 Methodology
 Open access
 Published:
CNNDDI: a learningbased method for predicting drug–drug interactions using convolution neural networks
BMC Bioinformatics volume 23, Article number: 88 (2022)
Abstract
Background
Drug–drug interactions (DDIs) are the reactions between drugs. They are compartmentalized into three types: synergistic, antagonistic and no reaction. As a rapidly developing technology, predicting DDIsassociated events is getting more and more attention and application in drug development and disease diagnosis fields. In this work, we study not only whether the two drugs interact, but also specific interaction types. And we propose a learningbased method using convolution neural networks to learn feature representations and predict DDIs.
Results
In this paper, we proposed a novel algorithm using a CNN architecture, named CNNDDI, to predict drug–drug interactions. First, we extract feature interactions from drug categories, targets, pathways and enzymes as feature vectors and employ the Jaccard similarity as the measurement of drugs similarity. Then, based on the representation of features, we build a new convolution neural network as the DDIs’ predictor.
Conclusion
The experimental results indicate that drug categories is effective as a new feature type applied to CNNDDI method. And using multiple features is more informative and more effective than single feature. It can be concluded that CNNDDI has more superiority than other existing algorithms on task of predicting DDIs.
Background
Drug–drug interactions (DDIs) mean the reactions between drugs. They are compartmentalized into three types: synergistic, antagonistic and no reaction [1,2,3]. The DDIs play a significant role in drug development and disease diagnosis fields, which still consumes manpower, substance sources and time [4].
Powered by advanced machine learning technology, methods of DDIs’ prediction have been evolved from traditional methods [5,6,7], including text mining methods and statistical methods, to machine learning methods. Furthermore, more and more studies use deep learning methods in the field of bioinformatics [8,9,10,11,12,13,14,15].
The task of predicting DDIs is vitally interrelated with similarities between drugs. The fundamental hypothesis of this task is that if drug A and drug B interact each other, causing a specific biological impact, drugs have similarity to drug A (or drug B) are possible to interact with drug B (or drug A) and causes same effect [16].
Cami et al. [17] utilized a logistic regression model to solve the DDIs’ problem. On this basis, Gottlied et al. [18] exploited more different drug–drug similarities and proposed another logistic regression model. Two similaritybased models based on drug interaction profile fingerprints were proposed [16, 19] and a heterogeneous networkassisted inference framework was introduced by Cheng et al. [20]. Some other algorithms were extended on the task of DDIs’ prediction. For instance, TMFUF [12] is based on the triple matrix factorization, DDINMF [21] is based on the seminonnegative matrix factorization. Three algorithms were proposed in [22], including neighbor recommender algorithm, random walk algorithm, and the matrix perturbation algorithm. Further, they proposed a novel algorithms named ‘Manifold Regularized Matrix Factorization’. In 2019, SFLLN was proposed in [23] based on linear neighborhood regularization using four types of drug features. It is a sparse feature learning ensemble method.
DeepDDI was proposed [10] to classify the DDIs’ events from DrugBank [24]. DeepDDI calculates features’ similarity and reduces features’ dimension by principal component analysis (PCA). Lee et al. [25] concentrated on concrete types of two drugs, not simply whether they interact or not. DDIMDL [11] is a multimodal deep neural network algorithm, which combines diverse drug features that predicting 65 types of DDI events.
Convolutional neural network (CNN) is a typical artificial neural network based on supervised learning, which has good performance on computer vision filed [26]. And it develops more network structures from CNN. They have been used extensively in bioinformatics [27, 28]. Many studies apply deep learning method in the task of DDIs’ prediction, and most of them choose deep neural network (DNN). But compared with deep neural network, CNN performs better in feature learning and can alleviate the degree of overfitting effectively. Considering features selected contain noise and advantages of CNN, we decide to use CNN to solve the problem of DDIs’ prediction.
In this paper, we propose a novel algorithm based on CNN, named CNNDDI, to learn the best combination of drug features and predict DDIassociated events. CNNDDI method contains two parts. One part is a feature selection framework. We utilize drug categories as another feature, and choose the best combination form of drug features. The other part is a CNNbased DDI’s predictor. We utilize a new CNN to predict DDIassociated events based on features pairs selected from feature selection framework.
Results and discussion
Evaluation criteria
Predicting DDI’ events can be regarded as a multilabel classification problem. Therefore, the prediction results are divided into four kinds, true positive (TP), false positive (FP), true negative (TN) and false negative (FN). In addition, precision and recall criteria are common used evaluation criteria, which can evaluate the accuracy of results. Precision means in the classified positive samples, the proportion of TP samples. And recall means in all positive samples, the proportion of correct samples classified. The expressions are as follows:
