Discovering NDM-1 inhibitors using molecular substructure embeddings representations

Abstract: 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.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Discovering NDM-1 inhibitors using molecular substructure embeddings representations ; volume:20 ; number:2 ; year:2023 ; extent:20 ; Journal of integrative bioinformatics ; 20, Heft 2 (2023) (gesamt 20)

Contributors, Places and Time

• Creator: Papastergiou, Thomas ; Azé, Jérôme ; Bringay, Sandra ; Louet, Maxime ; Poncelet, Pascal ; Rosales-Hurtado, Miyanou ; Vo-Hoang, Yen ; Licznar-Fajardo, Patricia ; Docquier, Jean-Denis ; Gavara, Laurent

Further information

• DOI: 10.1515/jib-2022-0050
• URN: urn:nbn:de:101:1-2023080114044737753376
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 14.08.2025, 10:57 AM CEST