 Methodology
 Open access
 Published:
Drugtarget interaction prediction using semibipartite graph model and deep learning
BMC Bioinformatics volume 21, Article number: 248 (2020)
Abstract
Background
Identifying drugtarget interaction is a key element in drug discovery. In silico prediction of drugtarget interaction can speed up the process of identifying unknown interactions between drugs and target proteins. In recent studies, handcrafted features, similarity metrics and machine learning methods have been proposed for predicting drugtarget interactions. However, these methods cannot fully learn the underlying relations between drugs and targets. In this paper, we propose anew framework for drugtarget interaction prediction that learns latent features from drugtarget interaction network.
Results
We present a framework to utilize the network topology and identify interacting and noninteracting drugtarget pairs. We model the problem as a semibipartite graph in which we are able to use drugdrug and proteinprotein similarity in a drugprotein network. We have then used a graph labeling method for vertex ordering in our graph embedding process. Finally, we employed deep neural network to learn the complex pattern of interacting pairs from embedded graphs. We show our approach is able to learn sophisticated drugtarget topological features and outperforms other stateoftheart approaches.
Conclusions
The proposed learning model on semibipartite graph model, can integrate drugdrug and proteinprotein similarities which are semantically different than drugprotein information in a drugtarget interaction network. We show our model can determine interaction likelihood for each drugtarget pair and outperform other heuristics.
Background
Prediction of DrugTarget Interactions (DTI) is a critical part of drug discovery in pharmaceutical research. Compared to biochemical experimental methods which are laborious, time consuming and extremely expensive, computational methods are of high interest because they can efficiently identify potential DTIs or narrow down the search space for biologists and biochemists.
Most of traditional approaches for predicting DTI, either for drug discovery or repositioning (reusing already available drugs for new targets) are ligandbased approaches. These techniques predict drugtarget interactions based on the similarity between the target proteins’ ligands [1, 2]. Dockingbased methods utilize 3D structure information of a target protein. Ligand’s and docking methods then run simulations to estimate the likelihood that it will interact with a certain drug based on their binding affinity and strength [3, 4]. However, these approaches often lead to poor prediction results when a target has only a small number of known binding ligands. On the other hand, the performance of dockingbased approaches is limited to availability of 3D structures of target proteins and can be quite poor.
Machine learning methods for computational prediction of DTI have become more popular in recent years [5, 6]. In these approaches, DTI has been modeled using different techniques such as recommendation systems [7, 8], supervised classification problem [9], bipartite graph [10, 11] and networkbased approaches [12, 13].
In recent years, several approaches tried to take advantage of drug chemical structure and protein sequence by integrating them into the known drugtarget network in the form of drugdrug and protein similarities. These methods are based on guilt by association assumption where similar drugs may share similar targets and vice versa. Mostly, these approaches treated similarity information as input features and formulated the DTI prediction as a binary classification task in which presence of an interaction between drugs and targets is captured. For instance, bipartite local model (BLM) is proposed to model DTI network and a support vector machine is used for prediction task [10]. This work is further extended by Mei et al. by combining BLM with a neighborbased interactionprofile inferring (NII) technique (called BLMNII) [14]. This method is able to learn the DTI features from neighbors and predict interactions for new drug or target candidates. In another study, Xia et al. proposed NetLapRLS which is a semisupervised learning method for DTI prediction [15]. NetLapRLS applies Laplacian regularized least square and incorporates both similarity and interaction kernels into the prediction framework. Van Laarhoven et al. introduced a Gaussian interaction profile (GIP) kernelbased approach coupled with RLS for DTI prediction [16, 17]. Zheng et al. proposed a collaborative matrix factorization (MSCMF) for DTI [18]. They incorporated drug and protein similarity matrices to regulate the DTI network. In [19] and [20], random walk with restart algorithm is presented to predict new drug target interactions using known DTI as well as drugdrug and proteinprotein similarities and interactions. Networkbased Inference (NBI) models the prediction problem as a network where the drugs and targets are represented as nodes, and the interacting drugtarget pairs and similarities are represented as edges. The network diffusion technique is then applied to propagate interaction information throughout the drugtarget interaction network [21].
