Toward better drug discovery with knowledge graph
Abstract
Drug discovery is the process of new drug identification. This process is driven by the increasing data from existing chemical libraries and data banks. The knowledge graph is introduced to the domain of drug discovery for imposing an explicit structure to integrate heterogeneous biomedical data. The graph can provide structured relations among multiple entities and unstructured semantic relations associated with entities. In this review, we summarize knowledge graph-based works that implement drug repurposing and adverse drug reaction prediction for drug discovery. As knowledge representation learning is a common way to explore knowledge graphs for prediction problems, we introduce several representative embedding models to provide a comprehensive understanding of knowledge representation learning.
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Graph representation learning has been successful in characterizing drugs at the systems level without patient data to predict interactions with other drugs, protein targets, side effects, and diseases.
“Graph representation learning has been successful in characterizing drugs at the systems level without patient data to make predictions about interactions with other drugs, protein targets, side effects, diseases [6, 38, 39, 40, 48, 165].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [40]
Cites
KEGG: Kyoto Encyclopedia of Genes and Genomes
Kanehisa, M. (2000) · Nucleic Acids Research · doi:10.1093/nar/28.1.27 · Cited by 37,077 · see citing works
KEGG: Kyoto Encyclopedia of Genes and Genomes
Ogata, H., Goto, S., Sato, K., Fujibuchi, W., Bono, H., & Kanehisa, M. (1999) · Nucleic Acids Research · doi:10.1093/nar/27.1.29 · Cited by 31,232 · see citing works
Efficient Estimation of Word Representations in Vector Space
Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1301.3781 · Cited by 26,568 · see citing works
DeepWalk: online learning of social representations
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123 · see citing works
Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting
Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., & Woo, W.-C. (2015) · arXiv · doi:10.48550/arxiv.1506.04214 · Cited by 7,709 · see citing works
UniProt: a worldwide hub of protein knowledge
The UniProt Consortium (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1049 · Cited by 7,698 · see citing works
(2013) · Cited by 5,726 · see citing works
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 · see citing works
Neural Message Passing for Quantum ChemistryPreprint
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., & Dahl, G. E. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.01212 · Cited by 5,414 · see citing works
The Gene Ontology Resource: 20 years and still GOing strong
The Gene Ontology Consortium (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1055 · Cited by 4,747 · see citing works
Drug repurposing: progress, challenges and recommendations
Pushpakom, S., Iorio, F., Eyers, P. A., Escott, K. J., Hopper, S., Wells, A., Doig, A., Guilliams, T., Latimer, J., McNamee, C., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2018.168 · Cited by 4,718 · see citing works
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 · see citing works
Knowledge Graph Embedding by Translating on Hyperplanes
Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8870 · Cited by 3,915 · see citing works
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3070843 · Cited by 2,947 · see citing works
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 2,852 · see citing works
Embedding Entities and Relations for Learning and Inference in Knowledge Bases
Yang, B., Yih, W.-T., He, X., Gao, J., & Deng, L. (2014) · arXiv · doi:10.48550/arxiv.1412.6575 · Cited by 2,252 · see citing works
Complex Embeddings for Simple Link Prediction
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., & Bouchard, G. (2016) · arXiv · doi:10.48550/arxiv.1606.06357 · Cited by 2,100 · see citing works
A Three-Way Model for Collective Learning on Multi-Relational Data
Nickel, M., Tresp, V., & Kriegel, H. (2011) · Cited by 1,806 · see citing works
Reactome: a database of reactions, pathways and biological processes
Croft, D., O'Kelly, G., Wu, G., Haw, R., Gillespie, M., Matthews, L., Caudy, M., Garapati, P., Gopinath, G., Jassal, B., et al. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkq1018 · Cited by 1,751 · see citing works
Proceedings of the Web Conference 2021
(2021) · doi:10.1145/3442381 · Cited by 1,500 · see citing works
The SIDER database of drugs and side effects
Kuhn, M., Letunic, I., Jensen, L. J., & Bork, P. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1075 · Cited by 1,451 · see citing works
RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space
Sun, Z., Deng, Z.-H., Nie, J.-Y., & Tang, J. (2019) · arXiv · doi:10.48550/arxiv.1902.10197 · Cited by 1,414 · see citing works
DeepWalk: Online Learning of Social RepresentationsPreprint
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1403.6652 · Cited by 1,109 · see citing works
