Contrastive learning-based multi-mechanism disentangled assessment for drug-drug interaction.

ABSTRACT:

Background

Polypharmacy's ability to circumvent acquired resistance to single drug makes it a critical strategy for treating complex diseases. However, it inevitably carries risks of drug-drug interactions (DDIs) that may alter pharmacological activities and potentially lead to severe adverse events or mortality. Computational assessment of drug combination has emerged as an effective approach to support clinical decision-making. Current risk identification methods focus on mining historical interaction patterns to uncover underlying mechanisms, yet face challenges from data sparsity. While data augmentation strategy can mitigate such problem, conventional approaches often introduce noise that obscures core pharmacological mechanisms, undermining safety evaluation.

Results

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SUBMITTER: Zhang J 

PROVIDER: S-EPMC12659097 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature