0000-0001-9689-1642
Shivangi SinghDepartment of Pharmaceutical Chemistry, School of Pharmaceutical Sciences, Lovely Professional University, Jalandhar, Delhi G.T. Road (NH-1), Phagwara, Punjab, 144411, India.

Funding

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6 · The paper itself

Abstract

backgroundA pivotal impetus has driven the development of numerous small molecules aiming to improve therapeutic strategies for type 2 diabetes. Glucokinase (GK) activation has been offered a new realm of therapeutic antidiabetic activity with novel heterocyclic derivatives. In the context of antidiabetic drug design, GK is an interesting and newly validated target. A key enzyme needed for blood glucose homeostasis is Glucokinase, which is dysfunctional in individuals with type 2 diabetes. Heterocyclic derivatives are utilized in this innovative approach to activate GK enzymes as medicinal agents that will significantly improve type 2 diabetes management.

objectivesTo address type 2 diabetes, as well as minimize unwanted side effects, this research endeavor aimed to develop activators of glucokinase.

methodsA rigorous scrutiny was conducted of the Maybridge online repository, which houses a formidable collection of 53,000 lead compounds. A collection of 125 compounds that contain the thiazolidinedione core was selected from this extensive collection. The structures were generated using ChemDraw 2D, stabilized conformation with ChemBioDraw Ultra, and docked using Auto Dock Vina 1.5.6 in this methodology. In addition, log P was predicted online using the Swiss ADME algorithm. The PKCSM software was used to predict the toxicity of the leading compounds.

resultsThe highest binding affinity was found for AS72 and AS108 to GK receptors. GI absorption and excretion of these compounds were efficient due to Lipinski's Rule of Five compliance. When compared with the standard drugs Dorzagliatin (GKA) and MRK (co-crystallized ligand), these substances demonstrated a notable lack of AMES toxicity, skin sensitization, and hepatotoxicity.

conclusionIn recent studies, lead molecules that possess enhanced pharmacokinetic profiles, increased binding affinity, and lower toxicity were developed to act as glucokinase activators.

Indexed as

Enzyme ActivatorsGlucokinaseHeterocyclic CompoundsHypoglycemic AgentsDiabetes Mellitus, Type 2HumansMolecular Docking SimulationPharmacophoreThiazolidinedionesEnzyme ActivatorsGlucokinaseHeterocyclic CompoundsHypoglycemic AgentsThiazolidinedionesbinding affinityDiabetesdockingdrug designmechanism.thiazolidinedione derivatives

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.