DrugDAGT: a dual-attention graph transformer with contrastive learning improves drug-drug interaction prediction.

ABSTRACT:

Background

Drug-drug interactions (DDIs) can result in unexpected pharmacological outcomes, including adverse drug events, which are crucial for drug discovery. Graph neural networks have substantially advanced our ability to model molecular representations; however, the precise identification of key local structures and the capture of long-distance structural correlations for better DDI prediction and interpretation remain significant challenges.

Results

Here, we present DrugDAGT, a dual-attention graph transformer framework with contrastive learning for predicting multiple DDI types. The dual-attention graph transformer incorporates attention mechanisms at both the bond and atomic levels, thereby enabling the integration of short and long-range dependencies within drug mol

SUBMITTER: Chen Y 

PROVIDER: S-EPMC11472440 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature