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Machine learning for the prediction of safe and biologically active organophosphorus molecules

Par Conseil national de recherches du Canada

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DOITrouver le DOI : https://doi.org/10.21428/594757db.7b542d48
AuteurRechercher : 1; Rechercher : 1; Rechercher : 1; Rechercher :
Affiliation
  1. Conseil national de recherches du Canada. Technologies numériques
FormatTexte, Article
Conférence36th Canadian Conference on Artificial Intelligence, June 5-9, 2023, Montreal, QC, Canada
Sujetmolecule design; organophosphorus molecule; recurrent neural networks; self-attention
Résumé
Drug discovery is a complex process with a large molecular space to be considered. By constraining the search space, the fragment based drug design is an approach that can effectively sample the chemical space of interest. Here we propose a framework of Recurrent Neural Networks (RNN) with an attention model to sample the chemical space of organophosphorus molecules using the fragment-based approach. The framework is trained with a ZINC dataset that is screened for high druglikeness scores. The goal is to predict molecules with similar biological action modes as organophosphorus pesticides or chemical warfare agents yet less toxic to humans. The generated molecules contain a starting fragment of PO2F but have a bulky hydrocarbon side chain limiting its binding effectiveness to the targeted protein.
Date de publication
Maison d’éditionCanadian Artificial Intelligence Association
Licence
Dans
  • Proceedings of the 36th Canadian Conference on Artificial Intelligence, 2023S3 (5 juin 2023).
Langueanglais
Publications évaluées par des pairsOui
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Identificateur de l’enregistrement65c47bd7-003b-4165-a109-46b6b99341c8
Enregistrement créé2023-07-07
Enregistrement modifié2023-07-10
Date de modification :