A large number of networkbased methods, mostly identify DTI based on specific heuristics. For example, BLM uses common neighbors as heuristic by measuring the weighted nearest neighbor. In another study the shortest path between drugs and target is proposed as a heuristic [22]. Recently, Yu et. al [11] investigated the predictive power of similarity indices such as common neighbors and Jaccard Index on predicting DTI, purely based on known DTI information. Although these heuristic make sense in drugtarget interaction, they cannot fully reveal the underlying relations between drugs and targets. Very recently, deep learning techniques have gained much attention for their promising performance to learn complex networks such as social and biological networks [23–25]. DTI network is no exception and recently some deep learning based methods are proposed to deal with limitation of handcrafted feature, and similarity metrics [26–28].
Inspired by link prediction methods for complex graphs, in this paper we propose a supervised learning heuristic for drugtarget interaction prediction that unlike traditional methods that rely on handengineered graph features, it learns the network topology by itself. First, we construct a semibipartite graph by exploiting known DTIs and drugdrug and proteinprotein similarities. Then, in preprocessing step, we provide positive samples among known interactions and likely negative samples among unknown data. We then propose a subgraph extraction algorithm to extract subgraphs for each drugtarget pair sample. Our algorithm captures the closest neighbors by considering geometric distances in drug target nodes as well as drugdrug and proteinprotein similarities. Each subgraph represents the graph topology surrounding of each drugtarget pair. To learn a meaningful model and preserve the ordering of graph vertices, an ordering mechanism is required to assign similar indices to nodes with similar structural role from different subgraphs. For this purpose, we employed a graph labeling method to measure the similarity between nodes and subgraphs. After ordering the vertices, subgraphs are encoded into embedding vectors. Finally, we use deep neural network to learn nonlinear topological features and complex patterns from the enclosing subgraphs.
Methods
DTI problem formulation
Predicting drugtarget interaction can be formulated as link prediction of a bipartite graph in which nodes represent drugs and targets in two sets and the edges denote the interactions. To capture the drugdrug and targettarget similarities, we formulate the DTI network as an undirected semibipartite graph G=<D,T,E,F,H>, where D and T are set of drug (chemical compound) and target (protein) nodes respectively, E⊂D×T is the set of edges (observed links) between D and T, i.e. E={(d_{i},t_{j})d_{i}∈D,t_{j}∈T}, F⊂D×D is the set of edges between the nodes in D, i.e. F={(d_{i},d_{j})d_{i},d_{j}∈D} and H⊂T×T is the set of edges between the nodes in T, i.e. H={(t_{i},t_{j})t_{i},t_{j}∈T}. An example of such a network is shown in Fig. 1a where drugdrug and targettarget similarities are integrated into the graph. The drugtarget interaction network can be represented by a m×n adjacency matrix Y as follows:
where y_{ij} denotes the <i,j>th element of matrix Y (1≤i≤m,1≤j≤n) and (d_{i},t_{j}) denotes drug d_{i} and target t_{j} pair. The goal here is to assign a score to each y_{ij} that ultimately help to classify it as whether they interact or not. Note that elements with y_{ij}=1 and y_{ij}=0 correspond to positive and unknown interactions, respectively. Throughout this paper, the set of protein targets that interact with drug d_{i} and drugs that interact with protein t_{j} are shown by \(T_{d_{i}} \subset T\) and \(D_{t_{i}} \subset D\), respectively. Drugdrug and proteinprotein similarities, are also represented by S^{D}∈[0,1]^{m×m} and S^{T}∈[0,1]^{n×n} matrices, respectively.
Workflow
Figure 1 presents the proposed framework in this work. After data preparation and constructing the semibipartite graph, positive samples are determined randomly from the graph. Negative samples, however, are determined by a method to be discussed in preprocessing step (subsection “Preprocessing”) which selects reliable negatives among unknowns. Then, our learning model is applied to learn drug target interaction from prepared samples. Our method consists of three steps shown in Fig. 1.

1.
Extracting enclosing subgraphs: In this step, for each (d_{i},t_{j}) pair sample, an enclosing subgraph with K vertices are created to capture the neighboring information of (d_{i},t_{j}).

2.
Encoding subgraphs: In this step, a vertex ordering is applied on each subgraph and then the new subgraphs are converted to embedding vectors.

3.
Learning phase: A deep neural network is trained to learn nonlinear graph topological features to predict unknown links.
