Cited by 144Computational Drug Discovery MethodsBioinformatics and Genomic NetworksRecommender Systems and Techniques

The DOI matches its Crossref record; 99 of the 99 references it cites were found in a registry or an index.

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Open access

Thafar, Maha A., Olayan, Rawan S., Ashoor, Haitham, Albaradei, Somayah, Bajic, Vladimir B., Gao, Xin, Gojobori, Takashi, Essack, Magbubah·2020·Journal of Cheminformatics

DOI 10.1186/s13321-020-00447-2PMC PMC7325230

Open this paper in CoChat — free account Open PDF

Abstract

In silico prediction of drug–target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug–target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts D rug–T arget i nteractions using G raph E mbedding, graph M ining, and S imilarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug–target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug–target interactions graph with two other complementary graphs namely: drug–drug similarity, target–target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. DTiGEMS+ integrates multiple drug–drug similarities and target–target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1186/s13321-020-00447-2

Cites

99 of 99 references verified0 citation errors · 0 not checkable

Statistics

Fig. 1 DTIs prediction problem depictionCC BY · PubMed Central

Fig. 2 Integrating multiple similarities using different functionsCC BY · PubMed Central

Fig. 3 An illustration of Sum and Max scores for a D–D–D–T path structureCC BY · PubMed Central

Fig. 4 DTiGEMS+ prediction Framework. DTIs: drug–target interactions; DD: drug–drug; TT: target–target; FV: feature vector; FSS alg.: forward similarity selection algorithm; SNF fuc: similarity network fusion function; COS similarity: cosine similarity; ML: machine learningCC BY · PubMed Central

Fig. 5 Comparison results for DTiGEMS+ and other methods in terms of AUPR using the Yamanishi_08 datasets. The best performing method is indicated in blue, the second-best method in purple, and all other methods in greenCC BY · PubMed Central

