Mgae-dc: Predicting the Synergistic Effects of Drug Combinations Through Multi-channel Graph Autoencoders
Peng Zhang, Shikui Tu
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.
Introduction
Drug combination therapy, a treatment modality that combines two or more therapeutic agents, is a widely-used paradigm for various complex diseases such as cancer [1], hypertension [2] and infectious diseases [3]. Compared with the monotherapy, the drug combination therapy has the advantages of enhancing the efficiency, overcoming the drug resistance and reducing dose-dependent toxicity [4]. However, most of the drug combinations show additive effects which is equal to the sum of single-drug administrations, while rare drug combinations show synergistic effects or antagonistic effects where they have greater or lower effects than the sum of their individual administrations [5]. The drug combinations with strong synergistic effects, or synergistic drug combinations (SDCs), are attractive, new candidate therapies for clinical studies [6].
Methods
Data collection and preprocessing
The drug combinations’ synergy data are mainly comprised of four datasets including O’Neil, ALMANAC, CLOUD and FORCINA datasets [30]. Each drug combination in the datasets is represented by a drug-drug-cell line triple, and its synergistic effect is quantified by four synergy types namely Loewe additivity (Loewe), Bliss independence (Bliss), zero interaction potency (ZIP), and highest single agent (HSA), respectively. In general, combinations who with higher synergy scores are more synergistic, and vice versa [18].
Results
To display more details of the methods’ performances, three typical cell lines including MDAMB436, ES2 and LNCAP are selected, because different methods achieve superior, median and inferior level performances in these three cell lines in terms of RMSE, respectively (Fig 2B). In cell line MDAMB436, the scatter plot of MGAE-DC prediction results and the ground truth is displayed in the first column. The straight line in red, which represents the function between the predicted synergy scores and the ground truth fitted using the least squares regression, indicating their strong linear correlation. Then the distributions of RMSE and PCC of different methods in the cell line are shown in the second and third column, respectively. Similar performances are achieved by MGAE-DC and PRODeppSyn, and they significantly outperform other methods on all drug combinations from the corresponding cell line
MGAE-DC architecture
MGAE-DC consists of an embedding module and a predictor module (Fig 1). The embedding module is implemented by a MGAE to learn low-dimensional drug embeddings. As given in Fig 1A, the synergy data of drug combinations in each cell line are represented as three graphs, i.e., synergistic graph, additive graph and antagonistic graph. The nodes are drugs, and the edges are determined according to the levels of their synergy scores, i.e., high, moderate, and low, for the three graphs respectively.
Discussion
In this paper, we propose a MGAE-based method, MGAE-DC, for predicting the synergistic effects of drug combinations. Our method considers the synergy data from the aspects of not only synergistic combinations but also additive and antagonistic ones, and integrating both unique and common features of drug combinations across different cell lines. Experiments on four benchmark datasets have demonstrated that MGAE-DC achieves consistent and robust performance and outperforms state-of-the-art methods. MGAE-DC is a valuable tool to facilitate the discovery of rational combination therapies for clinical study.
Conclusion
Among the three types of drug combinations, additive combinations are the majority but less important, synergistic and antagonistic combinations are rare but more attractive candidates for clinical study. Therefore, we exclude the additive combinations, and further evaluate the performances of different methods on the synergistic and antagonistic combinations, respectively. The fourth column shows that MGAE-DC outperforms other methods on both synergistic and antagonistic combinations, which we are most concerned about. The methods in the other two cell lines ES2 and LINCAP achieve similar results and further demonstrate the effectiveness of MGAE-DC in predicting synergy effects of drug combinations in a specific cell line.
Citation: Zhang P, Tu S (2023) MGAE-DC: Predicting the synergistic effects of drug combinations through multi-channel graph autoencoders. PLoS Comput Biol 19(3): e1010951. https://doi.org/10.1371/journal.pcbi.1010951
Editor: Nir Ben-Tal, Tel Aviv University, ISRAEL
Received: October 3, 2022; Accepted: February 14, 2023; Published: March 3, 2023
Copyright: © 2023 Zhang, Tu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The source code and data are available at https://github.com/yushenshashen/MGAE-DC.
Funding: This work is supported by the National Natural Science Foundation of China (grant No. 62172273, to ST), and Shanghai Municipal Science and Technology Major Project (2021SHZDZX0102, to ST). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
Competing interests: The authors have declared that no competing interests exist.
