Biomedical knowledge graphs driving new target identification and novel drug discovery

Abstract

Biomedical knowledge graphs (KGs) provide a structured and traceable framework for target-oriented, AI-assisted drug discovery. By linking genes, proteins, diseases, phenotypes, compounds, pathways, assays, structures, adverse events, and clinical evidence, biomedical KGs can partially mitigate evidence fragmentation and support hypothesis generation across molecular, cellular, and clinical levels. This review summarizes how biomedical KGs are constructed, reasoned over, evaluated, and translated into practical drug-discovery tasks. We first outline major data resources, entity and relation extraction, knowledge fusion, quality control, and large language model-assisted KG construction, with emphasis on ontology grounding, evidence tracing and hallucination control. We then compare key KG reasoning paradigms, including symbolic rule-based reasoning, representation learning, path- and subgraph-aware reasoning, graph Transformer models, and KG-LLM hybrid reasoning. Their strengths and limitations are discussed in relation to interpretability, scalability, leakage risk, benchmark bias, and prospective validation. We further highlight the need to incorporate three-dimensional structural evidence, including protein structures, binding pockets, variants, conformational states, and ligand compatibility, so that graph-based relevance can be connected with chemical tractability. Finally, we discuss applications of biomedical KGs in target prioritization, druggability assessment, mechanism interpretation, drug-target interaction prediction, molecular generation, lead optimization, ADMET and safety assessment, drug repurposing, combination therapy, biomarker discovery, precision medicine, and drug-resistance modeling. Current limitations include heterogeneous data quality, incomplete causal evidence, static graph representations, privacy constraints, and insufficient experimental or clinical validation. Overall, biomedical KGs should be viewed as evidence-integration and prioritization engines rather than automatic proof-generating systems for drug discovery, guiding rigorous, iterative, and experimentally grounded translational decisions.

Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (82425104 to Honglin Li), the National Key R&D Program of China (2022YFC3400504). Shiliang Li is also sponsored by the Shanghai Rising-Star Program (23QA1402800).

Ethical statements

Not applicable.

Author contributons

The authors confirm their contributions to the work as follows: conceptualization: Li S, Li H; manuscript writing: Dou P, Yang X; manuscript revision: Jiang Y, Xie X, Geng X, Huang H. All authors reviewed the results and approved the final version of the manuscript.

Data availability

Data sharing is not applicable to this review as no datasets were generated or analyzed.

Conflict of interest

The authors declare that there is no conflict of interest.

References

[1] Catacutan DB, Alexander J, Arnold A, Stokes JM. 2024. Machine learning in preclinical drug discovery. Nature Chemical Biology 20: 960-973

[2] Sadybekov AV, Katritch V. 2023. Computational approaches streamlining drug discovery. Nature 616: 673-685

[3] Ocana A, Pandiella A, Privat C, Bravo I, Luengo-Oroz M, et al. 2025. Integrating artificial intelligence in drug discovery and early drug development: a transformative approach. Biomarker Research 13: 45

[4] Houssein EH, Hosney ME, Emam MM, Younis EMG, Ali AA, et al. 2023. Soft computing techniques for biomedical data analysis: open issues and challenges. Artificial Intelligence Review 56: 2599-2649

[5] Sarvepalli S, Vadarevu S. 2025. Role of artificial intelligence in cancer drug discovery and development. Cancer Letters 627: 217821

[6] Li Z, Hua S, Song S, Luo Y, Qian W, et al. 2026. Multi-omics integration identifies MT2A as a biomarker and a candidate host target linking zinc dysregulation to COVID-19 mortality . Targetome 2: e006

[7] Peng C, Xia F, Naseriparsa M, Osborne F. 2023. Knowledge graphs: opportunities and challenges. Artificial Intelligence Review 56: 13071-13102

[8] Ding K, Zhu Z, Tang Y, Feng K, Zhuang X, et al. 2026. Bridging data and discovery: a survey on knowledge graphs in AI for science. National Science Review 13: nwag140

[9] Chen X, Jia S, Xiang Y. 2020. A review: knowledge reasoning over knowledge graph. Expert Systems with Applications 141: 112948

[10] Steiner T, Verborgh R, Troncy R, Gabarro J, Van de Walle R. 2012. Adding realtime coverage to the Google knowledge graph. ISWC-PD'12: Proceedings of the 2012th International Conference on Posters & Demonstrations Track. Volume 914. Aachen, DEU: CEUR-WS.org. pp. 65-68 doi: 10.5555/2887379.2887396