Based on precision and recall, Accuracy, F1score, area under the precisionrecall curve (AUPR) and area under the ROC curve (AUC) are utilized to evaluate the performance of the algorithm.
In the study, we adopt Accuracy, F1score, microaveraged AUPR and microaveraged AUC as the evaluation metrics. Microaveraged metrics means metrics are averaged after getting the results of all classes.
Performance
To analyze the effect of different similarity algorithms on performance of CNNDDI, we utilize cosine similarity, Jaccard similarity and Gaussian similarity to calculate features’ similarities. Table 1 shows the experimental results of our method on three similarity measures. It can be seen that using different similarity measures exhibits similar properties. CNNDDI is robust to these three similarity measures, so Jaccard similarity measure is used in the experiments.
To demonstrate the superiority of drug categories and influence of different combination forms, we further test the performance of CNNDDI model with different features’ types. The experimental results are shown in Table 2. As for one feature, CNNDDI using drug categories as the feature performs best, the AUPR score using drug categories is 0.9139, which is quite higher than the second highest score produced by drug targets (the value is 0.8470). Similarly, using drug categories achieves the highest scores of other five evaluation metrics. So the drug category is effective as a new feature type applied to CNNDDI method. On the whole, using multiple features is informative and helps CNNDDI perform better than single feature. The combination of four features has the highest AUPR score (the value is 0.9251) in all combinations. Thus it can be proved that every feature improves the performance of CNNDDI to a certain extend.
Comparison experiments
We evaluate the effectiveness of our algorithm and four stateofart algorithms. The four algorithms are random forest (RF), gradient boosting decision tree (GBDT), logistic regression (LR) and Knearest neighbor (KNN). We measure feature similarities in the same manner. In the experiment, we set the decision tress number of RF to be 100 and the neighbor number of KNN to be 4.
Table 3 shows CNNDDI algorithm has better performance than other four methods in these 6 accuracy assessments. The score of ACC is 0.8871, it is better than the score of GBDT, RF, KNN and LR (0.8327, 0.7837, 0.7581 and 0.7558 respectively). And other evaluation metrics achieved by CNNDDI are 0.9251, 0.9980, 0.7496, 0.8556 and 0.7220, respectively, which are significantly higher than the sores of other methods. The LR algorithm gets the worst performance, whose scores are 0.7558, 0.8087, 0.9950, 0.3894, 0.5617 and 0.3331, respectively. Compared with GBDT, which gets the second best performance, the ACC score is 0.8871, increased by 6.53%. And the score of AUPR is 0.9251, increased by 4.79%, all of other evaluation metrics have been improved in varying degrees.
And we compare our algorithm with DDIMDL. Considering DDIMDL using different features, we retrain DDIMDL model with features selected by CNNDDI. As shown in Table 4, DDIMDL represents the original algorithm proposed by original paper [11]. DDIMDL* represents DDIMDL with features selected by CNNDDI. It can be concluded that the drug category is effective as a new feature type, and CNNDDI still performs better than DDIMDL in the case of using the same features.
Conclusions
In the work, we proposed a novel semisupervised algorithm using a CNN architecture, named CNNDDI, to predict drug–drug interactions. First, we extract feature interactions from drug categories, targets, pathways and enzymes as feature vectors. Then, based on the representation of feature, we proposed a new convolution neural network as the predictor of DDIsassociated events. The predictor consists of five convolutional layers, two fullconnected layers and a softmax layer based on CNN.
To demonstrate the performance of our method, we compare it with other startoftheart methods. The evaluation shows our method, CNNDDI, has better performance than other existing stateofart measures. Meanwhile, we discuss the contribution of combinational features and each single feature. Overall, CNNDDI has more advantages on predicting DDIs’ events. In consideration of consuming longer time, we will try to improve the efficiency of CNNDDI in the future.
Methods
We propose a novel method called CNNDDI to predict DDIassociated events. The method mainly contain two parts, combinational features selection module and CNNbased prediction module. As shown in Fig. 1, we combine four drug features and obtain a low dimensional as the CNN model inputs. Then a deep CNN model is built to calculate the probability of DDIs’ types. In this section, we will thoroughly expound the structure and principle of CNNDDI.
Data collection
DDIMDL proposed a data set that classifying DDIs’ events into 65 types, not simply focusing on whether they interact or not. The data set includes 572 drugs and 74,528 DDIsassociated events collected from DrugBank. Which is a manually collected data source that provides drugs comprehensive information and unified syntax in describing DDIs.
To extend the information of DDIMDL, we extract drugs categories from DrugBank. 572 drugs have 1622 types of categories in DDIMDL.
In our paper, cross validation is utilized to demonstrate the effectiveness of our method. We set the fold number of cross validation is 5. In our experiments, we randomly divide the data set into five subsets, choose four subsets as the train set and another one as the test set. We test on the data set five times following the above steps, and the final result is the average of multiple results.
CNNDDI algorithm
Drug–drug similarity
