ArticleMolecular diversity2024
A consensual machine-learning-assisted QSAR model for effective bioactivity prediction of xanthine oxidase inhibitors using molecular fingerprints.
Article in Molecular diversity, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Interpretable QSAR models for acute oral toxicity via tuned XGBoost and hybrid data sampling techniques.Scientific reports · 2026Article
- Development of machine learning-based multi-task quantitative structure-activity relationship models for predicting toxicities in six human organ systems.Computational toxicology (Amsterdam, Netherlands) · 2026Article
- Drug-induced adverse events in modern pharmacotherapy: mechanisms, clinical manifestations, and implications for risk assessment and management.Frontiers in pharmacology · 2026Review
- Unveiling potent xanthine oxidase inhibitors in two Balanophora spp. using machine learning-based virtual screening and molecular docking approach.Scientific reports · 2025Article
- Machine-learning and simulation identify food-derived xanthine oxidase inhibitors with in-vitro activity.Scientific reports · 2025Article
- The future of pharmaceuticals: Artificial intelligence in drug discovery and development.Journal of pharmaceutical analysis · 2025Review
- Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors.Computational and structural biotechnology journal · 2024Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Xanthine oxidase inhibitors (XOIs) have been widely studied due to the promising potential as safe and effective therapeutics in hyperuricemia and gout. Currently, available XOI molecules have been developed from different experiments but they are with the wide structure diversity and significant varying bioactivities. So it is of great practical significance to present a consensual QSAR model for effective bioactivity prediction of XOIs based on a systematic compiling of these XOIs across different experiments. In this work, 249 XOIs belonging to 16 scaffolds were collected and were integrated into a consensual dataset by introducing the concept of IC
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Registered trials
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