R2-DDI: relation-aware feature refinement for drug-drug interaction prediction.
Brief Bioinform
; 24(1)2023 01 19.
Article
em En
| MEDLINE
| ID: mdl-36573491
ABSTRACT
Precisely predicting the drug-drug interaction (DDI) is an important application and host research topic in drug discovery, especially for avoiding the adverse effect when using drug combination treatment for patients. Nowadays, machine learning and deep learning methods have achieved great success in DDI prediction. However, we notice that most of the works ignore the importance of the relation type when building the DDI prediction models. In this work, we propose a novel R$^2$-DDI framework, which introduces a relation-aware feature refinement module for drug representation learning. The relation feature is integrated into drug representation and refined in the framework. With the refinement features, we also incorporate the consistency training method to regularize the multi-branch predictions for better generalization. Through extensive experiments and studies, we demonstrate our R$^2$-DDI approach can significantly improve the DDI prediction performance over multiple real-world datasets and settings, and our method shows better generalization ability with the help of the feature refinement design.
Palavras-chave
Texto completo:
1
Bases de dados:
MEDLINE
Assunto principal:
Efeitos Colaterais e Reações Adversas Relacionados a Medicamentos
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Revista:
Brief Bioinform
Assunto da revista:
BIOLOGIA
/
INFORMATICA MEDICA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
China