 Research
 Open access
 Published:
Dual graph convolutional neural network for predicting chemical networks
BMC Bioinformatics volume 21, Article number: 94 (2020)
Abstract
Background
Predicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery. The recent rapid growth of the amount of available data has enabled applications of computational approaches such as statistical modeling and machine learning method. Both a set of chemical interactions and chemical compound structures are represented as graphs, and various graphbased approaches including graph convolutional neural networks have been successfully applied to chemical network prediction. However, there was no efficient method that can consider the two different types of graphs in an endtoend manner.
Results
We give a new formulation of the chemical network prediction problem as a link prediction problem in a graph of graphs (GoG) which can represent the hierarchical structure consisting of compound graphs and an intercompound graph. We propose a new graph convolutional neural network architecture called dual graph convolutional network that learns compound representations from both the compound graphs and the intercompound network in an endtoend manner.
Conclusions
Experiments using four chemical networks with different sparsity levels and degree distributions shows that our dual graph convolution approach achieves high prediction performance in relatively dense networks, while the performance becomes inferior on extremelysparse networks.
Background
Predicting chemical networks, consisting of a set of interactions among chemical compounds, is one of the fundamental tasks in bioinformatics and chemoinformatics, as well as predicting chemical properties of each compound. Largescale analysis of chemical networks is useful for metabolic engineering [1–5] and various applications in drug discovery [6–12]. The rapid growth of the amount of available data including chemical structures and networks has enabled applications of datadriven approaches such as statistical modeling and machine learning methods [13]. Chemical compounds and chemical networks are often modeled as graphs which are general and powerful data representations of complex realworld phenomena. In a molecular compound graph, the nodes correspond to atoms and the edges correspond to chemical bonds among them. A chemical network is also described as a graph over compounds, where the nodes correspond to compounds and the edges correspond to chemical interactions between them.
Molecular fingerprinting [14] is a widely used way for molecular graph representation, where each compound is represented as a fixeddimensional feature vector. Each element of a molecular fingerprint corresponds to a substructure (e.g., benzene ring) and a chemical property (e.g., aromatic). Examples include PubChem fingerprint [15], Extendedconnectivity fingerprint [16], EState fingerprint [17], and MACCS fingerprint [18]. They have been used for predicting various chemical properties, but the performance depends heavily on the choice of fingerprints. Statistical machine learning methods such as kernel methods have also been successfully applied to chemical property prediction [19–21]. In addition, statistical machine learning methods have been applied to predicting chemical networks such as metabolic reactions [22–27], drugdrug interactions [28–31] and beneficial drug combinations [32, 33] by taking a pair of compounds as an input to a classifier.
Most of the above mentioned studies are based on offtheshelf feature representation of chemical compounds such as the molecular finger printings and tailored similarity functions such as kernel functions. More recently, driven by the significant advances of deep neural networks, researchers are moving to automatic extraction of flexible and expressive compound features from data, which succeed in improving the predictive performance [34]. Typical studies consider chemical property prediction formulated as classification or regression problems based on representation learning of compounds such as graph convolutional neural networks [35–39]. Some studies predict chemical networks by taking compound pairs as inputs [40]. Although not necessarily being specific to chemical network prediction, representation learning from chemical networks is mainly based on network embedding methods [41–43].
Most of the previously mentioned studies represent both chemical compounds and their interaction networks as graph structured data. Despite the wide ranging and rapidly spreading applications of deep learning in the chemical domain, chemical compound graphs and their interaction networks have been studied rather independently. Such differentlevel structures in a chemical network are unified as a hierarchicallystructured graph, namely, a graph of graphs (GoG) (Fig. 1). This hierarchically structured graph has two types of graph structures: the internal graph structure inside a single compound and the external graph structure among a set of compounds.
In this paper we develop an effective modeling method for the GoG which has a more general and complex graph structure than a single graph, and to consider the link prediction task on a GoG. We extends the existing graph convolutional neural network to GoGs by introducing a new architecture called dual graph convolutional neural network, which allows us to (i) seamlessly handle both internal and external graph structures in an endtoend manner using backpropagation [44] and (ii) efficiently learn lowdimensional representations of the GoG nodes. We conduct experiments of the link prediction task using four chemical network datasets, that are, drugdrug interaction network, drug indication network, drug function network, and metabolic reaction network. They have different levels of sparsity and different tail weights of degree distributions, and we use them for evaluating applicability of the proposed approach.