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,869
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939754 · Cited by 10,926
KEGG: new perspectives on genomes, pathways, diseases and drugs
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y., & Morishima, K. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkw1092 · Cited by 8,865
DrugBank 5.0: a major update to the DrugBank database for 2018
Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Sajed, T., Johnson, D., Li, C., Sayeeda, Z., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gkx1037 · Cited by 8,404
Extended-Connectivity Fingerprints
Rogers, D. O. W., & Hahn, M. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci100050t · Cited by 7,925
The relationship between Precision-Recall and ROC curves
Davis, J., & Goadrich, M. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143874 · Cited by 6,559
Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation
Powers, D. M. W. (2020) · arXiv · doi:10.48550/arxiv.2010.16061 · Cited by 5,440
PubChem Substance and Compound databases
Kim, S., Thiessen, P. A., Bolton, E. E., Chen, J., Fu, G., Gindulyte, A., Han, L., He, J., He, S., Shoemaker, B. A., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv951 · Cited by 5,436
ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A., Bellis, L. J., Bento, A. P., Chambers, J., Davies, M., Hersey, A., Light, Y., McGlinchey, S., Michalovich, D., Al-Lazikani, B., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gkr777 · Cited by 4,382
The price of innovation: new estimates of drug development costs
DiMasi, J. A., Hansen, R. W., & Grabowski, H. G. (2003) · Journal of Health Economics · doi:10.1016/s0167-6296(02)00126-1 · Cited by 4,265
Drug repositioning: identifying and developing new uses for existing drugs
Ashburn, T. T., & Thor, K. B. (2004) · Nature Reviews Drug Discovery · doi:10.1038/nrd1468 · Cited by 3,237
From genomics to chemical genomics: new developments in KEGG
Kanehisa, M. I. (2005) · Nucleic Acids Research · doi:10.1093/nar/gkj102 · Cited by 3,126
DrugBank: a knowledgebase for drugs, drug actions and drug targets
Wishart, D. S., Knox, C., Guo, A. C., Cheng, D., Shrivastava, S., Tzur, D., Gautam, B., & Hassanali, M. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm958 · Cited by 2,896
The ChEMBL database in 2017
Gaulton, A., Hersey, A., Nowotka, M., Bento, A. P., Chambers, J. K., Méndez, D., Mutowo, P., Atkinson, F. L., Bellis, L. J., Cibrián–Uhalte, E., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1074 · Cited by 2,394
Best Practices for QSAR Model Development, Validation, and Exploitation
Tropsha, A. (2010) · Molecular Informatics · doi:10.1002/minf.201000061 · Cited by 2,107
Similarity network fusion for aggregating data types on a genomic scale
Wang, B., Mezlini, A. M., Demir, F., Fiume, M., Tu, Z., Brudno, M., Haibe-Kains, B., & Goldenberg, A. (2014) · Nature Methods · doi:10.1038/nmeth.2810 · Cited by 2,036
A Comprehensive Survey of Graph Embedding: Problems, Techniques, and Applications
Cai, H., Zheng, V. W., & Chang, K. C.-C. (2018) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2807452 · Cited by 2,034
Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research
Agatonović-Kuštrin, S., & Beresford, R. (2000) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/s0731-7085(99)00272-1 · Cited by 1,987
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine LearningPreprint
Lemaître, G., Nogueira, F., & Aridas, C. K. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1609.06570 · Cited by 1,923
Graph embedding techniques, applications, and performance: A survey
Goyal, P., & Ferrara, E. (2018) · Knowledge-Based Systems · doi:10.1016/j.knosys.2018.03.022 · Cited by 1,801
DeepDTA: deep drug–target binding affinity prediction
Öztürk, H., Özgür, A., & Özkırımlı, E. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty593 · Cited by 1,792
Drug—target network
Yıldırım, M. A., Goh, K.-I., Cusick, M. E., Barabási, A.-Ĺ., & Vidal, M. (2007) · Nature Biotechnology · doi:10.1038/nbt1338 · Cited by 1,744
The ChEMBL bioactivity database: an update
Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J. K., Davies, M., Krüger, F. A., Light, Y., Mak, L., McGlinchey, S., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1031 · Cited by 1,520
PubChem: Integrated Platform of Small Molecules and Biological Activities
Bolton, E., Wang, Y., Thiessen, P. A., & Bryant, S. H. (2008) · Annual reports in computational chemistry · doi:10.1016/s1574-1400(08)00012-1 · Cited by 1,267
Prediction of drug–target interaction networks from the integration of chemical and genomic spaces
Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., & Kanehisa, M. I. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn162 · Cited by 1,148
Gaussian interaction profile kernels for predicting drug–target interaction
van Laarhoven, T., Nabuurs, S. B., & Marchiori, E. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr500 · Cited by 943
A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information
