Discovering NDM-1 inhibitors using molecular substructure embeddings representations - Hydrosciences Montpellier Accéder directement au contenu
Article Dans Une Revue Journal of Integrative Bioinformatics Année : 2023

Discovering NDM-1 inhibitors using molecular substructure embeddings representations

Résumé

NDM-1 (New-Delhi-Metallo-β-lactamase-1) is an enzyme developed by bacteria that is implicated in bacteria resistance to almost all known antibiotics. In this study, we deliver a new, curated NDM-1 bioactivities database, along with a set of unifying rules for managing different activity properties and inconsistencies. We define the activity classification problem in terms of Multiple Instance Learning, employing embeddings corresponding to molecular substructures and present an ensemble ranking and classification framework, relaying on a k-fold Cross Validation method employing a per fold hyper-parameter optimization procedure, showing promising generalization ability. The MIL paradigm displayed an improvement up to 45.7 %, in terms of Balanced Accuracy, in comparison to the classical Machine Learning paradigm. Moreover, we investigate different compact molecular representations, based on atomic or bi-atomic substructures. Finally, we scanned the Drugbank for strongly active compounds and we present the top-15 ranked compounds.
Fichier principal
Vignette du fichier
10.1515_jib-2022-0050.pdf (3.42 Mo) Télécharger le fichier
Origine Fichiers éditeurs autorisés sur une archive ouverte
licence

Dates et versions

hal-04575121 , version 1 (16-05-2024)

Licence

Identifiants

Citer

Thomas Papastergiou, Jérôme Azé, Sandra Bringay, Maxime Louet, Pascal Poncelet, et al.. Discovering NDM-1 inhibitors using molecular substructure embeddings representations. Journal of Integrative Bioinformatics, 2023, 20 (2), ⟨10.1515/jib-2022-0050⟩. ⟨hal-04575121⟩
0 Consultations
0 Téléchargements

Altmetric

Partager

Gmail Mastodon Facebook X LinkedIn More