Method
We formulate the chemical network prediction problem as a link prediction problem in a graph of graphs (GoG). Our solution which we call dual graph convolution is an extension of the graph convolutional neural networks that enables us endtoend modeling of chemical networks using two kinds of graph convolution layers: internal graph convolution layers and external graph convolution layers.
Problem formulation
Throughout the paper, we denote vectors by bold lowercase letters (e.g., \(\mathbf {v} \in \mathbb {R}^{d}\)), matrices by bold uppercase letters (e.g., \(\mathbf {M} \in \mathbb {R}^{m \times n}\)), and scalars and discrete symbols (such as graphs and nodes) by nonbold letters (e.g., \(\mathcal G\) and n).
A GoG is a hierarchically structured graph \(\mathcal G = (\mathcal V, \mathcal A)\), where \(\mathcal V\) is the set of nodes, \(\mathcal A\) is the adjacency list. Each node in the GoG is also a graph, which we denote by \(G = (V, A) \in \mathcal V\), where V is the set of nodes, and A is the adjacency list. We refer to \(\mathcal G\) as an external graph and G as an internal graph. Generally, a GoG can have more than two levels. In this paper, we only consider two levels for simplicity, and refer to them by internal graph and external graph; however, our fundamental idea itself is easily generalized to GoGs with more levels. A chemical network is represented as a GoG \(\mathcal G\), whose nodes \(\mathcal V\) are the set of compounds, and whose edges referred to by its adjacency list \(\mathcal A\) are the set of binary relations (e.g., interact or not) among the compounds. For each compound \(G = (V, A) \in \mathcal V\), V is the set of the atoms included in the compound, and A indicates the set of chemical bonds among the atoms.
Given a GoG, our goal is to obtain a feature representation of each internal graph \(G \in \mathcal V\) and to predict the probability of the existence of a (hidden) link between arbitrary two internal graphs \(G_{i}, G_{j} \in \mathcal V\).
Proposed method: dual graph convolutional neural network
We propose the dual graph convolutional neural network for a GoG that consists of three components (Fig. 2): the internal graph convolution layer (“Internal graph convolution” section), the external graph convolution layer (“External graph convolution” section), and the link prediction layer (“Endtoend training of the link prediction function”).
Internal graph convolution
The internal convolution layer takes a chemical compound represented as an internal graph G=(V,A) as its input, and outputs a fixeddimensional vector representation for the compound. At the bottom of the internal convolution layer, the lowdimensional realvalued vector representation \(\mathbf v_{k} \in \mathbb R^{d}\) for the kth atom v_{k}∈V is randomly initialized, where d is the dimension of the vector. Each v_{k} is initialized differently depending on the types of atoms (e.g., hydrogen or oxygen), and trained using backpropagation as well as the subsequent external convolution and link prediction layers in an endtoend manner (“Drug indication network” section).
Given the initialized atom feature v_{k} for each atome v_{k}, starting from \(\mathbf v_{k}^{(0)} = \mathbf v_{k}\), we update \({\mathbf v}_{k}^{(t)}\) to \({\mathbf v}_{k}^{(t+1)}\) by the internal convolution operation:
where f_{G} is the nonlinear activation function such as ReLU. A_{k} is the list of the adjacency atoms of v_{k}, and \(\mathbf W \in \mathbb R^{d \times d}\) and \(\mathbf M \in \mathbb R^{d \times d}\) are the weight matrices to be learned. As with the graph convolution of Duvenaud et al. [35], each atom gradually incorporate global information of the compound graph into its representation by iterating the internal convolution step using the representations of its adjacent atoms. We make T iterations to obtain \({\mathbf v}_{k}^{(1)}, {\mathbf v}_{k}^{(2)}, \dots, {\mathbf v}_{k}^{(T)}\).