[11] Ji S, Pan S, Cambria E, Marttinen P, Yu PS. 2022. A survey on knowledge graphs: representation, acquisition, and applications. IEEE Transactions on Neural Networks and Learning Systems 33: 494-514

[12] Mendez D, Gaulton A, Bento AP, Chambers J, De Veij M, et al. 2019. ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Research 47: D930-D940

[13] The UniProt Consortium. 2025. UniProt: the Universal Protein Knowledgebase in 2025. Nucleic Acids Research 53: D609-D617

[14] Martin FJ, Amode MR, Aneja A, Austine-Orimoloye O, Azov AG, et al. 2023. Ensembl 2023. Nucleic Acids Research 51: D933-D941

[15] Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, et al. 2023. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Research 51: D638-D646

[16] Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. 2023. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Research 51: D587-D592

[17] Milacic M, Beavers D, Conley P, Gong C, Gillespie M, et al. 2024. The reactome pathway knowledgebase 2024. Nucleic Acids Research 52: D672-D678

[18] Piñero J, Ramírez-Anguita JM, Saüch-Pitarch J, Ronzano F, Centeno E, et al. 2020. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Research 48: D845-D855

[19] Knox C, Wilson M, Klinger CM, Franklin M, Oler E, et al. 2024. DrugBank 6.0: the DrugBank knowledgebase for 2024. Nucleic Acids Research 52: D1265-D1275

[20] Nguyen DT, Mathias S, Bologa C, Brunak S, Fernandez N, et al. 2017. Pharos: collating protein information to shed light on the druggable genome. Nucleic Acids Research 45: D995-D1002

[21] Chen X, Yan CC, Zhang X, Zhang X, Dai F, et al. 2016. Drug-target interaction prediction: databases, web servers and computational models. Briefings in Bioinformatics 17: 696-712

[22] Ioannidis VN, Zheng D, Karypis G. 2020. Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing. arXiv 00:2007.10261

[23] Chandak P, Huang K, Zitnik M. 2023. Building a knowledge graph to enable precision medicine. Scientific Data 10: 67

[24] Aronson AR. 2001. Effective mapping of biomedical text to the UMLS metathesaurus: The MetaMap Program. Journal of the American Medical Informatics Association 2001: 17-21

[25] Wang Y, Tang SC. 2022. The race to develop oral SERDs and other novel estrogen receptor inhibitors: recent clinical trial results and impact on treatment options. Cancer and Metastasis Reviews 41: 975-990

[26] Pérez A, Weegar R, Casillas A, Gojenola K, Oronoz M, et al. 2017. Semi-supervised medical entity recognition: a study on Spanish and Swedish clinical corpora. Journal of Biomedical Informatics 71: 16-30

[27] Bose P, Srinivasan S, Sleeman WC, Palta J, Kapoor R, et al. 2021. A survey on recent named entity recognition and relationship extraction techniques on clinical texts. Applied Sciences 11: 8319

[28] Devlin J, Chang MW, Lee K, Toutanova K. 2018. BERT: pre-training of deep bidirectional transformers for language understanding. arXiv 00:1810.04805

[29] Lee J, Yoon W, Kim S, Kim D, Kim S, et al. 2020. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36: 1234-1240

[30] Habibi M, Weber L, Neves M, Wiegandt DL, Leser U. 2017. Deep learning with word embeddings improves biomedical named entity recognition. Bioinformatics 33: i37-i48

[31] Boudjellal N, Zhang H, Khan A, Ahmad A. 2020. Biomedical relation extraction using distant supervision. Scientific Programming 2020: 8893749

[32] Luo L, Yang Z, Cao M, Wang L, Zhang Y, et al. 2020. A neural network-based joint learning approach for biomedical entity and relation extraction from biomedical literature. Journal of Biomedical Informatics 103: 103384

[33] Shvaiko P, Euzenat J. 2013. Ontology matching: state of the art and future challenges. IEEE Transactions on Knowledge and Data Engineering 25: 158-176

[34] Christen P. 2012. Data matching: concepts and techniques for record linkage, entity resolution, and duplicate detection . Berlin, Heidelberg: Springer. doi: 10.1007/978-3-642-31164-2

[35] Bordes A, Usunier N, Garcia-Durán A, Weston J, Yakhnenko O. 2013. Translating embeddings for modeling multi-relational data. NIPS'13: Proceedings of the 27th International Conference on Neural Information Processing Systems 2: 2787-2795