Trial of Solanezumab for Mild Dementia Due to Alzheimer’s Disease
Honig, L. S., Vellas, B., Woodward, M., Boada, M., Bullock, R., Borrie, M., Hager, K., Andreasen, N., Scarpini, E., Liu-Seifert, H., et al. (2018) · New England Journal of Medicine · doi:10.1056/nejmoa1705971 · Cited by 1,049 · see citing works
Data-driven prediction of drug effects and interactions
Tatonetti, N. P., Ye, P. P., Daneshjou, R., & Altman, R. B. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3003377 · Cited by 936 · see citing works
Neural Information Processing Systems (NIPS)
Shang, X., Zhu, Z., Leimkuhler, B., & Storkey, A. (2015) · Cited by 831 · see citing works
The Human Phenotype Ontology in 2017
Köhler, S., Vasilevsky, N., Engelstad, M., Foster, E. D., McMurry, J. A., Aymé, S., Baynam, G. S., Bello, S. M., Boerkoel, C. F., Boycott, K. M., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1039 · Cited by 757 · see citing works
TTD: Therapeutic Target Database
Chen, X. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.412 · Cited by 723 · see citing works
Drug discovery and development: Role of basic biological research
Mohs, R. C., & Greig, N. H. (2017) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1016/j.trci.2017.10.005 · Cited by 674 · see citing works
Systematic integration of biomedical knowledge prioritizes drugs for repurposing
Himmelstein, D. S., Lizee, A., Hessler, C., Brueggeman, L., Chen, S. L., Hadley, D., Green, A., Khankhanian, P., & Baranzini, S. E. (2017) · eLife · doi:10.7554/elife.26726 · Cited by 666 · see citing works
Deep learning improves prediction of drug–drug and drug–food interactions
Ryu, J. Y., Kim, H. U., & Lee, S. Y. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1803294115 · Cited by 662 · see citing works
Convolutional 2D Knowledge Graph EmbeddingsPreprint
Dettmers, T., Minervini, P., Stenetorp, P., & Riedel, S. (2017) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.833287 · Cited by 639 · see citing works
European Semantic Web Conference 2009
Nielsen, F. Å. (2009) · Cited by 553 · see citing works
Exploiting drug-disease relationships for computational drug repositioning
Dudley, J. T., Deshpande, T. A., & Butte, A. J. (2011) · Briefings in Bioinformatics · doi:10.1093/bib/bbr013 · Cited by 526 · see citing works
Prasad, V. K., & Mailankody, S. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2017.3601 · Cited by 503 · see citing works
Pirmohamed, M., Breckenridge, A., Kitteringham, N. R., & Park, B. K. (1998) · BMJ · doi:10.1136/bmj.316.7140.1295 · Cited by 457 · see citing works
A multimodal deep learning framework for predicting drug–drug interaction events
Deng, Y., Xu, X., Qiu, Y., Xia, J., Zhang, W., & Liu, S. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa501 · Cited by 457 · see citing works
An Overview of Drug Discovery and Development
Berdigaliyev, N., & Aljofan, M. (2020) · Future Medicinal Chemistry · doi:10.4155/fmc-2019-0307 · Cited by 409 · see citing works
A de novo molecular generation method using latent vector based generative adversarial network
Prykhodko, O., Johansson, S., Kotsias, P.-C., Arús‐Pous, J., Bjerrum, E. J., Engkvist, O., & Chen, H. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0397-9 · Cited by 395 · see citing works
Randomized SMILES strings improve the quality of molecular generative models
Arús‐Pous, J., Johansson, S., Prykhodko, O., Bjerrum, E. J., Tyrchan, C., Reymond, J., Chen, H., & Engkvist, O. (2019) · Journal of Cheminformatics · doi:10.1186/s13321-019-0393-0 · Cited by 384 · see citing works
Extremely high conductivity observed in the triple point topological metal MoP
Kumar, N., Sun, Y., Nicklas, M., Watzman, S. J., Young, O., Leermakers, I., Hornung, J., Klotz, J., Gooth, J., Manna, K., et al. (2019) · RePEc: Research Papers in Economics · Cited by 374 · see citing works
KGNN: Knowledge Graph Neural Network for Drug-Drug Interaction Prediction
Lin, X., Quan, Z., Wang, Z., Ma, T., & Zeng, X. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/380 · Cited by 364 · see citing works
Self-citationPharmGKB: The Pharmacogenomics Knowledge Base
Thorn, C. F., Klein, T. E., & Altman, R. B. (2013) · Methods in molecular biology · doi:10.1007/978-1-62703-435-7_20 · Cited by 362 · see citing works
Constructing knowledge graphs and their biomedical applications
Nicholson, D. N., & Greene, C. S. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.05.017 · Cited by 304 · see citing works
Liu, M., Wu, Y., Chen, Y., Sun, J., Zhao, Z., Chen, X.-W., Matheny, M. E., & Xu, H. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2011-000699 · Cited by 291 · see citing works
Systematic Evaluation of Drug–Disease Relationships to Identify Leads for Novel Drug Uses
Chiang, A. P.-F., & Butte, A. J. (2009) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2009.103 · Cited by 268 · see citing works
A Bayesian machine learning approach for drug target identification using diverse data types