Finally, summing all of the atom features over all of the internal convolution steps to obtain the compound representation as
where σ_{G} is a nonlinear function such as the softmax function. We denote by \(\mathbf g_{i}^{(T)}\) the representation of compound graph \(G_{i} \in \mathcal V\), which will be the initial feature vector in the external graph convolution introduced in “External graph convolution” section.
We have freedom of choices for the nonlinear activation functions and parameter initialization. In the experiments, we use the ReLU function as activation function f_{G}. We use different W and M for different degrees (A_{k} and A_{m}) and convolutional steps. We ignored the chemical bond types mainly for computational efficiency; the data size is increased by encoding the bond information as adjacency matrices. This is compensated to some extent by introducing the different parameter matrices for different node degrees by following Duvenaud et al. ([35]). The representation v_{k} of atom k is randomly initialized using a Gaussian distribution depending on the atom type, the valence, the number of hydrogen, the number of degrees, and the aromatic sign as with the neural finger print [35]. We use the softmax function as σ_{G} in Eq. (2).
External graph convolution
The set of representations for all the compound graphs \(\{ \mathbf g_{i}^{(T)} \}_{G_{i} \in {\mathcal V}}\) are further updated with the external convolution to incorporate structural information of the external chemical network. Starting from ℓ=0, we make L updates using the external convolution operation given as
where \(f_{\mathcal G} \) is a nonlinear activation function, \(\mathcal A_{i}\) is the adjacency list of compound G_{i} in the external chemical network, and \(\mathbf U \in \mathbb R^{d \times d}\) and \(\mathbf V \in \mathbb R^{d \times d}\) are the weight matrices to be learned. We obtain the final chemical graph representation \({\mathbf h}_{i}^{(T+L)}\) considering all of the L external convolution steps as
where \(\sigma _{\mathcal G}\) is a nonlinear activation function; we use the softmax function in our experiments. Note that dual convolution does not aim to obtain a single representation of the external chemical network, but to obtain the representation of each compound considering both the internal and external graph structures, which will be used in the following link prediction layer.
We use the softmax function as \(f_{\mathcal G}\), and use different U and V for different convolutional steps. we do not distinguish different degrees because the interaction networks have much larger degrees than molecular graphs.
Endtoend training of the link prediction function
The link between two compounds G_{i} and G_{j} is predicted using their final representations \(\mathbf h_{i}^{(T+L)}\) and \(\mathbf h_{j}^{(T+L)}\). A multilayer neural network p outputs a twodimensional vector \(\mathbf y \in \mathbb R^{2}\):
and the softmax function gives the final link probability:
where t∈{0,1} is the binary label (i.e., link or nolink).
We use the twolayer neural network as the link prediction network (5) whose input is given as
where ⊕ is the concatenation of two vectors and ⊙ is the Hadamard product (i.e., elementwise product). Note that the symmetry of p with respect to its two inputs is ensured because the above construction is symmetric with respect to \(\mathbf h_{i}^{(T+L)}\) and \(\mathbf h_{j}^{(T+L)}\). We use ReLU for all of the nonlinear activation functions.
Given a set of all compound graphs and some observed links among them as the training dataset, we minimize the crossentropy loss function:
with respect to the model parameters Θ including the set of all weight matrices in the dual graph convolutional network and the atom features (that are initialized randomly). N is the total number of internal graph pairs in the training dataset, and t_{i} is the ith label (link or nolink).
Result
We evaluate the proposed dual graph convolution that combines the structural information of both internal and external graphs in a GoG. We compare the link prediction accuracy of the proposed method and several baselines using four chemical networks. The experimental results show the proposed method works well for moderately dense chemical networks with heavytailed degree distributions. In an extremely sparse and lighttailed network, intercompound links are almost useless, and the domain specific features (i.e., Morgan indices) perform the best. The internal convolution also suffers from the lack of intercompound links used as the training data.
Datasets
We prepare four different chemical GoGs with different levels of sparsity and different weights of the tails of the degree distributions (Figs. 3, 4, 5, and 6). Among the four chemical networks we describe below, the first two have heavytailed degree distributions, while the others have relatively lighttailed. One of our main interests is to obtain insights about the conditions of chemical networks in which our proposed neural network architecture is effective.