[36] Sun Z, Zhang Q, Hu W, Wang C, Chen M, et al. 2020. A benchmarking study of embedding-based entity alignment for knowledge graphs. Proceedings of the VLDB Endowment 13: 2326-2340

[37] Paulheim H. 2016. Knowledge graph refinement: a survey of approaches and evaluation methods. Semantic Web 8: 489-508

[38] Pan S, Luo L, Wang Y, Chen C, Wang J, et al. 2024. Unifying large language models and knowledge graphs: a roadmap. IEEE Transactions on Knowledge and Data Engineering 36: 3580-3599

[39] Ye H, Zhang N, Chen H, Chen H. 2022. Generative knowledge graph construction: a review. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 2022 . USA: Association for Computational Linguistics. pp. 1-17 doi: 10.18653/v1/2022.emnlp-main.1

[40] Zhang J, Qi S, Dong Y, Yuan L, Shen T, et al. 2026. GKG-LLM: a unified framework for generalized knowledge graph construction. Information Fusion 128: 103956

[41] Wang Q, Li C, Liu Y, Zhu Q, Song J, et al. 2025. An adaptive framework embedded with LLM for knowledge graph construction. IEEE Transactions on Multimedia 27: 2912-2923

[42] Ding Y, Li J, Wang P, Tang Z, Yan B, et al. 2024. Rethinking negative instances for generative named entity recognition. Findings of the Association for Computational Linguistics: ACL 2024, Bangkok, Thailand . pp. 3461-3475 doi: 10.18653/v1/2024.findings-acl.206

[43] Zhang B, Soh H. 2024. Extract, define, canonicalize: an LLM-based framework for knowledge graph construction. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, Miami, Florida, USA. pp. 9820-9836 doi: 10.18653/v1/2024.emnlp-main.548

[44] Gao S, Yu K, Yang Y, Yu S, Shi C, et al. 2025. Large language model powered knowledge graph construction for mental health exploration. Nature Communications 16: 7526

[45] Dobbins NJ. 2025. Generalizable and scalable multistage biomedical concept normalization leveraging large language models. Research Synthesis Methods 16: 479-490

[46] Lin Z, Zhang Z, Wu J, Zheng Y, Wu X. 2025. Guiding large language models for biomedical entity linking via restrictive and contrastive decoding. Findings of the Association for Computational Linguistics: EMNLP 2025, Suzhou, China, 2025. Stroudsburg, PA, USA: Association for Computational Linguistics. pp. 23745-23759 doi: 10.18653/v1/2025.findings-emnlp.1292

[47] Singhal K, Azizi S, Tu T, Mahdavi SS, Wei J, et al. 2023. Large language models encode clinical knowledge. Nature 620: 172-180

[48] Chen Q, Hu Y, Peng X, Xie Q, Jin Q, et al. 2025. Benchmarking large language models for biomedical natural language processing applications and recommendations. Nature Communications 16: 3280

[49] Galárraga L, Teflioudi C, Hose K, Suchanek FM. 2015. Fast rule mining in ontological knowledge bases with AMIE+. VLDB Journal 24: 707-730

[50] Meilicke C, Chekol MW, Ruffinelli D, Stuckenschmidt H. 2019. Anytime bottom-up rule learning for knowledge graph completion. IJCAI'19: Proceedings of the 28th International Joint Conference on Artificial Intelligence. Macao, China: AAAI Press. pp. 3137-3143 doi: 10.5555/3367471.3367477

[51] Yang F, Yang Z, Cohen WW. 2017. Differentiable learning of logical rules for knowledge base reasoning. arXiv 00:1702.08367

[52] Sadeghian A, Armandpour M, Ding P, Wang DZ. 2019. DRUM: end-to-end differentiable rule mining on knowledge graphs. arXiv 00:1911.00055

[53] Yang B, Yih WT, He X, Gao J, Deng L. 2014. Embedding entities and relations for learning and inference in knowledge bases. arXiv 00:1412.6575

[54] Trouillon T, Welbl J, Riedel S, Gaussier E, Bouchard G. 2016. Complex embeddings for simple link prediction. arXiv 00:1606.06357

[55] Sun Z, Deng ZH, Nie JY, Tang J. 2019. RotatE: knowledge graph embedding by relational rotation in complex space. arXiv 00:1902.10197

[56] Schlichtkrull M, Kipf TN, Bloem P, van den Berg R, Titov I, et al. 2018. Modeling relational data with graph convolutional networks. The Semantic Web. Lecture Notes in Computer Science 10843: 593-607

