Repository logo
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Српски
  • Yкраї́нська
  • Log In
    New user? Click here to register. Have you forgotten your password?
Repository logo
  • Communities & Collections
  • All of DSpace
  • English
  • Català
  • Čeština
  • Deutsch
  • Español
  • Français
  • Gàidhlig
  • Italiano
  • Latviešu
  • Magyar
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Suomi
  • Svenska
  • Türkçe
  • Tiếng Việt
  • Қазақ
  • বাংলা
  • हिंदी
  • Ελληνικά
  • Српски
  • Yкраї́нська
  • Log In
    New user? Click here to register. Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "BENMEHACHE, Cherrouk"

Now showing 1 - 1 of 1
Results Per Page
Sort Options
  • Loading...
    Thumbnail Image
    Item
    Anti-malarial evaluation activity of some bioactive substances: an advanced computational approach leading to novel drug pre-formulation
    (OTMANINE Khaled, 2025) GHERIOUNE, Nesrine; BENMEHACHE, Cherrouk
    This study aims to evaluate the effectiveness of four plant compounds as potential antimalarials: Artemisinin from Artemisia annua extract, Chamazulene from Artemisia afra extract, and both Thymol and Carvacrol from Thymus vulgaris (Thyme) extract. The study relied on computational simulation techniques, targeting four key parasite proteins: PFNDH2, Plasmepsin, and PF-Plasmepsin_2, as well as Apicoplast DNA polymerase. Molecular docking results showed strong interactions and precisely defined binding sites, particularly between Chamazulene and PF-Plasmepsin_2 (ΔG = -7.7 kcal/mol) and Artemisinin with PF-NDH2 (ΔG = -7.4kcal/mol). In addition, a Quantitative StructureActivity Relationship (QSAR) model was developed using Multiple Linear Regression (MLR) and Support Vector Regression (SVR), based on a dataset comprising 71 derivatives of known antimalarial compounds. The model demonstrated high predictive accuracy (R² > 0.93, and RMSE < 0.82).

DSpace software copyright © 2002-2026 LYRASIS

  • Cookie settings
  • Privacy policy
  • End User Agreement
  • Send Feedback