HMMF: a hybrid multi-modal fusion framework for predicting drug side effect frequencies

作者：Wuyong Liu, Jingyu Zhang, Guanyu Qiao, Jilong Bian, Benzhi Dong, Yang Li · 发表于：BMC Bioinformatics · 年份：2024 · DOI：10.1186/s12859-024-05806-6 · 被引用次数：12 · 研究领域：Computational Drug Discovery Methods、Pharmacovigilance and Adverse Drug Reactions、Biomedical Text Mining and Ontologies

BACKGROUND: The identification of drug side effects plays a critical role in drug repositioning and drug screening. While clinical experiments yield accurate and reliable information about drug-related side effects, they are costly and time-consuming. Computational models have emerged as a promising alternative to predict the frequency of drug-side effects. However, earlier research has primarily centered on extracting and utilizing representations of drugs, like molecular structure or interaction graphs, often neglecting the inherent biomedical semantics of drugs and side effects. RESULTS: To address the previously mentioned issue, we introduce a hybrid multi-modal fusion framework (HMMF) for predicting drug side effect frequencies. Considering the wealth of biological and chemical semantic information related to drugs and side effects, incorporating multi-modal information offers additional, complementary semantics. HMMF utilizes various encoders to understand molecular structures, biomedical textual representations, and attribute similarities of both drugs and side effects. It then models drug-side effect interactions using both coarse and fine-grained fusion strategies, effectively integrating these multi-modal features. CONCLUSIONS: HMMF exhibits the ability to successfully detect previously unrecognized potential side effects, demonstrating superior performance over existing state-of-the-art methods across various evaluation metrics, including root mean squared error an...