Pyrazines et Perception Olfactive : Chimie, Modélisation Moléculaire et Perspectives Industrielles
Language: French
Published by Eliva Press, 2025
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 61.30
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Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - Dans cette étude, une approche intégrée combinant des méthodes 2D-QSPR, 3D-QSPR, Monte Carlo et apprentissage automatique a été déployée pour modéliser la relation structure-odeur de 78 composés odorants dérivés de la 1,4-pyrazine, dans le but de guider rationnellement la conception de nouveaux arômes à usage alimentaire. Les modèles 2D-QSPR, en particulier HQSPR/BC, ont présenté une excellente qualité d'ajustement avec un Q _LOO = 0,832 et un R = 0,916. Du côté des modèles 3D-QSPR, CoMSIA/SEH a fourni des performances robustes (Q = 0,624 ; R _CV = 0,590 ; R _nonCV = 0,932 ; R _bootstrap = 0,963), tandis que le modèle Topomer CoMFA/SE a obtenu R _training = 0,899 et R _test = 0,916, démontrant sa bonne capacité de généralisation. En parallèle, la méthode Monte Carlo, appliquée à des descripteurs SMILES optimisés, a permis de générer un modèle TF2 fiable, avec R _entrainement = 0,957 et R _test = 0,834, évalué à l'aide des indices d'idéalité et de contradiction de la corrélation. Par ailleurs, plusieurs algorithmes de machine learning ont été testés, et les meilleurs résultats ont été obtenus avec les modèles Extra Trees (ET), Random Forest (RF) et Bagging (Bag), qui ont démontré une excellente capacité prédictive sur les données de test, avec respectivement R = 0,814, 0,802 et 0,784, accompagnés de faibles erreurs RMSE de 0,816, 0,841 et 0,878. Ces modèles ont surpassé d'autres approches comme XGBoost, LightGBM ou Decision Tree, souvent sujets à un surapprentissage ou à une dispersion élevée en test. L'ensemble de ces modèles a permis la conception rationnelle de 36 nouvelles molécules odorantes, dont les niveaux d'activité ont été prédits. Ces composés ont ensuite été évalués par docking moléculaire vis-à-vis de la Porcine Odorant Binding Protein (pOBP), utilisant la 2-isobutyl-3-méthoxypyrazine comme ligand de référence, suivis d'analyses ADME/Tox pour estimer leur profil pharmacocinétique et de simulations de dynamique moléculaire pour vérifier la stabilité des complexes ligand-récepteur. Ces résultats fournissent une base solide et innovante pour la découverte de nouvelles entités odorantes à valeur ajoutée, combinant sélectivité, stabilité et propriétés.…
Seller Inventory # 9789999328432
- Title
- Pyrazines et Perception Olfactive : Chimie, Modélisation Moléculaire et Perspectives Industrielles
- Author
- Mohamed Ouabane
- Publisher
- Eliva Press
- Publication year
- 2025
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- French
- ISBN 10
- 9999328431
- ISBN 13
- 9789999328432
- Item weight
- 178 grams
- Dimensions
- 229x152x6 mm
"Synopsis" may belong to another edition of this title.
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| Item | 7 to 10 business days | 5 to 7 business days |
|---|---|---|
| First item | £ 29.21 | £ 37.56 |
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