Preprocessing
One of the challenges to train a model using DTI network is that, only a small number of interactions (positive samples) are known. Those that do not interact with each other are not known (i.e. missing edges in the network). Therefore, in most approaches (e.g. [28, 30–32]), negative samples are chosen randomly from the dataset. However, this might result in inaccurate findings and impact the classifier’s decision boundary. In fact, a study by Liu et. al. [29] showed properly choosing reliable negative samples can drastically improves the performance. This is the case in some approaches such as Bayesian Matrix Factorization [33], BLM [10] and Gaussian kernel profile [17]. In this work, similar to [29], first we identify reliable negative samples. The main idea is the drugs that are dissimilar to every known drug of a given target are not much likely to interact by the target and vice versa. First, we create a pool of negative candidate pairs of drugs and targets. This set excludes the set of known interacting pairs (i.e. corresponding y_{ij}=1). Any negative candidate interaction is defined by a triplet (d_{i},t_{j},s_{ij}) where s_{ij} is a score between drug d_{i} and target t_{j}. We compute \(s^{DT}_{ij} = \sum _{t_{k} \in T_{d_{i}}} S_{t_{j}t_{k}}^{T}\), that sums up similarity of every target that interacts with d_{i} with t_{j}. Similarly, we compute \(s^{TD}_{ji} = \sum _{d_{k} \in D_{t_{j}}} S_{d_{i}d_{k}}^{D}\), that sums up similarity of every drugs that interact with t_{j} with d_{i}. Finally, a similarity score between d_{i} and t_{j} is computed by:
The negative candidate pool is then ranked based on the similarity score computed above in decreasing order and those with the highest values of the score are considered to be the reliable negatives. Using these reliable negative samples and randomly drawn positive samples from known interactions, we will train a neural network classifier.
Extracting enclosing subgraph
For each (d_{i},t_{j}) pair chosen from the graph G=(D,T,E,F,H) where d_{i}∈D and t_{j}∈T, an enclosing subgraph \(G_{d_{i}t_{j}}\) which is also a semibipartite graph is extracted that captures the surrounding environment of (d_{i},t_{j}). Here, we only consider E edges to find neighbors of any drug are target nodes and vice versa. The challenge is how to identify a subgraph with K number of vertices for a drugtarget pair considering both DTI and similarity information which are semantically different. K is a predefined parameter also called subgraph size. The most important information are firstorder (firsthop) drugtarget interaction links from (d_{i},t_{j}). In the first step, target neighbors of d_{i}, N(d_{i})⊂T and drug neighbors of t_{j}, N(t_{j})⊂D are added into subgraph. If the number of vertices in the subgraph is less than K, we construct a pool of vertices (χ), consisting of neighbors of nodes that have been included into the subgraph but their neighbors have not been included yet and will be processed. Then, we sort the pool based on similarity of drugs with d_{i} and target proteins with t_{j} (using S^{D} and S^{T}, respectively) in decreasing order and keep adding to the subgraph from top of the pool till size of the subgraph meets K. If the number of vertices in the subgraph is more than K, first use graph labeling to impose an ordering for subgraph, and then reorder it using this order. After that, if \(G_{d_{i},t_{j}} > K\), the bottom \(G_{d_{i},t_{j}}  K\) vertices are discarded. At the end, the subgraph induces by identified vertices. This process is summarized in Algorithm 1.
Subgraph pattern encoding
Unlike some recent approaches that provide embedding features for each node of the graph [34], we provide an embedding feature only for each subgraph representing a drugtarget pair’s topological structure. To learn a meaningful model, it is necessary to find a vertex ordering for each subgraphs. For this purpose, we use graph labeling. The idea is to make vertices from different subgraphs that have similar structural role, get assigned to similar orders (rankings). A graph labeling function is a map f:V→C from vertices V to an ordered set C, conventionally called colors in literature. In our problem, f must be a onetoone function, so each vertex is mapped to a unique color.
Among graph labeling algorithms, WeisfeilerLehman (WL) algorithm [35] is wellknown because of its graph isomorphism test. WL provides vertex ordering based on topological structure of a graph. In this algorithm, initially, all vertices get the same label. Then, in an iterative fashion, each vertex gets a signature string by concatenating its own labels and their immediate neighbors’ labels. Then, signature strings are sorted lexicographically in ascending order and each vertex gets a new label based on its signature string order. For instance, let vertex x with label 2 has neighbors with labels {1,2,3} and vertex y with label 3 has neighbors with labels {2,2,4}. The signature string of x and y are {2,123} and {3,224}, respectively. Since, {2,123} is lexicographically smaller than {3,224}, x gets smaller label than y. This process is repeated until vertices get unique labels. At the end, vertices with similar structural roles get similar labels [36].
