ArticleBMC chemistry2024
QSAR analysis of VEGFR-2 inhibitors based on machine learning, Topomer CoMFA and molecule docking.
Article in BMC chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 19 citations in OpenAlex.
- Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies.Computational and structural biotechnology journal · 2026Review
- Identification of potent inhibitors of potential VEGFR2: a graph neural network-based virtual screening andJournal of enzyme inhibition and medicinal chemistry · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Explainable AI-driven prediction of APE1 inhibitors: enhancing cancer therapy with machine learning models and feature importance analysis.Molecular diversity · 2025Article
- Exploring novel furochochicine derivatives as promising JAK2 inhibitors in HeLa cells: Integrating docking, QSAR-ML, MD simulations, and experiments.Computational and structural biotechnology journal · 2025Article
- From Deep Learning to the Discovery of Promising VEGFR-2 Inhibitors.ChemMedChem · 2024Article
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
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Abstract
VEGFR-2 kinase inhibitors are clinically approved drugs that can effectively target cancer angiogenesis. However, such inhibitors have adverse effects such as skin toxicity, gastrointestinal reactions and hepatic impairment. In this study, machine learning and Topomer CoMFA, which is an alignment-dependent, descriptor-based method, were employed to build structural activity relationship models of potentially new VEGFR-2 inhibitors. The prediction ac-curacy of the training and test sets of the 2D-SAR model were 82.4 and 80.1%, respectively, with KNN. Topomer CoMFA approach was then used for 3D-QSAR modeling of VEGFR-2 inhibitors. The coefficient of q2 for cross-validation of the model 1 was greater than 0.5, suggesting that a stable drug activity-prediction model was obtained. Molecular docking was further performed to simulate the interactions between the five most promising compounds and VEGFR-2 target protein and the Total Scores were all greater than 6, indicating that they had a strong hydrogen bond interactions were present. This study successfully used machine learning to obtain five potentially novel VEGFR-2 inhibitors to increase our arsenal of drugs to combat cancer.
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