Drug–drug interaction network
The first dataset is a drug–drug interaction network that is a network of drug compounds where two compounds are connected with a link if they are known to interact, interfere, or cause adverse reactions when taken together.
We used 1,993 approved drugs that have at most 64 atoms in DrugBank database (blackhttps:// www.drugbank.ca/releases/latest), version 5.0.9 (as of October 2, 2017) [45]). Out of all possible \(\binom {1993}{2}=1,985,028\) compound pairs, 186,555 have edges; the link density is 0.0940 which means it is a relatively dense network.
We have only positive links in this dataset; this situation is sometimes dealt with positiveandunlabeled learning [46]; however, we just regard sampled nolinks as the negative links for simplicity [47]. We randomly choose n positive links and n nolinks (i.e., negative links) as the training dataset. We vary n from 1k to 10k to investigate the importance of incorporating the information of the external graph by the external convolution. As the test dataset, we randomly extract positive and negative links from the same data distribution as the original network to preserve the data imbalance, which results in 9,398 positive links and 90,601 negative links.
Drug indication network
The drug indication dataset is a network of drug compounds where two compounds are linked if they have similar indications. Our dataset is extracted from SIDER2, version 4.1 (as of October 21, 2015, http://git.dhimmel.com/SIDER2/), which includes 938 drugs that have fewer than 64 atoms. Out of all possible \(\binom {938}{2}=439{,}453\) compound pairs, we define 48,679 positive links whose indication values are positive. As well as the drugdrug interaction network, we sample nolinks as the negative links. We use 2,215 positive links and 17,785 negative links as the test set.
Drug–function network
The Drug function network dataset is a network of drug compounds where two compounds are linked if they share a same target protein. From the original dataset [48] which uses the DrugBank database, version 2.5 (as of January 29, 2009), we used 3,918 compounds that have fewer than 64 atoms. Out of all possible \(\binom {3918}{2}=7,673,403\) compound pairs, 35,562 have edges; the link density is 0.0046 which means it is a sparse network.
As well as the drugdrug interaction dataset, this network also has only positive links; therefore, we sample nolinks as the negative links. We have 1,390 positive links and 298,609 negative links in the test set.
Metabolite reaction network
The last dataset is the metabolic reaction network dataset that is a network of metabolite compounds where two compounds are linked if they are the substrateproduct pair in an enzymatic reaction on metabolic pathways [26]. Enzymatic reactions and the associated chemical compounds were obtained from the KEGG LIGAND database, Release 62.0 [49]. In this study we collected 5,920 compounds that have fewer than 64 atoms. Out of all possible \(\binom {5920}{2}=17,520,240\) compound pairs, only 5,041 have edges; the link density is 0.0003 which means it is an extremely sparse network. These edges are regarded as positive links, and the other compoundcompound pairs are regarded as negative links.
Different from the other datasets, this network has both 5,041 positive links and 220,096 negative links; the test set consists of 223 positive links 9,777 negative links.
Specific implementation of the proposed model
We implement the proposed dual graph convolutional network using Chainer [50] and use ADAM [51] as the optimizer. The learning rate is set to 0.001. We use heldout development datasets to choose d, the number of dimension of the internal graph representations, from {32,62,128}, and the numbers of convolution steps T and L from {1,3,5}. Similarly, the batch size is selected from {64,128,256}. Generally, especially in dense external networks, the number of external convolution seems more important than that of the internal convolution. We also set the dropout rate 0.2 in Eq. (1). The sizes of the two layers in the link prediction function are set to 128 and 64, respectively.
Baseline methods
We compare the dual graph convolutional network with several baselines, namely, (i) a model using only internal graph convolution, (ii) models based only on external graph structures, (iii) a model based on hashed Morgan fingerprints instead of the internal graph convolution, and (iv) several similarity indices for link prediction.
Internal graph convolution
Internal graph convolution obtains 64dimensional representations of molecular graphs. We do not use the intercompound network, and we create a feature vector for each molecule by the internal convolution and directly use it as an input to the link prediction network. We use the same convolution formula as that by Duvenaud et al. [35].