Madhukar, N. S., Khade, P. K., Huang, L., Gayvert, K. M., Galletti, G., Stogniew, M., Allen, J. E., Giannakakou, P., & Elemento, O. (2019) · Nature Communications · doi:10.1038/s41467-019-12928-6 · Cited by 267 · see citing works
A Survey on Knowledge Graph Embedding: Approaches, Applications and Benchmarks
Dai, Y., Wang, S., Xiong, N. N., & Guo, W. (2020) · Electronics · doi:10.3390/electronics9050750 · Cited by 264 · see citing works
Repurpose Open Data to Discover Therapeutics for COVID-19 Using Deep Learning
Zeng, X., Song, X. H., Ma, T., Pan, X., Zhou, Y., Hou, Y., Zhang, Z., Li, K., Karypis, G., & Cheng, F. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00316 · Cited by 258 · see citing works
Self-citationDiscovering protein drug targets using knowledge graph embeddings
Mohamed, S. K., Nováček, V., & Nounu, A. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz600 · Cited by 227 · see citing works
Ding, Y., Tang, J., & Guo, F. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.10.028 · Cited by 223 · see citing works
SuperTarget goes quantitative: update on drug-target interactions
Hecker, N., Ahmed, J., von Eichborn, J., Dunkel, M., Macha, K., Eckert, A. P., Gilson, M. K., Bourne, P. E., & Preißner, R. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr912 · Cited by 213 · see citing works
MUFFIN: multi-scale feature fusion for drug–drug interaction prediction
Chen, Y., Ma, T., Yang, X., Wang, J., Song, B., & Zeng, X. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab169 · Cited by 211 · see citing works
Self-citationSumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization
Yu, Y., Huang, K., Zhang, C., Glass, L. M., Sun, J., & Xiao, C. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab207 · Cited by 201 · see citing works
Predicting drug side effects by multi-label learning and ensemble learning
Zhang, W., Liu, F., Luo, L., & Zhang, J. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0774-y · Cited by 187 · see citing works
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Zhang, W., Paudel, B., Zhang, W., Bernstein, A., & Chen, H. (2019) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3289600.3291014 · Cited by 180 · see citing works
Network embedding in biomedical data science
Su, C., Tong, J., Zhu, Y., Cui, P., & Wang, F. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby117 · Cited by 170 · see citing works
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 161 · see citing works
Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network
Karim, M. R., Cochez, M., Jares, J., Uddin, M., Beyan, O., & Decker, S. (2019) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3307339.3342161 · Cited by 161 · see citing works
Biological applications of knowledge graph embedding models
Mohamed, S. K., Nounu, A., & Nováček, V. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa012 · Cited by 143 · see citing works
Thafar, M. A., Olayan, R. S., Ashoor, H., Albaradei, S., Bajic, V. B., Gao, X., Gojobori, T., & Essack, M. (2020) · Journal of Cheminformatics · doi:10.1186/s13321-020-00447-2 · Cited by 143 · see citing works
A global network of biomedical relationships derived from text
Percha, B., & Altman, R. B. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty114 · Cited by 131 · see citing works
Knowledge Embedding Based Graph Convolutional Network
Yu, D., Yang, Y., Zhang, R., & Wu, Y. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449925 · Cited by 128 · see citing works
Sosa, D. N., Derry, A., Guo, M. G., Wei, E., Brinton, C., & Altman, R. B. (2019) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811215636_0041 · Cited by 110 · see citing works
Deep learning methods for biomedical named entity recognition: a survey and qualitative comparison
Song, B., Li, F., Liu, Y., & Zeng, X. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab282 · Cited by 109 · see citing works
Self-citationAbdelaziz, I., Fokoue, A., Hassanzadeh, O., Zhang, P., & Sadoghi, M. (2017) · Journal of Web Semantics · doi:10.1016/j.websem.2017.06.002 · Cited by 100 · see citing works
Zhao, K., & So, H. (2018) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2856535 · Cited by 88 · see citing works
Chu, Y., Shan, X., Chen, T., Jiang, M., Wang, Y., Wang, Q., Salahub, D. R., Xiong, Y., & Wei, D. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa205 · Cited by 88 · see citing works
Muñoz, E., Nováček, V., & Vandenbussche, P.-Y. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx099 · Cited by 76 · see citing works
Dai, Y., Guo, C., Guo, W., & Eickhoff, C. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa256 · Cited by 74 · see citing works
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Zhang, W., Paudel, B., Zhang, W., Bernstein, A., & Chen, H. (2019) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-162876 · Cited by 74 · see citing works
Knowledge-driven drug repurposing using a comprehensive drug knowledge graph
Zhu, Y., Che, C., Jin, B., Zhang, N., Su, C., & Wang, F. (2020) · Health Informatics Journal · doi:10.1177/1460458220937101 · Cited by 73 · see citing works
GrEDeL: A Knowledge Graph Embedding Based Method for Drug Discovery From Biomedical Literatures
