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

Abstract

Accurate prediction of synergistic effects of drug combinations can reduce the experimental costs for drug development and facilitate the discovery of novel efficacious combination therapies for clinical studies. The drug combinations with high synergy scores are regarded as synergistic ones, while those with moderate or low synergy scores are additive or antagonistic ones. The existing methods usually exploit the synergy data from the aspect of synergistic drug combinations, paying little attention to the additive or antagonistic ones. Also, they usually do not leverage the common patterns of drug combinations across different cell lines. In this paper, we propose a multi-channel graph autoencoder (MGAE)-based method for predicting the synergistic effects of drug combinations (DC), and shortly denote it as MGAE-DC. A MGAE model is built to learn the drug embeddings by considering not only synergistic combinations but also additive and antagonistic ones as three input channels. The later two channels guide the model to explicitly characterize the features of non-synergistic combinations through an encoder-decoder learning process, and thus the drug embeddings become more discriminative between synergistic and non-synergistic combinations. In addition, an attention mechanism is incorporated to fuse each cell-line’s drug embeddings across various cell lines, and a common drug embedding is extracted to capture the invariant patterns by developing a set of cell-line shared decoders. The generalization performance of our model is further improved with the invariant patterns. With the cell-line specific and common drug embeddings, our method is extended to predict the synergy scores of drug combinations by a neural network module. Experiments on four benchmark datasets demonstrate that MGAE-DC consistently outperforms the state-of-the-art methods. In-depth literature survey is conducted to find that many drug combinations predicted by MGAE-DC are supported by previous experimental studies. The source code and data are available at https://github.com/yushenshashen/MGAE-DC.

MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction

“…To evaluate the effectiveness of our model, it was compared with several existing methods on a benchmark dataset. These included methods based on machine learning for predicting drug combination synergy, such as Extreme Gradient Boosting(XGBoost), Random Forest(RF), Gradient Boosting Machines(GBM), Adaboost, Multilayer Perceptron(MLP), Support Vector Machines(SVM) and those based on deep learning, like DeepSynergy [ 23 ], TranSynergy [ 37 ], MGAE-DC [ 38 ], SDCNet [ 39 ], PRODeepSyn [ 40 ], DFFNDDS [ 41 ] and Deep Tensor Factorization(DTF) [ 42 ]. To delineate the distinctions between MFSynDCP and other deep learning-based approaches, we make the following summary for each deep learning model: DeepSynergy: DeepSynergy is a deep learning model that utilizes the chemical properties of two drugs and the gene expression of a cell line to forecast synergy scores.…”

A Review on Graph Neural Networks for Predicting Synergistic Drug Combinations

“…MGAE-DC [9] is a GAE-based model that has shown lower error rates in regression than PRODeepSyn, HypergraphSynergy, and DeepDDS. However, in classification mode, its results are comparable to those of the PRODeepSyn model.…”

“…Additionally, a GCN is utilized to extract drug features from drug structures.GAE -based methodsGAE acts as a transformative tool in the two investigated regression models. In MGAE-DC model[9], GAE encodes drug combinations, learning drug embeddings. In Zagidullin et al[92] GAE transforms molecular structures into fingerprints.…”

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.…”