External graph embedding
External graph embedding is a standard approach to link prediction using only the intercompound network (i.e., the external graph). We test DeepWalk [41] that is one of the wellknown embedding methods, and also test the general relational embedding model proposed by Yan et al. [52] where the latent representation for each molecule is initialized to a 64dimensional random vector. The link prediction network (5) is applied to a pair of molecules.
Hashed morgan fingerprints
We use the hashed Morgan fingerprints, which is wellknown offtheshelf chemical features based on chemical substructures. We use 2048dimensional Morgan fingerprints as a feature vector of a molecule. The link prediction network (5) is applied to a pair of molecules.
Similarity indices
A similarity index gives the similarity of arbitrary two nodes in a graph. Typical similarity indices include common neighbors index (CN), Jaccard’s coefficient index (Jaccard), and the Katz index (Katz). Table 1 summarizes their definitions. Despite their simplicity, they are quite powerful for biological network prediction [53]. Links are predicted in descending order of their similarity scores.
Results
All the datasets we use have imbalance nature in terms of the number of positive and negative links; therefore we measure the predictive performance of each method using (i) ROCAUC which is not affected by the label imbalance and (ii) PRAUC which can suitably evaluate the performance on imbalanced datasets.
Figures 7, 8, 9, and 10 show the comparison of the proposed method and the four baselines in terms of ROCAUC and PRAUC with different training set sizes. In Fig. 7 and Fig. 8, the dual graph convolution network achieves consistently better ROCAUC and PRAUC scores over the baselines in the drugdrug interaction network and the drug indication network. This is probably due to the high density and the heavytailed degree distribution of its external graph (i.e., intercompound graph). In such networks, the external links are likely to efficiently connect many nodes with short paths, and therefore, the dual convolution successfully extracts structural features in the external graph.
Figure 9 shows the result for the drug function network. The advantage of the dual convolution is rather limited in the relatively sparse lighttailed network, because the efficiency of external node connections is lower than the previous networks. Interestingly the performance of DeepWalk and the similarity indices, especially, the Katz index, improves as the size of the training set increases; this implies that DeepWalk and the Katz index successfully extracts structural features from longer paths. Given that DeepWalk the similarity indices do not consider the internal graph structure at all, information of the intercompound network seems more crucial than the compound graphs in the drugfunction network.
In contrast to the other networks, the metabolite network is an extremely sparse that has very few intercompound links and a very lighttailed degree distribution. The intercompound links are almost useless in this network, and therefore the relational embedding method, DeepWalk and the similarity indices that solely depend on intercompound links perform poorly (Fig. 10). Especially, the performance of DeepWalk and the Katz index significantly degrades in terms of both ROCAUC and PRAUC, because both are based on paths on a graph, and they cannot “walk" over the intercompound links in such a sparse network. Similarly, the proposed method cannot even benefit from the external convolution, and it suffers from the sparsity of the network. The lack of the external links as the training dataset is also a severe limitation for extracting features from the internal graphs. In such a sparse data domain, traditional offtheshelf features such as Morgan indices are still reliable choices.
In summary, our experimental results suggest that the dual convolution architecture is effective for relatively dense networks, especially when both the internal and external structures must be considered in an integrated manner. Among the networks, the links of the drugdrug interaction network represent direct chemical interactions between two compounds. In such networks, nontrivial combination of different chemical substructures of both ends of a link contributes to the interaction.
Discussion
We discuss the computational efficiency and extendability of the proposed model.
We compare the complexity and scalability of our dual convolution model and the existing graph neural network using only the internal graph convolution. Table 2 shows the comparison of complexity in terms of time and space required for one update of minibatch backpropagation training. In terms of time complexity, while the internal graph convolution requires the linear complexity with respect to the number of nonzero elements A in the adjacency matrix of internal graph, our method suffers from the (linear) complexity depending on the numbers of nodes involved in the external graph convolution and the complexity of internal graph convolutions. In terms of space complexity, in addition to storing the external embeddings, we need to store the internal graph embeddings which are associated with each external node. Taking the overlapping of different nodes into account, both complexity can be less than the worst case BD^{L}. However, this still leads to computational problems in terms of both time and space complexity. This is a limitation of the proposed method especially when we consider deeper convolutional network architectures, which is an important problem to be addressed in future work.