Sang, S., Yang, Z., Liu, X., Wang, L., Lin, H., Wang, J., & Dumontier, M. (2018) · IEEE Access · doi:10.1109/access.2018.2886311 · Cited by 61 · see citing works
Sosa, D. N., Derry, A., Guo, M. G., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/727925 · Cited by 61 · see citing works
ParamE: Regarding Neural Network Parameters as Relation Embeddings for Knowledge Graph Completion
Che, F., Zhang, D., Tao, J., Niu, M., & Zhao, B. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i03.5665 · Cited by 58 · see citing works
FP2VEC: a new molecular featurizer for learning molecular properties
Jeon, W., & Kim, D. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz307 · Cited by 50 · see citing works
Drug knowledge bases and their applications in biomedical informatics research
Zhu, Y., Elemento, O., Pathak, J., & Wang, F. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx169 · Cited by 49 · see citing works
Biomedical discovery through the integrative biomedical knowledge hub (iBKH)
Su, C., Hou, Y., Zhou, M., Rajendran, S., Maasch, J. R. M. A., Abedi, Z., Zhang, H., Bai, Z., Cuturrufo, A., Guo, W. L., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106460 · Cited by 34 · see citing works
Injecting Background Knowledge into Embedding Models for Predictive Tasks on Knowledge Graphs
d’Amato, C., Quatraro, N. F., & Fanizzi, N. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-77385-4_26 · Cited by 26 · see citing works
Finding melanoma drugs through a probabilistic knowledge graph
McCusker, J. P., Dumontier, M., Yan, R., He, S., Dordick, J. S., & McGuinness, D. L. (2017) · PeerJ Computer Science · doi:10.7717/peerj-cs.106 · Cited by 22 · see citing works
Drug repurposing through joint learning on knowledge graphs and literaturePreprint
Alshahrani, M., & Hoehndorf, R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/385617 · Cited by 20 · see citing works
TeX-Graph: Coupled tensor-matrix knowledge-graph embedding for COVID-19 drug repurposing
Kanatsoulis, C. I., & Sidiropoulos, N. D. (2021) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611976700.68 · Cited by 14 · see citing works
Li, M. M., Huang, K., & Žitnik, M. (2021) · arXiv (Cornell University) · Cited by 14 · see citing works
ExplaiNE: An Approach for Explaining Network Embedding-based Link PredictionsPreprint
Kang, B., Lijffijt, J., & Bie, T. D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.12694 · Cited by 13 · see citing works
MQuadE: a Unified Model for Knowledge Fact Embedding
Yu, J., Cai, Y., Sun, M., & Li, P. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449879 · Cited by 13 · see citing works
Binarized Knowledge Graph Embeddings
Kishimoto, K., Hayashi, K., Akai, G., Shimbo, M., & Komatani, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-15712-8_12 · Cited by 12 · see citing works
Feeney, A., Gupta, R., Thost, V., Angell, R., Chandu, G., Adhikari, Y., & Ma, T. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.13975 · Cited by 8 · see citing works
BTDE: Block Term Decomposition Embedding for Link Prediction in Knowledge Graph
Luo, T., Yifan, W., Yu, M., Li, X., Zhao, M., Xu, T., Yu, J. X., Gao, J., & Yu, R. (2020) · Frontiers in artificial intelligence and applications · doi:10.3233/faia200171 · Cited by 6 · see citing works
A Novel Embedding Model for Knowledge Graph Completion Based on Multi-Task Learning
Dou, J., Tian, B., Zhang, Y., & Xing, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-73194-6_17 · Cited by 4 · see citing works
Binarized Knowledge Graph EmbeddingsPreprint
Kishimoto, K., Hayashi, K., Akai, G., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.02970 · Cited by 2 · see citing works
Flagged first, then by citations
8 of the 101 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Zhang, B., Shi, H., & Wang, H. (2023) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s410301 · Cited by 347 · see citing works
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 · see citing works
“…to characterize drugs at the systems level without patient data to make predictions about interactions with other drugs, protein targets, side effects and diseases 6,38–40, As with proteins, small molecules are modelled as 2D and 3D molecular graphs such that nodes are atoms and edges are bonds.”
Enrichr-KG: bridging enrichment analysis across multiple libraries
Evangelista, J. E., Xie, Z., Marino, G. B., Nguyen, N. L. U., Clarke, D. J. B., & Ma’ayan, A. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkad393 · Cited by 233 · see citing works
Learning spatial structures of proteins improves protein–protein interaction prediction
Song, B., Luo, X., Luo, X., Liu, Y., Niu, Z., & Zeng, X. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab558 · Cited by 134 · see citing works
Self-citationComprehensive evaluation of deep and graph learning on drug–drug interactions prediction
Lin, X., Dai, L., Zhou, Y., Yu, Z., Zhang, W., Shi, J., Cao, D., Li, Z., Chen, H., Song, B., et al. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad235 · Cited by 121 · see citing works