Luo, Y., Zhao, X., Zhou, J., Yang, J., Zhang, Y., Kuang, W., Peng, J., Chen, L., & Zeng, J. (2017) · Nature Communications · doi:10.1038/s41467-017-00680-8 · Cited by 926
A side effect resource to capture phenotypic effects of drugs
Kuhn, M., Campillos, M., Letunic, I., Jensen, L. J., & Bork, P. (2010) · Molecular Systems Biology · doi:10.1038/msb.2009.98 · Cited by 899
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference
Cheng, F., Liu, C., Jiang, J., Lü, W., Li, W., Liu, G., Zhou, W., Huang, J. Y., & Tang, Y. (2012) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002503 · Cited by 827
Combining docking and molecular dynamic simulations in drug design
Alonso, H., Bliznyuk, A. A., & Gready, J. E. (2006) · Medicinal Research Reviews · doi:10.1002/med.20067 · Cited by 784
Structure-based maximal affinity model predicts small-molecule druggability
Cheng, A. C., Coleman, R. G., Smyth, K. T., Cao, Q., Soulard, P., Caffrey, D. R., Salzberg, A. C., & Huang, E. S. (2007) · Nature Biotechnology · doi:10.1038/nbt1273 · Cited by 714
Marginalized kernels between labeled graphs
Kashima, H., Tsuda, K., & Inokuchi, A. (2003) · Cited by 710
Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
Tsubaki, M., Tomii, K., & Sese, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty535 · Cited by 688
DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences
Lee, I., Keum, J., & Nam, H. (2019) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007129 · Cited by 671
SuperTarget and Matador: resources for exploring drug-target relationships
Gunther, S., Kuhn, M., Dunkel, M., Campillos, M., Senger, C., Petsalaki, E., Ahmed, J., Urdiales, E. G., Gewiess, A., Jensen, L. J., et al. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm862 · Cited by 647 · cited 1×
“…used biomedical literature and several reference databases, including KEGG [81, 82], DrugBank [83, 84], PubChem [85, 86], CheMBLE [87– 89], MATADOR [90], SuperTarget [90], Comparative Toxicogenomics Database (CTD) [91, 92], and the annotated database of common toxins and their targets (T3DB) [93].”
Method
Supervised prediction of drug–target interactions using bipartite local models
Kevin Bleakley, A., & Yamanishi, Y. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp433 · Cited by 638
The Comparative Toxicogenomics Database: update 2017
Davis, A. P., Grondin, C., Johnson, R. J., Sciaky, D., King, B. L., McMorran, R., Wiegers, J., Wiegers, T. C., & Mattingly, C. J. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw838 · Cited by 545
SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines
He, T., Heidemeyer, M., Ban, F., Cherkasov, A. R., & Ester, M. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0209-z · Cited by 529
Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework
Yamanishi, Y., Kotera, M., Kanehisa, M. I., & Goto, S. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq176 · Cited by 474
Graph kernels for chemical informatics
Ralaivola, L., Swamidass, S. J., Saigo, H., & Baldi, P. (2005) · Neural Networks · doi:10.1016/j.neunet.2005.07.009 · Cited by 459
Similarity-based machine learning methods for predicting drug–target interactions: a brief review
Ding, H., Takigawa, I., Mamitsuka, H., & Zhu, S. (2013) · Briefings in Bioinformatics · doi:10.1093/bib/bbt056 · Cited by 430
A Systematic Prediction of Multiple Drug-Target Interactions from Chemical, Genomic, and Pharmacological Data
Yu, H., Chen, M., Xu, X. J., Li, Y., Zhao, H., Fang, Y., Li, X., Zhou, W., Wang, W., & Wang, Y. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037608 · Cited by 426
Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction
Liu, Y., Wu, M., Miao, C., Zhao, P., & Li, X. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004760 · Cited by 392
Drug–target interaction prediction by learning from local information and neighbors
Mei, J.-P., Kwoh, C.-K., Yang, P., Li, X., & Zheng, J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts670 · Cited by 386
Annual Reports in Computational Chemistry
(2009) · Annual reports in computational chemistry · doi:10.1016/s1574-1400(09)00513-1 · Cited by 386
NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions
Wan, F., Hong, L., Xiao, A., Jiang, T., & Zeng, J. (2019) · Bioinformatics · doi:10.1093/bioinformatics/bty543 · Cited by 381
ChemmineR: a compound mining framework for R
Cao, Y., Charisi, A., Cheng, L.-C., Jiang, T., & Girke, T. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn307 · Cited by 374
End-to-End Learning From Spectrum Data: A Deep Learning Approach for Wireless Signal Identification in Spectrum Monitoring Applications
Kulin, M., Kazaz, T., Moerman, I., & Poorter, E. D. (2018) · IEEE Access · doi:10.1109/access.2018.2818794 · Cited by 361
Comparative Toxicogenomics Database: a knowledgebase and discovery tool for chemical-gene-disease networks
Davis, A. P., Murphy, C. G., Saraceni-Richards, C. A., Rosenstein, M. C., Wiegers, T. C., & Mattingly, C. J. (2008) · Nucleic Acids Research · doi:10.1093/nar/gkn580 · Cited by 323
Drug-Target Interaction Prediction with Graph Regularized Matrix Factorization
Ezzat, A., Zhao, P., Wu, M., Li, X., & Kwoh, C.-K. (2016) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2016.2530062 · Cited by 322
