ArticleScientific reports2022
The use of machine learning modeling, virtual screening, molecular docking, and molecular dynamics simulations to identify potential VEGFR2 kinase inhibitors.
Article in Scientific reports, 2022. 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.
- Clinical application of artificial intelligence technology in epilepsy.Acta epileptologica · 2026Review
- AI and experimental convergence: a synergistic pathway to JAK2 inhibitor discovery.Acta pharmacologica Sinica · 2026Article
- Design, Synthesis, Molecular Docking, and Anticancer Activity of Chalcone Derivatives as VEGFR-2 Inhibitors.Molecules (Basel, Switzerland) · 2025Article
- Machine Learning-Based High-Throughput Screening, Molecular Modeling and Quantum Chemical Analysis to Investigate Mycobacterium tuberculosis MetRS Inhibitors.ChemistryOpen · 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
- Elucidating the role of artificial intelligence in drug development from the perspective of drug-target interactions.Journal of pharmaceutical analysis · 2025Review
- AI-assisted discovery of potent FGFR1 inhibitors via virtual screening and in silico analysis.PloS one · 2025Article
- Peptides for microbe-induced cancers: latest therapeutic strategies and their advanced technologies.Cancer metastasis reviews · 2024Review
- Article
- Article
- From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors.ChemMedChem · 2024Article
- Role of CSF1R 550th-tryptophan in kusunokinin and CSF1R inhibitor binding and ligand-induced structural effect.Scientific reports · 2024Article
- Multiobjective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex.Journal of medicinal chemistry · 2023Article
- MD-Ligand-Receptor: A High-Performance Computing Tool for Characterizing Ligand-Receptor Binding Interactions in Molecular Dynamics Trajectories.International journal of molecular sciences · 2023Article
- Dietary Oncopharmacognosy as a Crosswalk between Precision Oncology and Precision Nutrition.Nutrients · 2023Review
- Natural Products in Precision Oncology: Plant-Based Small Molecule Inhibitors of Protein Kinases for Cancer Chemoprevention.Nutrients · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Targeting the signaling pathway of the Vascular endothelial growth factor receptor-2 is a promising approach that has drawn attention in the quest to develop novel anti-cancer drugs and cardiovascular disease treatments. We construct a screening pipeline using machine learning classification integrated with similarity checks of approved drugs to find new inhibitors. The statistical metrics reveal that the random forest approach has slightly better performance. By further similarity screening against several approved drugs, two candidates are selected. Analysis of absorption, distribution, metabolism, excretion, and toxicity, along with molecular docking and dynamics are performed for the two candidates with regorafenib as a reference. The binding energies of molecule1, molecule2, and regorafenib are - 89.1, - 95.3, and - 87.4 (kJ/mol), respectively which suggest candidate compounds have strong binding to the target. Meanwhile, the median lethal dose and maximum tolerated dose for regorafenib, molecule1, and molecule2 are predicted to be 800, 1600, and 393 mg/kg, and 0.257, 0.527, and 0.428 log mg/kg/day, respectively. Also, the inhibitory activity of these compounds is predicted to be 7.23 and 7.31, which is comparable with the activity of pazopanib and sorafenib drugs. In light of these findings, the two compounds could be further investigated as potential candidates for anti-angiogenesis therapy.
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