Self-citationAbu-Salih, B., Al‐Qurishi, M., Alweshah, M., AL-Smadi, M., Alfayez, R., & Saadeh, H. (2023) · Journal Of Big Data · doi:10.1186/s40537-023-00774-9 · Cited by 121 · see citing works
Drug-drug interactions prediction based on deep learning and knowledge graph: A review
Luo, H., Yin, W., Wang, J., Zhang, G., Liang, W., Luo, J., & Yan, C. (2024) · iScience · doi:10.1016/j.isci.2024.109148 · Cited by 111 · see citing works
Application of Artificial Intelligence in Drug–Drug Interactions Prediction: A Review
Zhang, Y., Deng, Z., Xu, X., Feng, Y., & Shang, J. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00582 · Cited by 105 · see citing works
KG-Predict: A knowledge graph computational framework for drug repurposing
Gao, Z., Ding, P., & Xu, R. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104133 · Cited by 99 · see citing works
Graph Neural Networks in Modern AI-Aided Drug Discovery
Zhang, O., Lin, H., Zhang, X., Wang, X., Wu, Z., Ye, Q., Zhao, W., Wang, J., Ying, K., Kang, Y., et al. (2025) · Chemical Reviews · doi:10.1021/acs.chemrev.5c00461 · Cited by 103 · see citing works
Machine learning on protein–protein interaction prediction: models, challenges and trends
Tang, T., Zhang, X., Liu, Y., Peng, H., Zheng, B., Yin, Y., & Zeng, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad076 · Cited by 89 · see citing works
Self-citationModality-DTA: Multimodality Fusion Strategy for Drug–Target Affinity Prediction
Yang, X., Niu, Z., Liu, Y., Song, B., Lü, W., Zeng, L., & Zeng, X. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3205282 · Cited by 72 · see citing works
Self-citationBai, X., He, S., Li, Y., Xie, Y.-B., Zhang, X., Du, W., & Li, J. (2025) · npj Computational Materials · doi:10.1038/s41524-025-01540-6 · Cited by 67 · see citing works
Explainability and white box in drug discovery
Kırboğa, K. K., Abbasi, S. W., & Küçüksille, E. U. (2023) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14262 · Cited by 60 · see citing works
Knowledge Graphs and Explainable AI in Healthcare
Rajabi, E., & Kafaie, S. (2022) · Information · doi:10.3390/info13100459 · Cited by 63 · see citing works
LMKG: A large-scale and multi-source medical knowledge graph for intelligent medicine applications
Yang, P., Wang, H., Huang, Y., Yang, S., Ya, Z., Huang, L., Zhang, Y., Wang, G., Yang, S., He, L. J., et al. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111323 · Cited by 55 · see citing works
Poisoning medical knowledge using large language models
Yang, J., Xu, H., Mirzoyan, S., Chen, T., Liu, Z., Liu, Z., Ju, W., Liu, L., Xiao, Z., Zhang, M., et al. (2024) · Nature Machine Intelligence · doi:10.1038/s42256-024-00899-3 · Cited by 63 · see citing works
Zhou, S., Liu, K., Li, D., Fu, C., Ning, Y., Ji, W., Liu, X., Xiao, B., & Wei, R. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103142 · Cited by 49 · see citing works
Deep Generative Models for Therapeutic Peptide Discovery: A Comprehensive Review
Lai, L., Liu, Y., Song, B., Li, K., & Zeng, X. (2025) · ACM Computing Surveys · doi:10.1145/3714455 · Cited by 47 · see citing works
Self-citationKGANSynergy: knowledge graph attention network for drug synergy prediction
Zhang, G., Gao, Z., Yan, C., Wang, J., Liang, W., Luo, J., & Luo, H. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad167 · Cited by 46 · see citing works
GraphXAI: a survey of graph neural networks (GNNs) for explainable AI (XAI)
Nandan, M., Mitra, S., & De, D. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11054-3 · Cited by 46 · see citing works
Bafna, M., Li, H., & Zhang, X. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad269 · Cited by 43 · see citing works
Wei, J., Zhuo, L., Zhou, Z., Lian, X., Fu, X., & Yao, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad247 · Cited by 43 · see citing works
Self-citationKG-Hub—building and exchanging biological knowledge graphs
Caufield, J. H., Putman, T., Schaper, K., Unni, D. R., Hegde, H., Callahan, T. J., Cappelletti, L., Moxon, S. A. T., Ravanmehr, V., Carbon, S., et al. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad418 · Cited by 39 · see citing works
Teng, S., Yin, C., Wang, Y., Chen, X., Yan, Z., Cui, L., & Wei, L. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106904 · Cited by 37 · see citing works
Aldughayfiq, B., Ashfaq, F., Jhanjhi, N. Z., & Humayun, M. (2023) · Healthcare · doi:10.3390/healthcare11121762 · Cited by 37 · see citing works
Djeddi, W. E., Hermi, K., Yahia, S. B., & Diallo, G. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05593-6 · Cited by 37 · see citing works
QSPR Analysis of Drugs for Treatment of Schizophrenia Using Topological Indices
Zhang, X., Saif, M. J., Idrees, N., Kanwal, S., Parveen, S., & Saeed, F. (2023) · ACS Omega · doi:10.1021/acsomega.3c05000 · Cited by 35 · see citing works
Computational Approaches to Drug Repurposing: Methods, Challenges, and Opportunities
Cousins, H. C., Nayar, G., & Altman, R. B. (2024) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-110123-025333 · Cited by 40 · see citing works
Deep ensemble learning for accurate retinal vessel segmentation
