Learning self-supervised molecular representations for drug–drug interaction prediction

Learning self-supervised molecular representations for drug–drug interaction prediction

Abstract Drug–drug interactions (DDI) are a critical concern in healthcare due to their potential to cause adverse effects and compromise patient safety. Supervised machine learning models for DDI prediction need to be optimized to learn abstract, transferable features, and generalize to larger chem...

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Bibliographic Details
Main Authors: Rogia Kpanou, Patrick Dallaire, Elsa Rousseau, Jacques Corbeil
Format: Article
Language:English
Published: BMC 2024-01-01
Series:BMC Bioinformatics
Subjects:
Drug–drug interactions
Contrastive learning
Deep neural networks
Representation learning
Fine-tuning
Smiles enumeration
Online Access:https://doi.org/10.1186/s12859-024-05643-7

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