We finally discuss the extendability of the proposed dual graph convolution model. What we proposed in this paper is a general graph neural network architecture for GoGs, and our proposed dual graph convolution is based on one of the simplest convolution operators [35]. Recent advances in graph neural networks have introduced various effective techniques such as graph attention [54], message passing [38], and neighbor sampling [55]. Most of these new techniques are independent of our proposed architecture and can be integrated into our architecture.
In this paper, we focused only on the link prediction problem on an intercompound network, and we particularly designed the output layer for the specific problem. However, other tasks such as compound classification or clustering can also be addressed by replacing the final layer specialized for each specific task, which will be an interesting future work.
Conclusion
We proposed a new formulation of the chemical network prediction problem as a link prediction problem in a GoG which can represent the hierarchical structure consisting of compound graphs and an intercompound graph. We proposed a new graph convolutional neural network architecture called dual graph convolutional network that learns compound representations from both the compound graphs and the intercompound network in an endtoend manner. We demonstrated the effectiveness of the proposed method for predicting interactions among molecules by using four chemical GoGs. Our dual convolution approach achieved high prediction performance even though the features were lowerdimensional compared to the offtheshelf features in relatively dense networks, while the performance becomes inferior on extremelysparse external networks because of the difficulty of exploiting the information about the external networks.
Availability of data and material
The datasets during the current study are available publicly and the source reference are given in main manuscript. The datasets during the current study are available from the corresponding author on reasonable request.
Abbreviations
 CN:

Common neighbors index
 GoG:

Graph of graphs
 Jaccard:

Jaccard’s index
 Katz:

Katz index
References
Greene N, Judson PN, Langowski JJ, Marchant CA. Knowledgebased expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR. SAR QSAR Environ Res. 1999; 10:299–314.
Moriya Y, Shigemizu D, Hattori M, Tokimatsu T, Kotera M, Goto S, Kanehisa M. PathPred: an enzymecatalyzed metabolic pathway prediction server. Nucleic Acids Res. 2010; 38:138–43.
Hatzimanikatis V, Li C, Ionita J, Henry C, Jankowski M, Broadbelt L. Exploring the diversity of complex metabolic networks. Bioinformatics. 2005; 21:1603–9.
Notebaart R, Szappanos B, Kintses B, Pal F, Gyorkei A, Bogos B, Lazar V, Spohn R, Csorgo B, A W, Ruppin E, Pal C, Papp B. Networklevel architecture and the evolutionary potential of underground metabolism. Proc Natl Acad Sci USA. 2014; 111:11762–7.
Darvas F. Predicting metabolic pathways by logic programming. J Mol Graphics. 1988; 6:80–6.
Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M. Prediction of drug–target interaction networks from the integration of chemical and genomic spaces. Bioinformatics. 2008; 24(13):232–40.
Cheng F, Liu C, Jiang J, Lu W, Li W, Liu G, Zhou W, Huang J, Tang Y. Prediction of drugtarget interactions and drug repositioning via networkbased inference. PLoS Comput Biol. 2012; 8(5):1002503.
Mei JP, Kwoh CK, Yang P, Li XL, Zheng J. Drug–target interaction prediction by learning from local information and neighbors. Bioinformatics. 2012; 29(2):238–45.
Bleakley K, Yamanishi Y. Supervised prediction of drug–target interactions using bipartite local models. Bioinformatics. 2009; 25(18):2397–403.
Lounkine E, Keiser MJ, Whitebread S, Mikhailov D, Hamon J, Jenkins JL, Lavan P, Weber E, Doak AK, Côté S, et al. Largescale prediction and testing of drug activity on sideeffect targets. Nature. 2012; 486(7403):361–7.
MedinaFranco JL, Giulianotti MA, Welmaker GS, Houghten RA. Shifting from the single to the multitarget paradigm in drug discovery. Drug Discovery Today. 2013; 18(9):495–501.
Wang Y, Zeng J. Predicting drugtarget interactions using restricted boltzmann machines. Bioinformatics. 2013; 29(13):126–34.