Du, L., Liu, H., Zhang, L., Lü, Y., Li, M., Hu, Y., & Zhang, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.106829 · Cited by 35 · see citing works
A Sample-Centric and Knowledge-Driven Computational Framework for Natural Products Drug Discovery
Gaudry, A., Pagni, M., Mehl, F., Moretti, S., Quirós-Guerrero, L.-M., Cappelletti, L., Rutz, A., Kaiser, M., Marcourt, L., Queiroz, E. F., et al. (2024) · ACS Central Science · doi:10.1021/acscentsci.3c00800 · Cited by 35 · see citing works
Prediction of multi-relational drug–gene interaction via Dynamic hyperGraph Contrastive Learning
Wen, T., Liu, Y., Lin, X., Song, B., & Zeng, X. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad371 · Cited by 33 · see citing works
Self-citationShen, X., Li, Z., Liu, Y., Song, B., & Zeng, X. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3335402 · Cited by 33 · see citing works
Self-citationConstruction of an aspect-level sentiment analysis model for online medical reviews
Zhao, Y., Zhang, L., Zeng, C., Lu, W., Chen, Y., & Fan, T. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103513 · Cited by 34 · see citing works
Huang, X., Wang, Y., Wu, J., Ding, W., & Pang, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124536 · Cited by 31 · see citing works
Jiang, J., Xu, J., Liu, Y., Song, B., Guo, X., Zeng, X., & Zou, Q. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad152 · Cited by 33 · see citing works
Self-citationDeep learning to catalyze inverse molecular design
Alshehri, A. S., & You, F. (2022) · Chemical Engineering Journal · doi:10.1016/j.cej.2022.136669 · Cited by 32 · see citing works
Zeng, J., & Jia, X. (2025) · Journal of Pharmaceutical Analysis · doi:10.1016/j.jpha.2025.101342 · Cited by 32 · see citing works
KR4SL: knowledge graph reasoning for explainable prediction of synthetic lethality
Zhang, K., Wu, M., Liu, Y., Feng, Y., & Zheng, J. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad261 · Cited by 27 · see citing works
Self-citationQiu, X., Wang, H., Tan, X., & Fang, Z. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108376 · Cited by 26 · see citing works
Lin, S., Zhang, G., Wei, D., & Xiong, Y. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105984 · Cited by 24 · see citing works
Fault diagnosis of the HVDC system based on the CatBoost algorithm using knowledge graphs
Wu, J., Li, Q., Chen, Q., Zhang, N., Mao, C., Yang, L., & Wang, J. (2023) · Frontiers in Energy Research · doi:10.3389/fenrg.2023.1144785 · Cited by 23 · see citing works
Nandi, S., Bhaduri, S., Das, D., Ghosh, P., Mandal, M., & Mitra, P. (2024) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.3c01161 · Cited by 23 · see citing works
Chen, J., Lu, Y., Zhang, Y., Huang, F., & Qin, J. (2023) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2023.100634 · Cited by 22 · see citing works
In silico protein function prediction: the rise of machine learning-based approaches
Chen, J., Gu, Z., Lai, L., & Pei, J. (2023) · Medical Review · doi:10.1515/mr-2023-0038 · Cited by 21 · see citing works
Zhang, C., Zang, T., & Zhao, T. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae043 · Cited by 22 · see citing works
An update on knowledge graphs and their current and potential applications in drug discovery
Serra, A., Fratello, M., Federico, A., & Greco, D. (2025) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2025.2490253 · Cited by 21 · see citing works
DrugRep-KG: Toward Learning a Unified Latent Space for Drug Repurposing Using Knowledge Graphs
Ghorbanali, Z., Zare‐Mirakabad, F., Akbari, M., Salehi, N., & Masoudi‐Nejad, A. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01291 · Cited by 21 · see citing works
HL7-FHIR-Based ContSys Formal Ontology for Enabling Continuity of Care Data Interoperability
Das, S., & Hussey, P. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13071024 · Cited by 21 · see citing works
Dynamic data‐driven railway bridge construction knowledge graph update method
Lai, J., Zhu, J., Guo, Y., You, J., Xie, Y., Wu, J., & Hu, Y. (2023) · Transactions in GIS · doi:10.1111/tgis.13111 · Cited by 21 · see citing works
MIBPred: Ensemble Learning-Based Metal Ion-Binding Protein Classifier
Zhang, H., Liu, S., Li, R., Yu, J.-W., Ye, D.-X., Yuan, S.-S., Lin, H., Cheng-bing, H., & Tang, H. (2024) · ACS Omega · doi:10.1021/acsomega.3c09587 · Cited by 20 · see citing works
iPiDA-LTR: Identifying piwi-interacting RNA-disease associations based on Learning to Rank
Zhang, W., Hou, J., & Liu, B. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010404 · Cited by 18 · see citing works
Knowledge Graphs and Their Applications in Drug Discovery
James, T., & Hennig, H. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-3449-3_9 · Cited by 18 · see citing works
Pan, Y., Deng, X., Zhuang, Y., & Li, J. (2022) · BioMed Research International · doi:10.1155/2022/3755460 · Cited by 17 · see citing works
Enabling Online Search and Fault Inference for Batteries Based on Knowledge Graph
Zhang, Z., Sun, Y., Zhang, L., Cheng, H., Cao, R., Liu, X., & Yang, S. (2023) · Batteries · doi:10.3390/batteries9020124 · Cited by 17 · see citing works
