ReviewComputational and structural biotechnology journal2024
Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors.
Review in Computational and structural biotechnology journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Review
- Discovery of small molecule inhibitors of liver X receptor for pediatric metabolic disorders by structure based screening, modelling, dft analysis and MD simulation.Scientific reports · 2026Article
- AI-driven drug-target interaction prediction: current progress, challenges, and future roadmap for precision medicine.Journal of computer-aided molecular design · 2026Review
- Review
- Computational Study of Enzyme Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Article
- Mechanistic study of α-mangostin derivatives as potent α-glucosidase inhibitors.Molecular diversity · 2025Article
- Development of QSAR Models and Web Applications for Predicting hDHFR Inhibitor Bioactivity Using Machine Learning.Molecules (Basel, Switzerland) · 2025Article
- Elucidation of Artemisinin as a Potent GSK3β Inhibitor for Neurodegenerative Disorders via Machine Learning-Driven QSAR and Virtual Screening of Natural Compounds.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Design, synthesis, and evaluation of triazolo[1,5-a]pyridines as novel and potent α‑glucosidase inhibitors.Scientific reports · 2025Article
- Article
- Leveraging machine learning models in evaluating ADMET properties for drug discovery and development.ADMET & DMPK · 2025Review
- Anti-cancer activity elucidation of geissolosimine as an MDM2-p53 interaction inhibitor: An in-silico study.PloS one · 2025Article
- Machine learning prediction of intestinal α-glucosidase inhibitors using a diverse set of ligands: a drug repurposing effort with drugBank database screening.In silico pharmacology · 2025Article
- Green carbon dots in the era of AI: sustainable synthesis, intelligent drug delivery, advanced diagnostics, and bioimaging.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- Artificial Intelligence-Driven Computational Approaches in the Development of Anticancer Drugs.Cancers · 2024Article
Corrections and comments
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence is transforming drug discovery, particularly in the hit identification phase of therapeutic compounds. One tool that has been instrumental in this transformation is Quantitative Structure-Activity Relationship (QSAR) analysis. This computer-aided drug design tool uses machine learning to predict the biological activity of new compounds based on the numerical representation of chemical structures against various biological targets. With diabetes mellitus becoming a significant health challenge in recent times, there is intense research interest in modulating antidiabetic drug targets. α-Glucosidase is an antidiabetic target that has gained attention due to its ability to suppress postprandial hyperglycaemia, a key contributor to diabetic complications. This review explored a detailed approach to developing QSAR models, focusing on strategies for generating input variables (molecular descriptors) and computational approaches ranging from classical machine learning algorithms to modern deep learning algorithms. We also highlighted studies that have used these approaches to develop predictive models for α-glucosidase inhibitors to modulate this critical antidiabetic drug target.
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Registered trials
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 OpenQuestion graph.