Lo YC, Rensi SE, Torng W, Altman RB. Machine learning in chemoinformatics and drug discovery. Drug Discovery Today. 2018; 23(8):1538–46.
Morgan HL. The generation of a unique machine description for chemical structuresa technique developed at chemical abstracts service. J Chem Document. 1965; 5(2):107–13.
Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. Pubchem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009; 37:623–33.
Rogers D, Hahn M. Extendedconnectivity fingerprints. J Chem Inf Model. 2010; 50(5):742–54.
Hall LH, Kier LB. The estate as the basis for molecular structure space definition and structure similarity. J Chem Inf Comput Sci. 1995; 35:1039–45.
Durant JL, Leland BA, Henry DR, Nourse JG. Reoptimization of mdl keys for use in drug discovery. J Chem Inf Comput Sci. 2002; 42:1273–80.
Schölkopf B, Tsuda K, Vert JP. Kernel Methods in Computational Biology. Cambridge, MA: MIT Press; 2004.
Ralaivola L, Swamidass SJ, Saigo H, Baldi P. Graph kernels for chemical informatics. Neural Networks. 2005; 18(8):1093–110.
Mahé P, Ralaivola L, Stoven V, Vert JP. The pharmacophore kernel for virtual screening with support vector machines. J Chem Inf Model. 2006; 46(5):2003–14.
Cheng F, Zhao Z. Machine learningbased prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J Am Med Informa Assoc. 2014; 21(e2):278–86.
Hameed PN, Verspoor K, Kusljic S, Halgamuge S. Positiveunlabeled learning for inferring drug interactions based on heterogeneous attributes. BMC Bioinformatics. 2017; 18(1):140.
Nakamura M, Hachiya T, Saito Y, Sato K, Sakakibara Y. An efficient algorithm for de novo predictions of biochemical pathways between chemical compounds. BMC Bioinformatics. 2012; 13:8.
Kotera M, Tabei Y, Yamanishi Y, Tokimatsu T, Goto S. Supervised de novo reconstruction of metabolic pathways from metabolomescale compound sets. Bioinformatics. 2013; 29:135–44.
Kotera M, Tabei Y, Yamanishi Y, Muto A, Moriya Y, Tokimatsu T, Goto S. Metabolomescale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach. Bioinformatics. 2014; 30(12):165–74.
Yamanishi Y, Tabei Y, Kotera M. Metabolomescale de novo pathway reconstruction using regioisomersensitive graph alignments. Bioinformatics. 2015; 31:161–70.
Gottlieb A, Stein GY, Oron Y, Ruppin E, Sharan R. Indi: A computational framework for inferring drug interactions and their associated recommendations. Mol Syst Biol. 2012; 8(1).
Vilar S, Harpaz R, Uriarte E, Santana L, Rabadan R, Friedman C. Drug–drug interaction through molecular structure similarity analysis. J Am Med Inform Assoc. 2012; 2012:1066—1074.
Vilar S, Uriarte E, Santana L, Tatonetti NP, Friedman C. Detection of drug–drug interactions by modeling interaction profile fingerprints. PLoS One. 2013; 8(3):58321.
Vilar S, Uriarte E, Santana L, Lorberbaum T, Hripcsak G, Friedman C, Tatonetti NP. Similaritybased modeling in largescale prediction of drugdrug interactions. Nat Protoc. 2014; 9(9):2147–63.
Zhao XM, Iskar M, Zeller G, Kuhn M, Van Noort V, Bork P. Prediction of drug combinations by integrating molecular and pharmacological data. PLoS Comput Biol. 2011; 7(12):1002323.
Iwata H, Sawada R, Mizutani S, Kotera M, Yamanishi Y. Largescale prediction of beneficial drug combinations using drug efficacy and target profiles. J Chem Inf Model. 2015; 55(12):2705–16.
Mayr A, Klambauer G, Unterthiner T, Hochreiter S. DeepTox: toxicity prediction using deep learning. Front Environ Sci. 2016; 3:80.
Duvenaud DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, AspuruGuzik A, Adams RP. Convolutional networks on graphs for learning molecular fingerprints. In: Advances in Neural Information Processing Systems (NIPS): 2015.