XGBoost-Based Intelligent Decision Making of HVDC System with Knowledge Graph
Li, Q., Chen, Q., Wu, J., Qiu, Y., Zhang, C., Huang, Y., Guo, J., & Yang, B. F. (2023) · Energies · doi:10.3390/en16052405 · Cited by 17 · see citing works
Han, S., Lee, J. E., Kang, S., So, M., Jin, H. K., Lee, J. H., Baek, S., Jun, H., Kim, T. Y., & Lee, Y. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae035 · Cited by 17 · see citing works
Recall DNA methylation levels at low coverage sites using a CNN model in WGBS
Luo, X., Wang, Y., Zou, Q., & Xu, L. (2023) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1011205 · Cited by 16 · see citing works
Lin, C., Wang, L., & Shi, L. (2022) · Methods · doi:10.1016/j.ymeth.2022.01.004 · Cited by 16 · see citing works
Alshehri, S., Alqarni, M. A., Namazi, N. I., Naguib, I. A., Venkatesan, K., Mosaad, Y. O., Pishnamazi, M., Alsubaiyel, A. M., & Abourehab, M. A. S. (2022) · Scientific Reports · doi:10.1038/s41598-022-17350-5 · Cited by 15 · see citing works
Poisoning scientific knowledge using large language modelsPreprint
Yang, J., Xu, H., Mirzoyan, S., Chen, T., Liu, Z., Ju, W., Liu, L., Zhang, M., & Wang, S. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.11.06.565928 · Cited by 15 · see citing works
MFDA: Multiview fusion based on dual-level attention for drug interaction prediction
Lin, K.-B., Kang, L., Yang, F., Lu, P., & Lu, J. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.1021329 · Cited by 14 · see citing works
Liu, Y., Xia, X., Gong, Y., Song, B., & Zeng, X. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102983 · Cited by 14 · see citing works
Self-citationKnowledge-augmented Graph Machine Learning for Drug Discovery: A Survey
Zhong, Z., Barkova, A., & Mottin, D. (2025) · ACM Computing Surveys · doi:10.1145/3744237 · Cited by 19 · see citing works
Su, Y.-S., Wang, M., Wang, P., Zheng, C.-H., Liu, Y.-S., & Zeng, X.-X. (2022) · Briefings Bioinform. · doi:10.1093/bib/bbac342 · Cited by 14 · see citing works
Self-citationMurali, V., Muralidhar, Y. P., Königs, C., Nair, M., Madhu, S., Nedungadi, P., Srinivasa, G., & Athri, P. (2022) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14092 · Cited by 13 · see citing works
Xiao, Z., Wang, L., Ding, Y., & Yu, L. (2022) · Methods · doi:10.1016/j.ymeth.2022.10.001 · Cited by 13 · see citing works
Liu, C., Yu, H., Chen, G., Yang, Q., Wang, Z., Niu, N., Han, L., Zhao, D., Wang, M., Liu, Y., et al. (2023) · eLife · doi:10.7554/elife.84414 · Cited by 13 · see citing works
Self-citationFeng, Z., Shen, Z., Li, H., & Li, S. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac465 · Cited by 12 · see citing works
Fouladvand, S., Gomez, F. R., Nilforoshan, H., Schwede, M., Noshad, M., Jee, O., You, J., Sosič, R., Leskovec, J., & Chen, J. H. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104407 · Cited by 12 · see citing works
An, Y., Tang, H., Jin, B., Xu, Y., & Wei, X. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02325-x · Cited by 12 · see citing works
Improvement of operation and maintenance efficiency of power transformers based on knowledge graphs
Yang, J., meng, Q., & Zhang, X. (2024) · IET Electric Power Applications · doi:10.1049/elp2.12418 · Cited by 12 · see citing works
Leveraging graph neural network for learner performance prediction
Hakkal, S., & Lahcen, A. A. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128724 · Cited by 12 · see citing works
Ling, Y., Qin, Z., & Ma, Z. (2025) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2025.105428 · Cited by 13 · see citing works
Effective drug-target affinity prediction via generative active learning
Liu, Y., Zhou, Z., Cao, X., Cao, D., & Zeng, X. (2024) · Information Sciences · doi:10.1016/j.ins.2024.121135 · Cited by 11 · see citing works
Self-citationZhang, C., Yang, H., Liu, X., Wu, R., Zong, H., Wu, E., Zhou, Y. K., Li, J., & Shen, B. (2025) · Journal of Medical Internet Research · doi:10.2196/67201 · Cited by 15 · see citing works
Xiang, S., Lin, H., Cai, F., & Jiang, Z. (2025) · Chinese Medicine · doi:10.1186/s13020-025-01226-7 · Cited by 11 · see citing works
Devarakonda, M. V., Mohanty, S., Sunkishala, R. R., Mallampalli, N., & Liu, X. (2024) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2024.104627 · Cited by 9 · see citing works
Using TransR to enhance drug repurposing knowledge graph for COVID-19 and its complications
Zhao, H., Li, H., Liu, Q., Dong, G., Hou, C., Li, Y., & Zhao, Y. (2023) · Methods · doi:10.1016/j.ymeth.2023.12.001 · Cited by 10 · see citing works
A divide and conquer framework for Knowledge Editing
Han, X., Li, R., Li, X., & Pan, J. Z. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110826 · Cited by 10 · see citing works
Examining Knowledge Extraction Processes from Heterogeneous Data Sources
Sarıkoz, S. K. (2023) · Brilliant Engineering · doi:10.36937/ben.2023.4798 · Cited by 9 · see citing works
MADGAN:A microbe-disease association prediction model based on generative adversarial networks
Hu, W., Yang, X., Wang, L., & Zhu, X. (2023) · Frontiers in Microbiology · doi:10.3389/fmicb.2023.1159076 · Cited by 9 · see citing works