Since WL ranks vertices based on topological structure of the graph and structural role of the vertices, it is suitable for any classifier model. WL treats any vertex in the graph identically. However, in our application, we construct each subgraph for a particular drugtarget pair and therefore WL is not able to capture that information. In addition, as WL requires reading and sorting of the vertices’ signature strings, it becomes computationally expensive since the signature strings can be very long for nodes with high degrees. Fast hashingbased WL algorithms were proposed [25, 37] which map unique signature strings to unique real values. To deal with issues mentioned above, we borrowed the PalleteWL algorithm [25] in which it can take advantage of vertex ordering capability of WL while capturing the core information of each subgraph (i.e. initial drugtarget pair) using a hashing function.
In PalleteWL, initially, geometric mean distance of any node in the subgraph \(G_{d_{i}t_{j}}\) to d_{i} and t_{j} is computed. Then, distance values are mapped to colors by function f. Function f first maps the smallest real number to color 1, and then maps the second smallest real number to color 2, and so on until every real number is mapped to a color. If two or more real numbers are equal, they are mapped to the same color. Then, a refinement process is iteratively done by mixing their original colors and nearby colors in such a way that the colors’ relative ordering is preserved. This process is driven using a hash function [25]. An example of this algorithm is shown in Fig. 2. In this example, first a subgraph is extracted for (d_{i},t_{j}) pair from the semibipartite graph. Then, labels for vertices in the subgraph are assigned based on their geometric distances to d_{i} and t_{j}. Finally, by the refinement process, each vertex is assigned to a unique label.
After vertex ordering is done on subgraphs with K vertices, subgraphs are encoded to adjacency matrices with size of K×K. Each matrix includes {0,1} for (d_{i},t_{j}) indices, depending of the existence of an edge between them, and values in (0,1] range for (d_{i},d_{k}) and (t_{j},t_{k}) indices (using S^{D} and S^{T}). As the matrices are symmetric, only uppertriangle part is used (Fig. 1d) and vertically converted to \(\frac {K(K1)}{2}\) vectors.
Learning phase by neural network
After we encode the enclosing subgraphs and identify embedding vectors for positive (d_{i},t_{j}) pair samples ((d_{i},t_{j})∈E) and negatives (d_{i},t_{j}) pair samples (when (d_{i},t_{j})∉E), we feed the information into a deep neural network to learn the nonlinear topological patterns. After the training phase, interaction for any given drugtarget pair can be predicted by the trained neural network. The output of neural network would give us a probability estimate to predict the interaction between testing drugtarget pair (i.e. positive or negative  see Fig. 1e).
Datasets
We adopted a wellknown dataset for prediction and evaluation of our DTI prediction method. This dataset has been constructed by [32]. This dataset includes drugprotein interaction network (extracted from the DrugBank database Version 3.0 [38]). It also includes drug chemical structure similarity network (i.e. a pairwise chemical structure similarity network measured by the dice similarities of the Morgan fingerprints with radius 2, which were computed by RDKit [39]), and protein sequence similarity network (which was obtained based on the pairwise SmithWaterman scores [40]). DTI network consists of binary edge weights (i.e. 1 represents a known interaction, and 0 otherwise) and the drug structure similarity network and the protein sequence similarity network consist of realvalued edge weights between 0 and 1. This datasets include 708 drugs, 1,512 protein targets and 1,923 known drugtarget interactions. These datasets have widely been used by researchers [28, 41, 42].
Results
Performance evaluation metrics and protocols
We used a neural network architecture with three fullyconnected layers with 32, 32 and 16 hidden neurons, respectively. For neurons’ activation, we used Rectified Linear Unit (ReLU). A softmax layer is used as the output layer (i.e. assigns estimated probability to each class). These hyper parameters are selected empirically based on trial and error.
