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Potential inhibitors of VEGFR1, VEGFR2, and VEGFR3 developed through Deep Learning for the treatment of Cervical Cancer

Abstract Cervical cancer stands as a prevalent gynaecologic malignancy affecting women globally, often linked to persistent human papillomavirus infection. Biomarkers associated with cervical cancer, including VEGF-A, VEGF-B, VEGF-C, VEGF-D, and VEGF-E, show upregulation and are linked to angiogenes...

Ausführliche Beschreibung

Gespeichert in:
Hauptverfasser: Anuraj Nayarisseri, Mohnad Abdalla, Isha Joshi, Manasi Yadav, Anushka Bhrdwaj, Ishita Chopra, Arshiya Khan, Arshiya Saxena, Khushboo Sharma, Aravind Panicker, Umesh Panwar, Francisco Jaime Bezerra Mendonça Junior, Sanjeev Kumar Singh
Format: Artigo
Sprache:Inglês
Veröffentlicht: Nature Portfolio 2024-06-01
Schriftenreihe:Scientific Reports
Schlagworte:
VEGFR inhibitors
Machine-learning
Deep learning
Molecular docking
Molecular dynamics simulation
R programming
Online-Zugang:https://doi.org/10.1038/s41598-024-63762-w
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