Computational prediction of drug–target interactions using chemogenomic approaches: an empirical survey
Ezzat, A., Wu, M., Li, X., & Kwoh, C. K. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby002 · Cited by 321
Interpretable Drug Target Prediction Using Deep Neural Representation
Gao, K. Y., Fokoue, A., Luo, H., Iyengar, A., Dey, S., & Zhang, P. (2018) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/468 · Cited by 247
Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach
Emig, D., Ivliev, A. E., Pustovalova, O., Lancashire, L., Bureeva, S. V., Nikolsky, Y., & Bessarabova, M. (2013) · PLoS ONE · doi:10.1371/journal.pone.0060618 · Cited by 242
A multiple kernel learning algorithm for drug-target interaction prediction
do Nascimento, A. C. A., Prudêncio, R. B. C., & Costa, I. G. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-0890-3 · Cited by 235
DDR: efficient computational method to predict drug–target interactions using graph mining and machine learning approaches
Olayan, R. S., Ashoor, H., & Bajić, V. B. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx731 · Cited by 233
Self-citation
node2vec: Scalable Feature Learning for Networks
Grover, A., & Leskovec, J. (2016) · PubMed · doi:10.48550/arxiv.1607.00653 · Cited by 229
Discovering protein drug targets using knowledge graph embeddings
Mohamed, S. K., Nováček, V., & Nounu, A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz600 · Cited by 225
Smote-variants: A python implementation of 85 minority oversampling techniques
Kovács, G. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.06.100 · Cited by 209
A comprehensive review of feature based methods for drug target interaction prediction
Sachdev, K., & Gupta, M. K. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103159 · Cited by 193
Deep mining heterogeneous networks of biomedical linked data to predict novel drug–target associations
Zong, N., Kim, H., Ngo, V., & Harismendy, O. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx160 · Cited by 190
Boosting compound-protein interaction prediction by deep learning
Tian, K., Shao, M., Wang, Y., Guan, J., & Zhou, S. (2016) · Methods · doi:10.1016/j.ymeth.2016.06.024 · Cited by 182
T3DB: a comprehensively annotated database of common toxins and their targets
Lim, E. L., Pon, A., Djoumbou-Feunang, Y., Knox, C., Shrivastava, S., Guo, A. C., Neveu, V., & Wishart, D. S. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkp934 · Cited by 168
Neuro-symbolic representation learning on biological knowledge graphs
Alshahrani, M., Khan, M. A., Maddouri, O., Kinjo, A. R., Queralt-Rosiñach, N., & Hoehndorf, R. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx275 · Cited by 168
Predicting drug-target interaction network using deep learning model
You, J., McLeod, R. D., & Hu, P. (2019) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2019.03.016 · Cited by 150
Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities
Thafar, M. A., Raies, A. B., Albaradei, S., Essack, M., & Bajić, V. B. (2019) · Frontiers in Chemistry · doi:10.3389/fchem.2019.00782 · Cited by 149
Self-citation
Predicting drug target interactions using meta-path-based semantic network analysis
Fu, G., Ding, Y., Seal, A., Chen, B., Sun, Y., & Bolton, E. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1005-x · Cited by 143
Target prediction utilising negative bioactivity data covering large chemical space
Mervin, L., Afzal, A. M., Drakakis, G., Lewis, R. P., Engkvist, O., & Bender, A. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0098-y · Cited by 138
Drug target prediction using adverse event report systems: a pharmacogenomic approach
Takarabe, M., Kotera, M., Nishimura, Y., Goto, S., & Yamanishi, Y. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts413 · Cited by 132
iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting
Rayhan, F., Ahmed, S., Shatabda, S., Farid, D. M., Mousavian, Z., Dehzangi, A., & Rahman, M. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-18025-2 · Cited by 121
Generative Oversampling for Mining Imbalanced Datasets.
Liu, A., Ghosh, J., & Martin, C. E. (2007) · DMIN · Cited by 110
Fast Gene Ontology based clustering for microarray experiments
Ovaska, K., Laakso, M., & Hautaniemi, S. (2008) · BioData Mining · doi:10.1186/1756-0381-1-11 · Cited by 109
Predicting drug-target interactions by dual-network integrated logistic matrix factorization
Hao, M., Bryant, S. H., & Wang, Y. (2017) · Scientific Reports · doi:10.1038/srep40376 · Cited by 107
Gradient boosting decision tree-based method for predicting interactions between target genes and drugs
Xuan, P., Sun, C., Zhang, T., Ye, Y., Shen, T., & Dong, Y. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00459 · Cited by 100
DASPfind: new efficient method to predict drug–target interactions
Ba-Alawi, W., Soufan, O., Essack, M., Kalnis, P., & Bajić, V. B. (2016) · Journal of Cheminformatics · doi:10.1186/s13321-016-0128-4 · Cited by 99
Self-citation
Revealing Drug-Target Interactions with Computational Models and Algorithms