Kearnes S, McCloskey K, Berndl M, Pande V, Riley P. Molecular graph convolutions: moving beyond fingerprints. J Computaided Mole Design. 2016; 30(8):595–608.
Niepert M, Ahmed M, Kutzkov K. Learning convolutional neural networks for graphs. In: Proceedings of the 33rd International Conference on Machine Learning (ICML): 2016.
Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE. Neural message passing for quantum chemistry. In: Proceedings of the 34th International Conference on Machine Learning (ICML): 2017.
Pham T, Tran T, Venkatesh S. Graph memory networks for molecular activity prediction. In: Proceedings of the 24th International Conference on Pattern Recognition (ICPR): 2018. p. 639–44.
Shen Y, Yuan K, Li Y, Tang B, Yang M, Du N, Lei K. Drug2vec: Knowledgeaware featuredriven method for drug representation learning. In: Proceedings of 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM): 2018. p. 757–800.
Perozzi B, AlRfou R, Skiena S. DeepWalk: online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD): 2014.
Grover A, Leskovec J. node2vec: Scalable feature learning for networks; 2016.
Wang T, Chen L, Zhao X. Prediction of drug combinations with a network embedding method. Combi Chem High Throughput Screening. 2018; 21(10):789–97.
Sutskever I, Vinyals O, Le QV. Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems (NIPS): 2014.
Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J. Drugbank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 2006; 34(suppl_1):668–72.
Cerulo L, Elkan C, Ceccarelli M. Learning gene regulatory networks from only positive and unlabeled data. BMC Bioinformatics. 2010; 11(1):228.
Mordelet F, Vert JP. SIRENE: supervised inference of regulatory networks. Bioinformatics. 2008; 24(16):76–82.
Takigawa I, Tsuda K, Mamitsuka H. Mining significant substructure pairs for interpreting polypharmacology in drugtarget network. PLoS ONE. 2011; 6(2):16999.
Kanehisa M, Goto S, Sato Y, Furumichi M, Tanabe M. Kegg for integration and interpretation of largescale molecular data sets. Nucleic Acids Res. 2011; 40(D1):109–14.
Tokui S, Oono K, Hido S. Chainer: a nextgeneration open source framework for deep learning. In: Proceedings of Workshop on Machine Learning Systems at NIPS 2015: 2015.
Kingma D, Ba J. Adam: A method for stochastic optimization. In: Proceedings of the Third International Conference for Learning Representations (ICLR): 2015.
Yang B, Yih Wt, He X, Gao J, Deng L. Embedding entities and relations for learning and inference in knowledge bases. In: Proceedings of the Third International Conference on Learning Representations (ICLR): 2015.
Lu Y, Guo Y, Korhonen A. Link prediction in drugtarget interactions network using similarity indices. BMC Bioinformatics. 2017; 18(1):39.
Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y. Graph attention networks. In: Proceedings of the Sixth International Conference on Learning Representations (ICLR): 2018.
Hamilton W, Ying Z, Leskovec J. Inductive representation learning on large graphs.
Acknowledgements
We thank all reviewers for their time and effort.
About this supplement
This article has been published as part of BMC Bioinformatics Volume 21 Supplement 3, 2020: Proceedings of the Joint International GIW & ABACBS2019 Conference: bioinformatics (part 2). The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume21supplement3.
Funding
This work was partially supported by JSPS KAKENHI Grant Number 15H01704. Publication costs are funded by JSPS KAKENHI Grant Number 15H01704.
Author information
Authors and Affiliations
Contributions
SH, YB, and HK contributed to the concept and design of the proposed method. SH and HA implemented the methods. SH, MT, IT, and YY designed the experiments and prepared the datasets. SH carried out the experiments. SH, MT, and HK drafted the manuscript. MT, YB, IT, and YY gave technical supports. All authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Harada, S., Akita, H., Tsubaki, M. et al. Dual graph convolutional neural network for predicting chemical networks. BMC Bioinformatics 21 (Suppl 3), 94 (2020). https://doi.org/10.1186/s1285902033780
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s1285902033780