Synergistic drug combinations prediction by integrating pharmacological data

There is compelling evidence that synergistic drug combinations have become promising strategies for combating complex diseases,and they have evident predominance comparing to traditional one drug-one disease approaches.In this paper,we develop a computational method,namely SyFFM,that takes pharmacological data into consideration and applies field-aware factorization machines to analyze and predict potential synergistic drug combinations.Firstly,features of drug pairs are constructed based on associations between drugs and target,and enzymes,and indication areas.Then,the synergistic scores of drug combinations are obtained by implementing field-aware factorization machines on latent vector space of these features.Finally,synergistic combinations can be predicted by introducing a threshold.We applied SyFFM to predict pairwise synergistic combinations and three-drug synergistic combinations,and the performance is good in terms of cross-validation.Besides,more than 90%combinations of the top ranked predictions are proved by literature and the analysis of parameters in model shows that our method can help to investigate and explain synergistic mechanisms underlying combinatorial therapy.

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

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.