Zhou, L., Li, Z., Yang, J., Tian, G., Liu, F., Wen, H., Li, P., Chen, M., Xiang, J., & Peng, L. (2019) · Molecules · doi:10.3390/molecules24091714 · Cited by 95
Identification of Common Biological Pathways and Drug Targets Across Multiple Respiratory Viruses Based on Human Host Gene Expression Analysis
Smith, S. B., Dampier, W., Tözeren, A., Brown, J. R., & Magid-Slav, M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033174 · Cited by 90
A novel descriptor based on atom-pair properties
Kuroda, M. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-016-0187-6 · Cited by 90
Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity
Wang, K., Sun, J., Zhou, S., Wan, C., Qin, S., Li, C., He, L., & Yang, L. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003315 · Cited by 75
Optimizing drug–target interaction prediction based on random walk on heterogeneous networks
Seal, A., Ahn, Y., & Wild, D. J. (2015) · Journal of Cheminformatics · doi:10.1186/s13321-015-0089-z · Cited by 63
Recent Advances in the Machine Learning-Based Drug-Target Interaction Prediction
Zhang, W., Lin, W., Zhang, D., Wang, S., Shi, J., & Niu, Y. (2018) · Current Drug Metabolism · doi:10.2174/1389200219666180821094047 · Cited by 58
Survey of Similarity-Based Prediction of Drug-Protein Interactions
Wang, C., & Kurgan, L. (2019) · Current Medicinal Chemistry · doi:10.2174/0929867326666190808154841 · Cited by 51
KeBABS: an R package for kernel-based analysis of biological sequences
Palme, J., Hochreiter, S., & Bodenhofer, U. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv176 · Cited by 48
Computational Discovery of Putative Leads for Drug Repositioning through Drug-Target Interaction Prediction
Coelho, E. D., Arrais, J. P., & Oliveira, J. L. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005219 · Cited by 46
Drug-Target Interactions: Prediction Methods and Applications
Anusuya, S., Kesherwani, M., Kanakaveti, V., Antonydhason, V., Shanmugam, G., Velmurugan, D., & Gromiha, M. M. (2016) · Current Protein and Peptide Science · doi:10.2174/1389203718666161108091609 · Cited by 39
Improved Prediction of Drug–Target Interactions Using Self-Paced Learning with Collaborative Matrix Factorization
Xia, L.-Y., Yang, Z., Zhang, H., & Liang, Y. (2019) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.9b00408 · Cited by 34
L2,1-GRMF: an improved graph regularized matrix factorization method to predict drug-target interactions
Cui, Z., Gao, Y.-L., Liu, J., Dai, L.-Y., & Yuan, S. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2768-7 · Cited by 26
Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives
Nath, A., Kumari, P., & Chaube, R. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-7756-7_2 · Cited by 25
Rchemcpp: a web service for structural analoging in ChEMBL, Drugbank and the Connectivity Map
Klambauer, G., Wischenbart, M., Mahr, M., Unterthiner, T., Mayr, A., & Hochreiter, S. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv373 · Cited by 24
Computational Methods for the Prediction of Drug-Target Interactions from Drug Fingerprints and Protein Sequences by Stacked Auto-Encoder Deep Neural Network
Wang, L., You, Z., Chen, X., Xia, S., Liu, F., Yan, X., & Zhou, Y. Z. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-59575-7_5 · Cited by 24
G.A.M.E.: GPU-accelerated mixture elucidator
Schurz, A., Su, B., Tu, Y.-S., Lu, T. T.-Y., Lin, O. A., & Tseng, Y. J. (2017) · Journal of Cheminformatics · doi:10.1186/s13321-017-0238-7 · Cited by 17
Network-based characterization of drug-protein interaction signatures with a space-efficient approach
Tabei, Y., Kotera, M., Sawada, R., & Yamanishi, Y. (2019) · BMC Systems Biology · doi:10.1186/s12918-019-0691-1 · Cited by 17
An integrated structure- and system-based framework to identify new targets of metabolites and known drugs
Naveed, H., Hameed, U. F. S., Harrus, D., Bourguet, W., Arold, S. T., & Gao, X. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv477 · Cited by 16
Self-citation
An integrative approach to develop computational pipeline for drug-target interaction network analysis
Bansal, A., Srivastava, P. A., & Singh, T. R. (2018) · Scientific Reports · doi:10.1038/s41598-018-28577-6 · Cited by 10
Integration of k-means clustering algorithm with network analysis for drug-target interactions network prediction
Aghakhani, S., Qabaja, A., & Alhajj, R. (2018) · International Journal of Data Mining and Bioinformatics · doi:10.1504/ijdmb.2018.094776 · Cited by 10
Ensemble learning algorithm for drug-target interaction prediction
Pathak, S., & Cai, X. (2017) · IEEE International Conference on Computational Advances in Bio and Medical Sciences · doi:10.1109/iccabs.2017.8114292 · Cited by 5
Integration of k-means clustering algorithm with network analysis for drug-target interactions network prediction
Aghakhani, S., Qabaja, A., & Alhajj, R. (2018) · International Journal of Data Mining and Bioinformatics · doi:10.1504/ijdmb.2018.10016075 · Cited by 4
Prediction of Drug-Target Interaction with Graph Regularized Non-Negative Matrix Factorization
Yan, X. W., Li, R., & Kang, L. (2019) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1237/3/032017 · Cited by 3