[57] Lao N, Mitchell T, Cohen WW. 2011. Random walk inference and learning in a large scale knowledge base. Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing . Edinburgh, United Kingdom: Association for Computational Linguistics. pp. 529-539 https://aclanthology.org/D11-1049/

[58] Xiong W, Hoang T, Wang WY. 2017. DeepPath: a reinforcement learning method for knowledge graph reasoning. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, Copenhagen, Denmark, 2017. Stroudsburg, PA, USA: Association for Computational Linguistics. pp. 564-573 doi: 10.18653/v1/D17-1060

[59] Teru KK, Denis E, Hamilton WL. 2019. Inductive relation prediction by subgraph reasoning. arXiv 00:1911.06962

[60] Xu XH, Zhang P, He YQ, Chao CP, Yan CY. 2022. Subgraph neighboring relations infomax for inductive link prediction on knowledge graphs. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, Vienna, Austria, 2022. International Joint Conferences on Artificial Intelligence Organization. pp. 2341-2347 doi: 10.24963/ijcai.2022/325

[61] Zhu ZC, Zhang ZB, Xhonneux LP, Tang J. 2021. Neural Bellman-Ford Networks: a general graph neural network framework for link prediction. arXiv 00: 2106.06935

[62] Zhu ZC, Yuan X, Galkin M, Xhonneux S, Zhang M, et al. 2022. A*Net: a scalable path-based reasoning approach for knowledge graphs. arXiv 00:2206.04798

[63] Ying C, Cai T, Luo S, Zheng S, Ke G, et al. 2021. Do transformers really perform bad for graph representation? arXiv 00:2106.05234

[64] Rampášek L, Galkin M, Dwivedi VP, Luu AT, Wolf G, et al. 2022. Recipe for a general, powerful, scalable graph transformer. arXiv 00:2205.12454

[65] Behrouz A, Hashemi F. 2024. Graph Mamba: towards learning on graphs with state space models. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Barcelona, Spain, 2024. New York, NY, USA: Association for Computing Machinery. pp. 119-130 doi: 10.1145/3637528.3672044

[66] Yao L, Mao C, Luo Y. 2019. KG-BERT: BERT for knowledge graph completion. arXiv 00:1909.03193

[67] Tang J, Yang Y, Wei W, Shi L, Su L, et al. 2024. GraphGPT: graph instruction tuning for large language models. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, DC, USA, 2024. New York, NY, USA: Association for Computing Machinery. pp. 491-500 doi: 10.1145/3626772.3657775

[68] Sun J, Xu C, Tang L, Wang S, Lin C, et al. 2023. Think-on-Graph: deep and responsible reasoning of large language model on knowledge graph. arXiv 00:2307.07697

[69] Ma J, Gao ZT, Chai Q, Sun WC, Wang PH, et al. 2025. Debate on Graph: a flexible and reliable reasoning framework for large language models. Proceedings of the AAAI Conference on Artificial Intelligence 39: 24768-24776

[70] Jumper J, Evans R, Pritzel A, Green T, Figurnov M, et al. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596: 583-589

[71] Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, et al. 2021. Accurate prediction of protein structures and interactions using a three-track neural network. Science 373: 871-876

[72] Lin Z, Akin H, Rao R, Hie B, Zhu Z, et al. 2023. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379: 1123-1130

[73] Abramson J, Adler J, Dunger J, Evans R, Green T, et al. 2024. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630: 493-500

[74] Le Guilloux V, Schmidtke P, Tuffery P. 2009. Fpocket: an open source platform for ligand pocket detection. BMC Bioinformatics 10: 168

[75] Halgren TA. 2009. Identifying and characterizing binding sites and assessing druggability. Journal of Chemical Information and Modeling 49: 377-389

[76] Schmidtke P, Bidon-Chanal A, Luque FJ, Barril X. 2011. MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories. Bioinformatics 27: 3276-3285

[77] Bowman GR, Geissler PL. 2012. Equilibrium fluctuations of a single folded protein reveal a multitude of potential cryptic allosteric sites. Proceedings of the National Academy of Sciences of the United States of America 109: 11681-11686

[78] Zhang Y, Li Z, Duan B, Qin L, Peng J. 2022. MKGE: knowledge graph embedding with molecular structure information. Computational Biology and Chemistry 100: 107730