After training the neural network, we can predict the interaction between any testing drugtarget pair. Similar to training phase, first, we extract enclosing subgraph for testing pairs. Then, we use our encoding methodology to construct the feature embedding subgraphs and feed them to the neural network. Neural network provides a prediction score for (d_{i},t_{j}), which represents the estimated likelihood of interaction. In our paper, for all experiments, 10fold cross validation is used to estimate the performance of our method on the data. In this method, the data is divided into 10 nonoverlapping subsets. 9 out of these 10 subsets are used for training and the remaining 1 subset is used for testing. Positive samples are randomly selected from known drugtarget interactions and negative samples are selected based on the method explained in Subsection “Preprocessing”. Like other researchers in this field, we employed the Area Under Receiver Operating Characteristic (AUROC) curve and Area Under PrecisionRecall (AUPR) curve to evaluate prediction performance for all methods [43]. In general, ROC curves show the tradeoff between the true positive rate (TPR) and false positive rate (FPR), and PR curves show the tradeoff between the precision and recall using different probability thresholds.
We comprehensively compared our approach with four baseline methods in drugtarget interaction predictions reported in literature, namely BLMNII [14], CMF [18], HNM [44] and NetLapRLS [15]. First, we compared the performance of our method with others when the data is balanced (i.e. number of positive and negatives are roughly equal). The AUROC and AUPR results show our approach achieved higher performance than other methods (Fig. 3ab).
In practice, DTI network is often very sparse with only few known DTIs. To mimic this imbalanced data situation, we randomly sample negative pairs 10 times more than positive pair samples [28] (positive to negative ratio α=10%). As expected, in all methods, both AUROC and AUPR scores decreased in compared to the case that number of positives and negatives were balanced (Fig. 3cd). Although in our method AUROC and AUPR scores dropped around 4% and 10% respectively, we observed our method still outperformed other methods with significant improvement.
To further mimic the practical situation and decrease the positive to negative ratio, we chose all unknown interactions as negative samples. In this case, the positive to negative ratio α≃0.18%. The performance of this setup is shown in Fig. 3ef. We observed that in this case, our method achieved a higher performance over baseline methods as well. As stated in [17, 32], in this case that the dataset is highly unbalanced, AUPR can provide a better assesment than AUROC metric. The reason is in this scenario, there are many more negatives than positives and AUPR does not account for true negatives. Although the performance of most methods in terms of AUROC are comparable (Fig. 3e), our approach significantly achieved better performance in terms of AUPR (Fig. 3f).
Since the datasets may contain redundant DTIs (i.e. a same protein is connected to more than one similar drugs and vice versa), the performance of prediction can be inflated. To analyze the robustness of our algorithm against removal of homologous proteins or similar drugs, we performed an experiment similar to [28] and [32], in which DTIs with similar drugs (i.e. drug structural similarity) >60% or similar proteins (i.e. protein sequence similarity) >40% are removed. The removal operations reduced the number of interactions from 1,923 to 900. Similar to other experiments, 10fold cross validation is used to provide AUROC and AUPR performance (shown in Fig. 4). The results indicates our approach outperformed other prediction methods in term of both AUROC and AUPR. As expected, compared to nonremoval case, prediction performance is decreased (Fig. 3ab).
As our model lies under the category of heuristic based approaches, we further compared the performance of our model with other heuristics employed in DTI prediction by Lu et. al [11]. These heuristics used for link prediction which can be categorized into firstorder, secondorder and highorder heuristic methods, based on the most distant node necessary for computing the heuristic [32]. Namely, heuristics proposed for DTI prediction in [11] are Preferential Attachment (PA) (i.e. firstorder heuristic) [45], modified common neighbors (CN) and modified Jaccard Index (i.e. secondorder heuristic) and Katz Index (i.e higherorder heuristic). The results illustrated in Fig. 5 show our model outperforms other heuristics in terms of AUROC (as AUPR performance for all other methods were close to zero, this metric is not shown). This is expected as [24] shows, learning highorder heuristics is feasible with a small subgraph size (K) using WL algorithm.
To show the effect of similarity information in our model, we conducted an experiment based on only drugtarget (DT) interaction network (i.e bipartitegraph), DT interaction network with drugdrug structural similarities (DD), DT interaction network with protein sequence similarities (TT) and all networks. The results are shown in Fig. 6. It shows additional networks such as drug or/and protein (target) similarity matrices improve the prediction performance. We observed 14% and 18% improvement when all networks are used compared to when only DT network is used in terms of AUROC and AUPR, respectively. Also, this experiment evaluates the robustness of our approach by providing different types of networks.