Research works printed but not linked: 2

Tenenbaum D (2019) KEGGREST: client-side REST access to KEGG. R Package Version 1.24.0. Fred Hutchinson Cancer Research Center, Seattle, WA, USA
Kam HT (1995) Random decision forest. In: Proceedings of the 3rd international conference on document analysis and recognition, vol 1416, pp 278–282

Sorted by citations

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review
Sabe, V. T., Ntombela, T., Jhamba, L. A., Maguire, G. E. M., Govender, T., Naicker, T., & Kruger, H. G. (2021) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2021.113705 · Cited by 665
Deep learning in drug discovery: an integrative review and future challenges
Askr, H., Elgeldawi, E., Ella, H. A., Elshaier, Y. A. M. M., Gomaa, M. M., & Hassanien, A. E. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10306-1 · Cited by 428
Graph representation learning in biomedicine and healthcare
Li, M. M., Huang, K., & Zitnik, M. (2022) · Nature Biomedical Engineering · doi:10.1038/s41551-022-00942-x · Cited by 294
DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations
Wang, J., Liu, X., Shen, S., Deng, L., & Liu, H. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab390 · Cited by 273
A unified drug–target interaction prediction framework based on knowledge graph and recommendation system
Ye, Q., Hsieh, C.-Y., Yang, Z., Kang, Y., Chen, J., Cao, D., He, S., & Hou, T. (2021) · Nature Communications · doi:10.1038/s41467-021-27137-3 · Cited by 261

See all 144

Authors: Thafar, M. A., Olayan, R. S., Ashoor, H., Albaradei, S., Bajic, V. B., Gao, X., Gojobori, T., & Essack, M.
Affiliations: Taif University (SA)
The Jackson Laboratory for Genomic Medicine (US)
King Abdulaziz University (SA)
King Abdullah University of Science and Technology (SA)
Corresponding author: Essack, Magbubah
ORCID: Thafar, Maha A.
Olayan, Rawan S.
Ashoor, Haitham
Albaradei, Somayah
Gao, Xin
Gojobori, Takashi
Essack, Magbubah
Venue: Journal of Cheminformatics
Publisher: Springer Science and Business Media LLC
Published: 2020-06-29
Volume: 12
Issue: 1
Pages: 44-44
ISSN: 1758-2946 (ISSN-L)
DOI: 10.1186/s13321-020-00447-2
PMC: PMC7325230
OpenAlex: W3039465695
Cited by: 143
References: 97
Citations by year: 2026: 12 · 2025: 18 · 2024: 28 · 2023: 24 · 2022: 15 · 2021: 44 · 2020: 2
FWCI: 10.68
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
Locations: Journal of Cheminformatics (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
The Mouseion at the JAXlibrary (Jackson Laboratory) (submittedVersion)
King Abdullah University of Science and Technology Repository (King Abdullah University of Science and Technology) (submittedVersion, cc-by)
Funders: King Abdullah University of Science and Technology (BAS/1/1606-01-01, BAS/1/1059-01-01, BAS/1/1624-01-01, FCC/1/1976-26-01, FCC/1/1976-17-01)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
Keywords: drug-target interaction prediction, graph embedding, graph mining, similarity-based prediction, heterogeneous networks, link prediction, drug-drug similarity, similarity fusion, drug repurposing, false positive reduction

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Abstract

Overview

Integrity signals

Figures

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks