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The use of machine learning modeling, virtual screening, molecular docking, and molecular dynamics simulations to identify potential VEGFR2 kinase inhibitors.
Salimi, Abbas; Lim, Jong Hyeon; Jang, Jee Hwan; Lee, Jin Yong.
Affiliation
  • Salimi A; Department of Chemistry, Sungkyunkwan University, Suwon, 16419, Korea.
  • Lim JH; Department of Chemistry, Sungkyunkwan University, Suwon, 16419, Korea.
  • Jang JH; School of Materials Science and Engineering, Sungkyunkwan University, Suwon, 16419, Korea. jhjang@ucaretron.com.
  • Lee JY; Ucaretron Inc., No. 3508, 40, Simin-daero 365 beon-gil, Dongan-gu, Anyang-si, Gyeonggi-do, Korea. jhjang@ucaretron.com.
Sci Rep ; 12(1): 18825, 2022 11 05.
Article in En | MEDLINE | ID: mdl-36335233
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Vascular Endothelial Growth Factor A / Molecular Dynamics Simulation Type of study: Diagnostic_studies / Prognostic_studies / Screening_studies Language: En Journal: Sci Rep Year: 2022 Document type: Article Country of publication: United kingdom
Fulltext
Add to My VHL
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Vascular Endothelial Growth Factor A / Molecular Dynamics Simulation Type of study: Diagnostic_studies / Prognostic_studies / Screening_studies Language: En Journal: Sci Rep Year: 2022 Document type: Article Country of publication: United kingdom