As our proposed model relies on topological features, we investigated the effect of the size of subgraph representing drugtarget pair in prediction task. Figure 7 shows the overall trend that as the number of vertices in subgraphs increases, the AUROC performance also increases. However, the performance of our model for K>15 remains flat. It is also observed that AUPR score decreases for K>15. The trend shown in our work confirms a study by Zhang et. al [24] that shows the most useful information is provided by closer vertices to the link being predicted by WL algorithm. Specifically, we see a diminishing return for AUPR for large values of K due to overfitting.
To investigate how negative sampling technique affects the performance of our model, we compared the performance of our model with negative sampling technique mentioned in Subsection “Preprocessing” and random sampling of unknown interactions. The 10fold cross validation results in terms of AUROC and AUPR are provided in Figs. 8 and 9, respectively. As expected, the performance when reliable negatives are used for training is higher than randomly selected negative samples. The importance of using reliable negative samples can be even more pronounced where positive to negative ratio α is low (i.e. 10%).
We additionally tested our method on four datasets introduced in [46] (socalled Yamanishi dataset). These datasets correspond to four different target protein types, namely nuclear receptors (NR), G proteincoupled receptors (GPCR), ion channels (IC) and enzymes (E). Dataset specification is provided in Additional file 1: Table S1. Results in Additional file 1: Figure S1 show our approach achieved consistent results in Yamanishi dataset. For NR dataset, the performance is relatively lower than other categories. We surmise this happens due to lack of enough training data.
Discussion
Although our methodology is not fully endtoend learning, it eliminates the use of handcrafted features and lets neural network learns features based DTI network. An important step in our methodology is to capture the network topology surrounding drugtarget link by enclosing subgraphs. All firstorder heuristics such as common neighbors can be calculated from the 1hop enclosing subgraphs. However, researchers have shown that highorder heuristics such as Katz perform much better than first and secondorder methods [47]. This is reflected in our comparisons shown in Fig. 5. To effectively learn highorder features, one may think that a very large hop number h is always needed. However, this leads to very large enclosing subgraph which dramatically increases the computational complexities. Moreover, Zhang et al. showed that we do not necessarily need a very large h to learn highorder graph structure [24]. The authors reported that features can be learnt using even small hhop subgraphs. This can indirectly be observed in Fig. 7 which shows the performance of our model quickly ramps up when number of nodes (K which is proportional to h) in subgraph increases.
Our methodology, similar to other graph/node labeling techniques, relies on preserving two key attributes, i.e. structural role topological directionality [24, 25]. Specifically in our approach, PalleteWL algorithm (Subsection “Subgraph pattern encoding”) achieves this preservation by labeling structural differences hence providing additional information to facilitate training process.
Although our neural network approach has advantage over methods that use handcrafted features by learning from network topology information, it has some limitations. Firstly, our method trains a fullyconnected neural network on flattened upper triangular of adjacency matrices (see Fig. 1 and its explanation) Since fullyconnected neural networks only accept fixed size feature vectors as input, subgraphs with different sizes need to be truncated. Consequently, our method may not consistently learn from the full hhop neighborhood of each link and may miss some structural information which may limit our model’s performance. Secondly, due to the limitation of adjacency matrix representations, our approach cannot learn from explicit features [24].
Very recently, other type of relations such as drugdrug and proteinprotein interactions, drugdisease and drugsideeffect associations have been considered for DTI prediction by researchers [28, 32, 48]. In future, we intend to incorporate these associations within our methodology.
We acknowledge that ultimate validation of drugtarget prediction is to show how the prediction method can rediscover some FDAapproved drugs. We can certainly generate the top (highest prediction scores) of drugtarget pairs for further inspection. However, fullfledge validation requires a much more comprehensive study of the FDAapproved drugs that is beyond the scope of this work.
Conclusion
We have proposed a DTI prediction methodology using drugtarget network, drug structural similarities and protein sequence similarities. We modeled this problem as link prediction in a semibipartite graph and used deep learning as a learning tool. One advantage of our model is that, it captures more useful relational information and automatically learns topological features from DTI network. Additionally, it uses neural networks to learn complex topological features which heuristics cannot express. Through comprehensive experimentation, we have shown that our model achieves better performance compared to other methods reported in literature.
Availability of data and materials
The datasets used in this project can be found in: https://github.com/HafezEM/DTI
References
Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK. Relating protein pharmacology by ligand chemistry. Nat Biotechnol. 2007; 25(2):197–206.