There are three common similarity measures, Jaccard similarity, cosine similarity and Gaussian similarity. To better measure the drug feature vectors’ similarity, we analyze the difference of measures’ results. Jaccard similarity calculates the intersection of components and the union. Gaussian similarity utilizes the Gaussian kernel function. And cosine similarity is used to calculate the cosine between two vectors in an inner product space [29].
Jaccard similarity can be calculated as follows:
where x_{i} and x_{j} are feature vectors of two drugs, X and Y are the vector sets respectively. \(\left {X \cup Y} \right\) represents the union of X and Y, \(\left {X \cap Y} \right\) represents the intersection. Further, M represents the number of elements. Subscript 11 means the elements where x_{i} and x_{j} are 1, 01 means elements where x_{i} is 0 and x_{j} is 1, 10 means elements where x_{i} is 1 and x_{j} is 0.
Cosine similarity can be calculated as follows:
where \( \cdot \) represents the Euclidean norm.
Gaussian similarity can be calculated as follows:
where \(\gamma\) represents hyper parameters. And \(\gamma = 1/\left( {\mathop \sum \limits_{i = 1}^{n} \left {x_{i} } \right/n} \right)\).
Feature selection module
Firstly, we evaluate the similarity between two drugs. The feature selection includes two steps: (1) calculating the similarity scores to evaluate correlation between drugs. (2) Generating feature vectors as the input to the prediction module.
The drugs’ feature can be represented as a binary vector, the value is 1 or 0. Value 1 means presence of components, value 0 means absence. For instance, the data set has 1622 types of categories. So the categories can be expressed as a 1622dimensional bit vector, the value means that the drug belongs to the category or not. Similarly, we can extract four binary feature vectors from one drug corresponding four features. Then we calculate the similarity between two drugs’ feature vectors by similarity measures. By this means, similarity matrices are generated as \(S = \left( {s_{ij} } \right)\), where the value of \({\text{s}}_{{{\text{ij}}}}\) is from 0 to 1. The closer the value is to 1, the higher the similar degree of drugs.
Prediction module using convolutional neural network
As shown in Fig. 1, CNNbased prediction module is the important part to predict DDIs’ events. Features selected from selection module are input vectors into the prediction module. Considering features selected contain noise and advantages of CNN, we decide to use CNN in the prediction module.
CNN is widely used and performs well on computer vision, like image classification, image detection and image segmentation. And powered by advanced deep learning technology, more and more studies have explored its application in bioinformatics field [30]. Compared with the pure deep neural network, CNN has the following advantages: (1) the convolutional layer has less parameters by using connections’ sparsity and parameters sharing. (2) The convolutional layer extracts information from global features and local features. On the task of DDIs’ prediction, Results of classification are strongly related to not only global drug features but also part of features combination. So it can enhance the capability of features learn. Consequently, in this article, we apply CNN as the supervised model for distilling integrated features information to predict DDIs.
The structure of prediction model is shown in Fig. 2. The prediction model based on CNN includes five convolutional layers, two fullconnected layers and a softmax layer. Among them, convolutional layers are mainly responsible for subspace feature extraction from the input vectors. Table 5 shows the specific configuration. The kernel size of each convolutional layer is same (3 × 1), and the filters’ number is increasing layerbylayer.
In addition, we add a residual block [31] to build one short connection between two layers. Figure 3 shows the structure of residual block. The output of residual block is expressed as follows:
where x is the input vectors, y is the output vectors. W_{1}, W_{2} are the weight vectors of two layers, b_{1}, b_{2} are the biases, and \(\sigma_{1}\) is the activation function of first layer.
The residual block strengthen the correlation of multilayer features. The short connection’s input vectors and output vectors must have the same dimensions, and the stacked convolutional layers’ output vectors are added together. It should be noted that no additional parameters are added in the residual block.
The output of each convolutional layer is passed through an activation function that enhances positive vectors and inhibits negative vectors from previous layer. In the paper, the activation function we use is Leaky ReLU. Compare with other activations, ReLU can increase feature sparsity and decrease the possibility of vanishing gradient. The expression is as follows:
where a represents hyperparameters, a is set 0.2.
There are two fullconnected layers after convolutional layers. The first fullconnected layer has 267 hidden units and the second has 65 hidden units. Considering predicting DDI’s events is a classification task, softmax function is used as the activation of the last fullconnected layer. So the loss function of the prediction module is as follows:
where K represents the number of events’ types, y_{i} represents the true value, 0 or 1.
The CNNDDI algorithm
The algorithm mainly contain two parts, combinational features selection module and CNNbased prediction module. The pseudocode of CNNDDI is shown in Algorithm 1.
Availability of data and materials
The datasets generated and/or analyzed during the current study are available in the drugbank repository and DDIMDL repository. https://go.drugbank.com/https://github.com/YifanDengWHU/DDIMDL.
Abbreviations
 DDIs:

Drug–drug interactions
 CNN:

Convolutional neural network
 DNN:

Deep neural network
 AUPR:

Area under the precisionrecall curve
 AUC:

Area under the ROC curve
 RF:

Random forest
 GBDT:

Gradient boosting decision tree
 LR:

Logistic regression
 KNN:

Knearest neighbor
 PCA:

Principal component analysis
References
Liu S, Tang B, Chen Q, et al. Drug–drug interaction extraction via convolutional neural networks. Comput Math Methods Med. 2016;56:1–8.
Hiroyuki K. How far should we go? Perspective of drugdrug interaction studies in drug development. Drug Metab Pharmacokinet. 2014;29(3):227–8.
Percha B, Altman RB. Informatics confronts drugdrug interactions. Trends Pharmacol Sci. 2013;34(3):178–84.
Fang H, Chen X, Pei X, Grant S, Tan M. Experimental design and statistical analysis for threedrug combination studies. Stat Methods Med Res. 2015;26(3):1261–80.
Isabel S, Paloma M, César S. Extracting drugdrug interactions from biomedical texts. BMC Bioinform. 2010;11(5):9.
Yan S, Jiang X, Chen Y. Text mining driven drugdrug interaction detection. In: 2013 IEEE international conference on bioinformatics and biomedicine. IEEE. 2013;349–54.
Tari L, Anwar S, Liang S, Cai J, Baral C. Discovering drugdrug interactions: a textmining and reasoning approach based on properties of drug metabolism. Bioinformatics. 2010;26(18):547–53.
Zhao T, Hu Y, Peng J, Cheng L. DeepLGP: a novel deep learning method for prioritizing lncRNA target genes. Bioinformatics. 2020;36(16):4466–72.
Zhao T, Hu Y, Cheng L. DeepDRM: a computational method for identifying diseaserelated metabolites based on graph deep learning approaches. Brief Bioinform. 2020;36(16):4466–72.
Ryu JY, Kim HU, Sang YL. Deep learning improves prediction of drugdrug and drugfood interactions. Proc Natl Acad Sci. 2018;115(18):201803294.
Deng Y, Xu X, Qiu Y, Xia J, Liu S. A multimodal deep learning framework for predicting drugdrug interaction events. In: 2020 15th IEEE international conference on automatic face and gesture. 2020.
Shi J, Huang H, Lin J, et al. Tmfuf: a triple matrix factorizationbased unified framework for predicting comprehensive drugdrug interactions of new drugs. BMC Bioinform. 2018;19(Suppl 14):411.
Peng J, Guan J, Hui W, et al. A novel subnetwork representation learning method for uncovering diseasedisease relationships. Methods. 2020.
Zhao T, Liu J, Zeng X, et al. Prediction and collection of proteinmetabolite interactions. Brief Bioinform. 2021.
Peng J, Xue H, Wei Z, Tuncali I, Hao J, Shang X. Integrating multinetwork topology for gene function prediction using deep neural networks. Brief Bioinform. 2021;22(2):2096–105.
Vilar S, Harpaz R, et al. Drugdrug interaction through molecular structure similarity analysis. J Am Med Inform Assoc. 2012;19(6):1066–74.
Cami A, Manzi S, Arnold A, Reis BY, Medina MA. Pharmacointeraction network models predict unknown drugdrug interactions. PLoS ONE. 2013;8(4):e61468.
Assarf G, Gideon S, Oran Y, et al. Indi: a computational framework for inferring drug interactions and their associated recommendations. Mol Syst Biol. 2012;17(8):592.
