Machine Learning Modeling Predicting Vascular Endothelial Growth Factor Receptor 2 (VEGFR2) Inhibitors Structure-Activity Relationships Using Quantum DFT Descriptors-Reference-Cited by-同舟云学术

Machine Learning Modeling Predicting Vascular Endothelial Growth Factor Receptor 2 (VEGFR2) Inhibitors Structure-Activity Relationships Using Quantum DFT Descriptors

Published:2023-10-16 Issue: Volume: Page:
ISSN:
Container-title:2023 XLIX Latin American Computer Conference (CLEI)
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Author:

Salas José Abreu¹,Oliva Roberto Espinosa²,Mirabal Pedro³,Lamazares Emilio⁴,Mena-Ulacia Karel⁵

Affiliation:

1. Instituto Universitario de Investigación Informática, Universidad de Alicante,Alicante,España

2. Universidad de Tarapacá,Departamento de Ingeniería en Computación e Informática,Arica,Chile

3. Unversidad Católica de Temuco,Departamento de Ingeniería Informática,Temuco,Chile

4. Universidad de Concepción,Pathophysiology Department,Concepción,Chile

5. Universidad Católica de Temuco,Departamento de Ciencias Biológicas y Químicas,Temuco,Chile

Funder

Universidad de Tarapacá

Fondecyt

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10346085/10345779/10346152.pdf?arnumber=10346152

Reference66 articles.

1. Design, synthesis, and evaluation of imidazo[1,2-b]pyridazine derivatives having a benzamide unit as novel VEGFR2 kinase inhibitors

2. STING activation reprograms tumor vasculatures and synergizes with VEGFR2 blockade

3. Engineered Extracellular Vesicles From Human Periodontal-Ligament Stem Cells Increase VEGF/VEGFR2 Expression During Bone Regeneration

4. Discovery of N -[5-({2-[(cyclopropylcarbonyl)amino]imidazo[1,2- b ]pyridazin-6-yl}oxy)-2-methylphenyl]-1,3-dimethyl-1 H -pyrazole-5-carboxamide (TAK-593), a highly potent VEGFR2 kinase inhibitor

5. Benzofuran‐isatin conjugates as potent VEGFR‐2 and cancer cell growth inhibitors