ArticleScientific reports2024
Structure-based drug-development study against fibroblast growth factor receptor 2: molecular docking and Molecular dynamics simulation approaches.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
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- Single-nucleus and machine-learning integration identifies HLA-DRA and FTL as immune-metabolic axes and traditional Chinese medicine-targetable hubs in calcific aortic valve disease.Journal of computer-aided molecular design · 2026Article
- In Silico Molecular Docking and Pharmacokinetic Evaluation of Cannabinoid Derivatives as Multi-Target Inhibitors for EGFR, VEGFR-1, and VEGFR-2 Proteins.Current issues in molecular biology · 2026Article
- Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies.Computational and structural biotechnology journal · 2026Review
- Unraveling the PFOS-NSCLC axis: integrated network toxicology, machine learning, and causal inference identify EIF4EBP1 as a key molecular hub.Frontiers in public health · 2026Article
- Computational identification of Moracin G and Isolonchocarpin as potent MELK inhibitors for anticancer drug discovery.Scientific reports · 2025Article
- Exploiting the Achilles' heel of cancer through a structure-based drug-repurposing approach and experimental validation of top drugs using the TRAP assay.Molecular diversity · 2025Article
- Integrating Multi-Domain Approach for Identification of Neo Anti-DHPS Inhibitors Against PathogenicBiology · 2025Article
- Structure-guided virtual screening reveals phytoconstituents as potent cathepsin B inhibitors: Implications for cancer, traumatic brain injury, and Alzheimer's disease.Frontiers in molecular biosciences · 2025Article
- Natural Compounds Targeting Thymic Stromal Lymphopoietin (TSLP): A Promising Therapeutic Strategy for Atopic Dermatitis.Biomolecules · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
Developing new therapeutic strategies to target specific molecular pathways has become a primary focus in modern drug discovery science. Fibroblast growth factor receptor 2 (FGFR2) is a critical signaling protein involved in various cellular processes and implicated in numerous diseases, including cancer. Existing FGFR2 inhibitors face limitations like drug resistance and specificity issues. In this study, we present an integrated structure-based bioinformatics analysis to explore the potential of FGFR2 inhibitors-like compounds from the PubChem database with the Tanimoto threshold of 80%. We conducted a structure-based virtual screening approach on a dataset comprising 2336 compounds sourced from the PubChem database. Primarily, the selection of promising compounds was based on several criteria, such as drug-likeness, binding affinities, docking scores, and selectivity. Further, we conducted all-atom molecular dynamics (MD) simulations for 200 ns, followed by an essential dynamics analysis. Finally, a promising FGFR2 inhibitor with PubChem CID:507883 (1-[7-(1H-benzimidazol-2-yl)-4-fluoro-1H-indol-3-yl]-2-(4-benzoylpiperazin-1-yl)ethane-1,2-dione) was screened out from the study. This compound indicates a higher potential for inhibiting FGFR2 than the control inhibitor, Zoligratinib. The identified compound, CID:507883 shows >80% structural similarity with Zoligratinib. ADMET analysis showed promising pharmacokinetic potential of the screened compound. Overall, the findings indicate that the compound CID:507883 may have promising potential to serve as a lead candidate against FGFR2 and could be further exploited in therapeutic development.
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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 OpenQuestion graph.