Vilar S, Uriarte E, Santana L, et al. Similaritybased modeling in largescale prediction of drugdrug interactions. Nat Protoc. 2014;9(9):2147–63.
Cheng F, Zhao Z. Machine learningbased prediction of drugdrug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties. J Am Med Inform Assoc. 2014;21(e2):e278–86.
Zhang W, Chen Y, Liu F, Luo F, Tian G, Li X. Predicting potential drugdrug interactions by integrating chemical, biological, phenotypic and network data. BMC Bioinform. 2017;18(1):18.
Yu H, Mao K, Shi J, et al. Predicting and understanding comprehensive drugdrug interactions via seminonnegative matrix factorization. BMC Syst Biol. 2018;12(S1):14.
Zhang WA, Jing KC, Huang FB. SFLLN: A sparse feature learning ensemble method with linear neighborhood regularization for predicting drugdrug interactions. Inf Sci. 2019;497(23):189–201.
Wiwshart D, et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46(D1): D1074–82.
Lee G, Park C, Ahn J. Novel deep learning model for more accurate prediction of drugdrug interaction effects. BMC Bioinform. 2019;20(1):415.
Alex K, Llya S, et al. Imagenet classification with deep convolutional neural notwork. Commun ACM. 2017;6(6):94–90.
Zhao T, Yang H, et al. Identifying drug–target interactions based on graph convolutional network and deep neural network. Brief Bioinform. 2020;22(2):2141–50.
Peng J, Wang Y, Guan J, Li J, Han R, Hao J, Wei Z, Shang X. An endtoend heterogeneous graph representation learningbased framework for drugtarget interaction prediction. Brief Bioinform. 2021.
Deepika S, Geetha TV. Drug side effect prediction through linear neighborhoods and multiple data source integration. J Biomed Inform. 2019;84:136–47.
Zhuang Z, Pan W, Shen X. A simple convolutional neural network for prediction of enhancerpromoter interactions with dna sequence data. Bioinformatics. 2019;35(17):2899–906.
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In: Conference on computer vision and pattern recognition (CVPR). 2016.
Acknowledgements
The authors thank the anonymous referees for their many useful suggestions.
About this supplement
This article has been published as part of BMC Bioinformatics Volume 23 Supplement 1, 2022: Selected articles from the Biological Ontologies and Knowledge bases workshop 2020. The full contents of the supplement are available online at https://bmcbioinformatics.biomedcentral.com/articles/supplements/volume23supplement1.
Funding
Publication costs are funded by the National Key Research and Development Program of China (2017YFC0907503). The funder had no role in the design of the work, data collection, data analysis, and writing the manuscript.
Author information
Authors and Affiliations
Contributions
TYZ helped revise this paper. CCZ and YL did the study’s experiments and wrote the paper. CCZ and YL contributed equally to this work. All authors have 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
Zhang, C., Lu, Y. & Zang, T. CNNDDI: a learningbased method for predicting drug–drug interactions using convolution neural networks. BMC Bioinformatics 23 (Suppl 1), 88 (2022). https://doi.org/10.1186/s12859022046122
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s12859022046122