Keiser MJ, Setola V, Irwin JJ, Laggner C, Abbas AI, Hufeisen SJ, Jensen NH, Kuijer MB, Matos RC, Tran TB, et al.Predicting new molecular targets for known drugs. Nature. 2009; 462(7270):175–81.
Cheng AC, Coleman RG, Smyth KT, Cao Q, Soulard P, Caffrey DR, Salzberg AC, Huang ES. Structurebased maximal affinity model predicts smallmolecule druggability. Nat Biotechnol. 2007; 25(1):71–5.
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. Autodock4 and autodocktools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16):2785–91.
Mousavian Z, MasoudiNejad A. Drug–target interaction prediction via chemogenomic space: learningbased methods. Expert Opin Drug Metab Toxicol. 2014; 10(9):1273–87.
Ding H, Takigawa I, Mamitsuka H, Zhu S. Similaritybased machine learning methods for predicting drug–target interactions: a brief review. Brief Bioinforma. 2013; 15(5):734–47.
Alaimo S, Giugno R, Pulvirenti A. Recommendation techniques for drug–target interaction prediction and drug repositioning. Data Min Tech Life Sci. 2016:441–62. https://doi.org/10.1007/9781493935727_23.
Manoochehri HE, Nourani M. Predicting drugtarget interaction using deep matrix factorization. In: 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE: 2018. p. 1–4. https://doi.org/10.1109/biocas.2018.8584817.
Wen M, Zhang Z, Niu S, Sha H, Yang R, Yun Y, Lu H. Deeplearningbased drug–target interaction prediction. J Proteome Res. 2017; 16(4):1401–9.
Bleakley K, Yamanishi Y. Supervised prediction of drug–target interactions using bipartite local models. Bioinformatics. 2009; 25(18):2397–403.
Lu Y, Guo Y, Korhonen A. Link prediction in drugtarget interactions network using similarity indices. BMC Bioinformatics. 2017; 18(1):39.
Fakhraei S, Huang B, Raschid L, Getoor L. Networkbased drugtarget interaction prediction with probabilistic soft logic. IEEE/ACM Trans Comput Biol Bioinforma (TCBB). 2014; 11(5):775–87.
Wu Z, Li W, Liu G, Tang Y. Networkbased methods for prediction of drugtarget interactions. Front Pharmacol. 2018; 9. https://doi.org/10.3389/fphar.2018.01134.
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.
Xia Z, Wu LY, Zhou X, Wong ST. Semisupervised drugprotein interaction prediction from heterogeneous biological spaces. In: BMC Syst Biol, vol. 4. BioMed Central: 2010. p. 6. https://doi.org/10.1186/175205094s2s6.
van Laarhoven T, Nabuurs SB, Marchiori E. Gaussian interaction profile kernels for predicting drug–target interaction. Bioinformatics. 2011; 27(21):3036–43.
Van Laarhoven T, Marchiori E. Predicting drugtarget interactions for new drug compounds using a weighted nearest neighbor profile. PloS ONE. 2013; 8(6):66952.
Zheng X, Ding H, Mamitsuka H, Zhu S. Collaborative matrix factorization with multiple similarities for predicting drugtarget interactions. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM: 2013. p. 1025–33. https://doi.org/10.1145/2487575.2487670.
Chen X, Liu MX, Yan GY. Drug–target interaction prediction by random walk on the heterogeneous network. Mol BioSyst. 2012; 8(7):1970–8.
Lee I, Nam H. Identification of drugtarget interaction by a random walk with restart method on an interactome network. BMC Bioinformatics. 2018; 19(8):208.
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.
BaAlawi W, Soufan O, Essack M, Kalnis P, Bajic VB. Daspfind: new efficient method to predict drug–target interactions. J Cheminformatics. 2016; 8(1):15.
Cai H, Zheng VW, Chang KCC. A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Trans Knowl Data Eng. 2018; 30(9):1616–37.
Zhang M, Chen Y. Link prediction based on graph neural networks. In: Advances in Neural Information Processing Systems: 2018. p. 5165–75.
Zhang M, Chen Y. Weisfeilerlehman neural machine for link prediction. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM: 2017. p. 575–83. https://doi.org/10.1145/3097983.3097996.
Zong N, Kim H, Ngo V, Harismendy O. Deep mining heterogeneous networks of biomedical linked data to predict novel drug–target associations. Bioinformatics. 2017; 33(15):2337–44.
