BDN-DDI: A bilinear dual-view representation learning framework for drug–drug interaction prediction

Ning, Gong; Sun, Yeping; Ling, Jie; Chen, Jijia; He, Jianxing

doi:10.1016/j.compbiomed.2023.107340

Cited by 6 publications

(1 citation statement)

References 33 publications

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“…Using a biomedical bipartite network, a link prediction model called the multiview GCN (MVGCN) was developed for predicting relationships among drugs, miRNA, and diseases, as well as drug–target interactions. , The SynGeNet method exploited the interplay between gene expression and network mining to scrutinize disease graphs and forecast combinations of drugs. Some studies have explored the collection of multimodal biological information to construct a knowledge graph (KG) for drug–drug interaction prediction. − Currently, numerous studies have focused on constructing biomedical heterogeneous graphs or knowledge graphs for predicting drug synergy. For instance, a network-embedding-based prediction model (NEXGB) combined topological information from protein–protein interaction (PPI) networks with information about drug–cancer cell line target proteins to generate features for drugs and cell lines .…”

Section: Introductionmentioning

confidence: 99%

HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction

Cheng,

Wang,

Liu

et al. 2024

J. Chem. Inf. Model.

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Drug synergy therapy is a promising strategy for cancer treatment. However, the extensive variety of available drugs and the time-intensive process of determining effective drug combinations through clinical trials pose significant challenges. It requires a reliable method for the rapid and precise selection of drug synergies. In response, various computational strategies have been developed for predicting drug synergies, yet the exploitation of heterogeneous biological network features remains underexplored. In this study, we construct a heterogeneous graph that encompasses diverse biological entities and interactions, utilizing rich data sets from sources, such as DrugCombDB, PubChem, UniProt, and cancer cell line encyclopedia (CCLE). We initialize node feature representations and introduce a novel virtual node to enhance drug representation. Our proposed method, the heterogeneous graph attention network for drug−drug synergy prediction (HANSynergy), has been experimentally validated to demonstrate that the heterogeneous graph attention network can extract key node features, efficiently harness the diversity of information, and further enhance network functionality through the incorporation of a multihead attention mechanism. In the comparative experiment, the highest accuracy (Acc) and area under the curve (AUC) are 0.877 and 0.947, respectively, in DrugCombDB_early data set, demonstrating the superiority of HANSynergy over the competing methods. Moreover, protein− protein interactions are important in understanding the mechanism of action of drugs. The heterogeneous attention mechanism facilitates protein−protein interaction analysis. By analyzing the changes of attention weight before and after heterogeneous network training, we investigated proteins that may be associated with drug combinations. Additionally, case studies align our findings with existing research, underscoring the potential of HANSynergy in drug synergy prediction. This advancement not only contributes to the burgeoning field of drug synergy prediction but also holds the potential to provide valuable insights and uncover new drug synergies for combating cancer.

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