MHCLSyn:Multi-View Hypergraph Contrastive Learning for Synergistic Drug Combination Prediction  

作　　者：Lei Li Guodong Lü Chunhou Zheng Renyong Lin Yansen Su 

机构地区：[1]School of Artificial Intelligence,Anhui University,Hefei 230601,China [2]Institute of Artificial Intelligence,Hefei Comprehensive National Science Center,Hefei 230601,China [3]State Key Laboratory of Pathogenesis,Prevention,and Treatment of Central Asian High Incidence Diseases,Clinical Medical Research Institute,The First Affiliated Hospital of Xinjiang Medical University,Urumqi 830054,China

出　　处：《Big Data Mining and Analytics》2024年第4期1273-1286,共14页大数据挖掘与分析(英文)

基　　金：supported by the National Key Research and Development Program of China(No.2021YFE0102100);the National Natural Science Foundation of China(Nos.62322301,62172002,and 82060373);the Outstanding Youth Research Project of Universities in Anhui Province(No.2022AH020010);the University Synergy Innovation Program of Anhui Province(Nos.GXXT-2022-035 and GXXT-2021-039);the Xinjiang Tianshan Project(No.2022TSYCLJ0032).

摘　　要：In the field of cancer treatment,drug combination therapy appears to be a promising treatment strategy compared to monotherapy.Recently,plenty of computational models are gradually applied to prioritize synergistic drug combinations.However,the existing prediction models have not fully exploited the multi-way relations between drug combinations and cell lines.Besides,the number of identified drug-drug-cell line triplets is insufficient owning to the high cost of in vitro screening,which affects the ability of models to capture and utilize multi-way relations.To address this challenge,we design the multi-view hypergraph contrastive learning model,termed MHCLSyn,for synergistic drug combination prediction.First,the synergistic drug-drug-cell line triplets are formulated as a drug synergy hypergraph,and three task-specific hypergraphs are designed based on the drug synergy hypergraph.Then,we design a multi-view hypergraph contrastive learning with enhancement schemes,which allows for more expressive and discriminative node representation learning on drug synergy hypergraph.After that,the representations of nodes indicating drug-drug-cell line triplets are inputted to fully connected network for making predictions.Extensive experiments show MHCLSyn achieves better performance than state-of-the-art prediction models on benchmark datasets and is applicable to unseen drug combinations or cell lines.Case study indicates that MHCLSyn is capable of detecting potential synergistic drug combinations.

关 键 词：synergistic drug combinations cell lines multi-way relations multi-view hypergraph contrastive learning 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] O211.61[自动化与计算机技术—控制科学与工程]