Predicting cell line-specific synergistic drug combinations through a relational graph convolutional network with attention mechanism

Abstract

Identifying synergistic drug combinations (SDCs) is a great challenge due to the combinatorial complexity and the fact that SDC is cell line specific. The existing computational methods either did not consider the cell line specificity of SDC, or did not perform well by building model for each cell line independently. In this paper, we present a novel encoder-decoder network named SDCNet for predicting cell line-specific SDCs. SDCNet learns common patterns across different cell lines as well as cell line-specific features in one model for drug combinations. This is realized by considering the SDC graphs of different cell lines as a relational graph, and constructing a relational graph convolutional network (R-GCN) as the encoder to learn and fuse the deep representations of drugs for different cell lines. An attention mechanism is devised to integrate the drug features from different layers of the R-GCN according to their relative importance so that representation learning is further enhanced. The common patterns are exploited through partial parameter sharing in cell line-specific decoders, which not only reconstruct the known SDCs but also predict new ones for each cell line. Experiments on various datasets demonstrate that SDCNet is superior to state-of-the-art methods and is also robust when generalized to new cell lines that are different from the training ones. Finally, the case study again confirms the effectiveness of our method in predicting novel reliable cell line-specific SDCs.

MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders

“…To present the performance of MGAE-DC predicting the synergy scores of drug combinations, which is a regression task, we compare it with four advanced methods including DeepSynergy [ 13 ], Matchmaker [ 14 ], PRODeepSyn [ 11 ], EC-DFR [ 20 ], HypergraphSynergy [ 32 ], TranSynergy [ 33 ] and SynPred [ 34 ]. Since some existing methods treat the prediction as a classification task, we also compare the performance of MGAE-DC with these methods including DTF [ 23 ], DeepDDS [ 16 ], Jiang’s method [ 29 ], SynPathy [ 35 ] and SDCNet [ 17 ]. For the classification task, the synergistic drug combinations are labeled as positive samples while the other combinations are treated as negative samples.…”

“…In this model, the drug chemical structures were treated as graphs, and the drug features were learned by a graph convolutional network (GCN) which encodes molecular topology information efficiently. Considering cell lines as different relations, the synergy data of drug combinations were modeled as a relational GCN (R-GCN) by Zhang et al’s SDCNet [ 17 ], where nodes are drugs, and edges are SDCs. Cell line-specific decoders were adopted to reconstruct the known SDCs, and predict new ones for each cell line, with the help of learned invariant features of drug combinations among the cell lines.…”

Graph neural network and multi-data heterogeneous networks for microbe-disease prediction

“…The biggest advantage of GCNN is its introduction of an optimized convolution parameter that extracts graph structure data features. This function is realized through a Laplace matrix in GCNN ( Zhang et al, 2022 ).…”

SDDSynergy: Learning Important Molecular Substructures for Explainable Anticancer Drug Synergy Prediction

“…They are divided into two categories: , relational network-based methods and molecular structure-based methods. To predict drug synergy, relational network-based methods use heterogeneous knowledge graphs to represent relationships between different entities. − Zhang and Tu , predicted drug synergy based on the drug and cell line embeddings of the synergistic combinations data through the graph embedding-based methods. Yue et al investigated drug–target heterogeneous network meta-pathways to explore molecular mechanisms of drug actions and forecast both the adverse and synergistic effects of drug combinations.…”

“…Yue et al investigated drug–target heterogeneous network meta-pathways to explore molecular mechanisms of drug actions and forecast both the adverse and synergistic effects of drug combinations. Zhang et al considered the synergistic drug combination graphs of different cell lines as a relational graph and constructed a relational graph convolutional network to learn and fuse the representations of drugs on different cell lines for synergy prediction. Molecular structure-based methods explore drug synergy by using information about drugs’ chemical structures. − At the same time it has been demonstrated that the pharmacological activity of drugs is primarily derived from their chemical substructures, , so that taking global structural information into account may result in erroneous predictions.…”