[79] Daza D, Alivanistos D, Mitra P, Pijnenburg T, Cochez M, et al. 2023. BioBLP: a modular framework for learning on multimodal biomedical knowledge graphs. Journal of Biomedical Semantics 14: 20

[80] Kuzmanic A, Bowman GR, Juarez-Jimenez J, Michel J, Gervasio FL. 2020. Investigating cryptic binding sites by molecular dynamics simulations. Accounts of Chemical Research 53: 654-661

[81] Hu W, Fey M, Zitnik M, Dong Y, Ren H, et al. 2020. Open graph benchmark: datasets for machine learning on graphs. arXiv 00:2005.00687

[82] Toutanova K, Chen D. 2015. Observed versus latent features for knowledge base and text inference. Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality. Beijing, China, 2015. Beijing, China: Association for Computational Linguistics. pp. 57-66 doi: 10.18653/v1/W15-4007

[83] Balazevic I, Allen C, Hospedales T. 2019. TuckER: tensor factorization for knowledge graph completion. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong, China, 2019. PA, USA: Association for Computational Linguistics. pp. 5185-5194 doi: 10.18653/v1/D19-1522

[84] Vashishth S, Sanyal S, Nitin V, Talukdar PP. 2019. Composition-based multi-relational graph convolutional networks. arXiv 00: 1911.03082

[85] Chen S, Liu X, Gao J, Jiao J, Zhang R, et al. 2021. HittER: hierarchical transformers for knowledge graph embeddings. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. Punta Cana, Dominican Republic, 2021. Stroudsburg, PA, USA: Association for Computational Linguistics. pp. 10395-10407 doi: 10.18653/v1/2021.emnlp-main.812

[86] Liu J, Mao Q, Jiang W, Li J. 2024. KnowFormer: revisiting transformers for knowledge graph reasoning. arXiv 00:2409.12865

[87] Wang L, Zhao W, Wei Z, Liu J. 2022. SimKGC: simple contrastive knowledge graph completion with pre-trained language models. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Dublin, Ireland, 2022. Stroudsburg, PA, USA: Association for Computational Linguistics. pp. 4281-4294 doi: 10.18653/v1/2022.acl-long.295

[88] Liu Y, Cao Y, Lin X, Shang Y, Wang S, et al. 2025. Enhancing large language model for knowledge graph completion via structure-aware alignment-tuning. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, Suzhou, China, 2025. Stroudsburg, PA, USA: Association for Computational Linguistics. pp. 20970-20984 doi: 10.18653/v1/2025.emnlp-main.1061

[89] Hou Y, Zhou S, Liang K, Meng L, Chen X, et al. 2025. Soft Reasoning Paths for Knowledge Graph Completion. arXiv 00:2505.03285

[90] Himmelstein DS, Lizee A, Hessler C, Brueggeman L, Chen SL, et al. 2017. Systematic integration of biomedical knowledge prioritizes drugs for repurposing. eLife 6: e26726

[91] Betz P, Meilicke C, Stuckenschmidt H. 2022. Supervised knowledge aggregation for knowledge graph completion. In The Semantic Web, eds Groth P, Vidal ME, Suchanek F, Szekely P, Kapanipathi P, et al. Cham: Springer. pp. 74-92 doi: 10.1007/978-3-031-06981-9_5

[92] Das R, Dhuliawala S, Zaheer M, Vilnis L, Durugkar I, et al. 2017. Go for a walk and arrive at the answer: reasoning over paths in knowledge bases using reinforcement learning. arXiv 00:1711.05851

[93] Liu Y, Hildebrandt M, Joblin M, Ringsquandl M, Raissouni R, et al. Neural Multi-hop Reasoning with Logical Rules on Biomedical Knowledge Graphs. The Semantic Web, eds Verborgh R, Hose K, Paulheim H, Champin PA, Maleshkova M, et al. Cham: Springer. pp. 375-391 doi: 10.1007/978-3-030-77385-4_22

[94] Liben-Nowell D, Kleinberg J. 2003. The link prediction problem for social networks. CIKM '03: Proceedings of the twelfth international conference on Information and knowledge management. New Orleans, LA, USA: Association for Computing Machinery. pp. 556-559 doi: 10.1145/956863.956972

[95] Zhou T, Lü L, Zhang YC. 2009. Predicting missing links via local information. The European Physical Journal B 71: 623-630

[96] Zhang M, Chen Y. 2018. Link prediction based on graph neural networks. arXiv 00:1802.09691