Decentralized knowledge discovery using massive heterogenous data in Cognitive IoT
Jha, V., & Tripathi, P. (2023) · Cluster Computing · doi:10.1007/s10586-023-04154-z · Cited by 9 · see citing works
Liu, W., Ren, Z., & Chen, L. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113063 · Cited by 9 · see citing works
M2GCN: multi-modal graph convolutional network for modeling polypharmacy side effects
Liu, Q., Yao, E., Liu, C., Zhou, X., Li, Y., & Xu, M. (2022) · Applied Intelligence · doi:10.1007/s10489-022-03839-z · Cited by 9 · see citing works
GETdb: A comprehensive database for genetic and evolutionary features of drug targets
Zhang, Q., He, Y., Lu, Y.-P., Wei, Q.-H., Zhang, H., & Yuan, Q. (2024) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.04.006 · Cited by 8 · see citing works
Plausible reasoning and knowledge extraction in Cognitive IoT
Jha, V., & Tripathi, P. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19382-7 · Cited by 8 · see citing works
Remote Fault Diagnosis Method for Traction Drive Systems Based on Domain-Adapted Knowledge Graphs
Chen, Z., Mo, S., Xu, J., Cheng, Z., Peng, T., & Gui, W. (2024) · IEEE Transactions on Industrial Cyber-Physical Systems · doi:10.1109/ticps.2024.3477433 · Cited by 8 · see citing works
A few-shot link prediction framework to drug repurposing using multi-level attention network
Yang, C., Chen, X., Huang, J., An, Y., Huang, Z., & Sun, Y. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.107936 · Cited by 10 · see citing works
Artificial intelligence systems for the design of magic shotgun drugs
Moreira‐Filho, J. T., da Silva, M. F. B., Borba, J. V. V. B., Filho, A. R. G., Muratov, E. N., Andrade, C. H., Braga, R. C., & Neves, B. J. (2022) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2022.100055 · Cited by 8 · see citing works
Transporter proteins knowledge graph construction and its application in drug development
Chen, X.-H., Ruan, Y., Liu, Y., Duan, X., Jiang, F. X., Tang, H., Zhang, H., & Zhang, Q. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.05.001 · Cited by 8 · see citing works
Self-citationKamuntavičius, G., Prat, A., Paquet, T., Bastas, O., Aty, H. A., Sun, Q., Andersen, C., Harman, J., Siladi, M. E., Rines, D., et al. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00914-0 · Cited by 8 · see citing works
Khan, M. S., Maha, N., Riaz, M., Yasmin, T., Irfan, A., & Basra, M. A. R. (2023) · Journal of Computational Chemistry · doi:10.1002/jcc.27273 · Cited by 7 · see citing works
Hao, Y., Chen, X., Fei, A., Jia, Q., Chen, Y., Shao, J., Pandiyan, S. S., & Wang, L. (2024) · Molecules · doi:10.3390/molecules29020492 · Cited by 7 · see citing works
Impact of medical technologies may be predicted using constructed graph bibliometrics
Jiang, L., Raza, A., Ariss, A.-B., Chen, D. J., Danaher‐Garcia, N., Lee, J., & He, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-52233-x · Cited by 7 · see citing works
Robust temporal knowledge inference via pathway snapshots with liquid neural network
Han, P., Wang, J., Liu, D., Liu, L., & Song, T. (2025) · Methods · doi:10.1016/j.ymeth.2025.05.003 · Cited by 7 · see citing works
Tang, T., Shen, T., Jiang, J., Li, W., Wang, P., Yuan, S., Cao, X., & Liu, Y. (2025) · BMC Biology · doi:10.1186/s12915-025-02359-9 · Cited by 8 · see citing works
Self-citationResearch on the Computational Prediction of Essential Genes
Guo, Y., Ju, Y., Dong, C., & Wang, L. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.803608 · Cited by 7 · see citing works
Zeng, L., Liu, Y., Yu, Z., & Liu, Y. (2022) · Briefings in Functional Genomics · doi:10.1093/bfgp/elac023 · Cited by 7 · see citing works
Self-citationHe, L., Fu, Y., Hou, S., Wang, G., Zhao, J., Xing, Y., Li, S., & Ma, J. (2023) · Artificial Intelligence Chemistry · doi:10.1016/j.aichem.2023.100034 · Cited by 7 · see citing works
- Authors
- Zeng, X., Tu, X., Liu, Y., Fu, X., & Su, Y.
- Affiliations
- Hunan University (CN)
- Anhui University (CN)
- Corresponding author
- Liu, Yuansheng
- Venue
- Current Opinion in Structural Biology
- Publisher
- Elsevier BV
- Published
- 2022-02
- Volume
- 72
- Pages
- 114-126
- ISSN
- 0959-440X (ISSN-L)
- OpenAlex
- W3207365242
- Cited by
- 229
- References
- 101
- Citations by year
- 2026: 20 · 2025: 62 · 2024: 53 · 2023: 64 · 2022: 28 · 2021: 2
- FWCI
- 2.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Current Opinion in Structural Biology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Fundamental Research Funds for the Central Universities (531118010626)
- National Natural Science Foundation of China (61872309)
- National Key Research and Development Program of China (2021YFE0102100)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- knowledge graphs, drug discovery, drug repurposing, adverse drug reaction prediction, knowledge representation learning, entity embeddings, semantic relations
- MeSH terms
- Algorithms, Pattern Recognition, Automated, Knowledge, Drug Discovery, Drug Repositioning
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