DDIPrompt: Drug-Drug Interaction Event Prediction based on Graph Prompt Learning

arXiv:2402.11472v1 Announce Type: cross
Abstract: Recently, Graph Neural Networks have become increasingly prevalent in predicting adverse drug-drug interactions (DDI) due to their proficiency in modeling the intricate associations between atoms and functional groups within and across drug molecules. However, they are still hindered by two significant challenges: (1) the issue of highly imbalanced event distribution, which is a common but critical problem in medical datasets where certain interactions are vastly underrepresented. This imbalance poses a substantial barrier to achieving accurate …

abstract arxiv become challenges cs.ai cs.lg event functional graph graph neural networks interactions issue modeling molecules networks neural networks prediction prompt prompt learning q-bio.bm type