Zong N, Wong RSN, Ngo V. Tripartite networkbased repurposing method using deep learning to compute similarities for drugtarget prediction. In: Computational Methods for Drug Repurposing. Springer: 2019. p. 317–328. https://doi.org/10.1007/9781493989553_19.
Wan F, Hong L, Xiao A, Jiang T, Zeng J. Neodti: Neural integration of neighbor information from a heterogeneous network for discovering new drugtarget interactions. bioRxiv. 2018:261396. https://doi.org/10.1093/bioinformatics/bty543.
Liu H, Sun J, Guan J, Zheng J, Zhou S. Improving compound–protein interaction prediction by building up highly credible negative samples. Bioinformatics. 2015; 31(12):221–9.
Li Z, Han P, You ZH, Li X, Zhang Y, Yu H, Nie R, Chen X. In silico prediction of drugtarget interaction networks based on drug chemical structure and protein sequences. Sci Rep. 2017; 7(1):11174.
Meng FR, You ZH, Chen X, Zhou Y, An JY. Prediction of drug–target interaction networks from the integration of protein sequences and drug chemical structures. Molecules. 2017; 22(7):1119.
Luo Y, Zhao X, Zhou J, Yang J, Zhang Y, Kuang W, Peng J, Chen L, Zeng J. A network integration approach for drugtarget interaction prediction and computational drug repositioning from heterogeneous information. Nat Commun. 2017; 8(1):573.
Gönen M. Predicting drug–target interactions from chemical and genomic kernels using bayesian matrix factorization. Bioinformatics. 2012; 28(18):2304–10.
Zitnik M, Agrawal M, Leskovec J. Modeling polypharmacy side effects with graph convolutional networks. Bioinformatics. 2018; 34(13):457–66.
Weisfeiler B, Lehman AA. A reduction of a graph to a canonical form and an algebra arising during this reduction. NauchnoTechnicheskaya Informatsia. 1968; 2(9):12–6.
Shervashidze N, Schweitzer P, Leeuwen E. J. v., Mehlhorn K, Borgwardt KM. Weisfeilerlehman graph kernels. J Mach Learn Res. 2011; 12(Sep):2539–61.
Kersting K, Mladenov M, Garnett R, Grohe M. Power iterated color refinement. In: TwentyEighth AAAI Conference on Artificial Intelligence: 2014.
Knox C, Law V, Jewison T, Liu P. i., Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V, et al.Drugbank 3.0: a comprehensive resource for ’omics’ research on drugs. https://doi.org/10.1093/nar/gkq1126.
Rogers D, Hahn M. Extendedconnectivity fingerprints. J Chem Inf Model. 2010; 50(5):742–54.
Smith TF, Waterman MS, et al.Identification of common molecular subsequences. J Mol Biol. 1981; 147(1):195–7.
Lin C, Ni S, Liang Y, Zeng X, Liu X. Learning to predict drug target interaction from missing not at random labels. IEEE Trans Nanobiosci. 2019. https://doi.org/10.1109/tnb.2019.2909293.
Yan XY, Zhang SW, He CR. Prediction of drugtarget interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods. Comput Biol Chem. 2019; 78:460–7.
Davis J, Goadrich M. The relationship between precisionrecall and roc curves. In: Proceedings of the 23rd International Conference on Machine Learning. ACM: 2006. p. 233–40. https://doi.org/10.1145/1143844.1143874.
Campillos M, Kuhn M, Gavin AC, Jensen LJ, Bork P. Drug target identification using sideeffect similarity. Science. 2008; 321(5886):263–6.
Barabási AL, Albert R. Emergence of scaling in random networks. Science. 1999; 286(5439):509–12.
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.
Lü L, Zhou T. Link prediction in complex networks: A survey. Phys A Stat Mech Appl. 2011; 390(6):1150–70.
Zeng X, Zhu S, Liu X, Zhou Y, Nussinov R, Cheng F. deepdr: a networkbased deep learning approach to in silico drug repositioning. Bioinformatics. 2019. https://doi.org/10.1093/bioinformatics/btz418.
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Eslami Manoochehri, H., Nourani, M. Drugtarget interaction prediction using semibipartite graph model and deep learning. BMC Bioinformatics 21 (Suppl 4), 248 (2020). https://doi.org/10.1186/s1285902035186
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DOI: https://doi.org/10.1186/s1285902035186