[97] Song X, Ma R, Li J, Zhang M, Wipf DP. 2021. Network in graph neural network. arXiv 00:2111.11638

[98] Wang X, Yang H, Zhang M. 2023. Neural common neighbor with completion for link prediction. arXiv 00:2302.00890

[99] Yin T, Chen S, Wu X, Tian W. 2017. GenePANDA-a novel network-based gene prioritizing tool for complex diseases. Scientific Reports 7: 43258

[100] Ghiassian SD, Menche J, Barabási AL. 2015. A DIseAse MOdule detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome. PLoS Computational Biology 11: e1004120

[101] Wang X, Yang K, Jia T, Gu F, Wang C, et al. 2024. KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition. Briefings in Bioinformatics 25: bbae161

[102] Xie J, Rao J, Xie J, Zhao H, Yang Y. 2024. Predicting disease-gene associations through self-supervised mutual infomax graph convolution network. Computers in Biology and Medicine 170: 108048

[103] Kumar P, Metzger VT, Purushotham ST, Kedia P, Bologa CG, et al. 2025. KG2ML: integrating knowledge graphs and positive unlabeled learning for identifying disease-associated genes. Frontiers in Bioinformatics 5: 1727953

[104] Liu C, Xiao K, Yu C, Lei Y, Lyu K, et al. 2024. A probabilistic knowledge graph for target identification. PLoS Computational Biology 20: e1011945

[105] Shen X, Yan S, Zeng T, Xia F, Jiang D, et al. 2025. TarIKGC: a target identification tool using semantics-enhanced knowledge graph completion with application to CDK2 inhibitor discovery. Journal of Medicinal Chemistry 68: 1793-1809

[106] Mountjoy E, Schmidt EM, Carmona M, Schwartzentruber J, Peat G, et al. 2021. An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nature Genetics 53: 1527-1533

[107] Pacini C, Duncan E, Goncalves E, Gilbert J, Bhosle S, et al. 2024. A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization. Cancer Cell 42: 301-316.e9

[108] Quan Y, Xiong ZK, Zhang KX, Zhang QY, Zhang W, et al. 2023. Evolution-strengthened knowledge graph enables predicting the targetability and druggability of genes. PNAS Nexus 2: pgad147

[109] Ye Q, Zeng Y, Jiang L, Kang Y, Pan P, et al. 2025. A knowledge-guided graph learning approach bridging phenotype- and target-based drug discovery. Advanced Science 12: e2412402

[110] Xu J, Yu C, Xu J, Torvik VI, Kang J, et al. 2025. PubMed knowledge graph 2.0: connecting papers, patents, and clinical trials in biomedical science. Scientific Data 12: 1018

[111] Hopkins AL, Groom CR. 2002. The druggable genome. Nature Reviews Drug Discovery 1: 727-730

[112] Zhou Y, Zhang Y, Zhao D, Yu X, Shen X, et al. 2024. TTD: Therapeutic Target Database describing target druggability information . Nucleic Acids Research 52: D1465-D1477

[113] Wagner JR, Lee CT, Durrant JD, Malmstrom RD, Feher VA, et al. 2016. Emerging computational methods for the rational discovery of allosteric drugs. Chemical Reviews 116: 6370-6390

[114] Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, et al. 2000. The protein data bank. Nucleic Acids Research 28: 235-242

[115] Volkamer A, Kuhn D, Rippmann F, Rarey M. 2012. DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics 28: 2074-2075

[116] Raies A, Tulodziecka E, Stainer J, Middleton L, Dhindsa RS, et al. 2022. DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets. Communications Biology 5: 1291

[117] Borhani N, Izadi I, Motahharynia A, Sheikholeslami M, Gheisari Y. 2025. DrugTar improves druggability prediction by integrating large language models and gene ontologies. Bioinformatics 41: btaf360

[118] Chen A, Zhu X, Jiang H, Gong M, Cui S, et al. 2025. Combination of silybin and carvedilol synergistically alleviates liver fibrosis by inhibiting Wnt/ β-catenin signaling . Targetome 1: e009

[119] Wang B, Mezlini AM, Demir F, Fiume M, Tu Z, et al. 2014. Similarity network fusion for aggregating data types on a genomic scale. Nature Methods 11: 333-337

[120] Chen YX, Chen H, Rong Y, Jiang F, Chen JB, et al. 2020. An integrative multi-omics network-based approach identifies key regulators for breast cancer. Computational and Structural Biotechnology Journal 18: 2826-2835

[121] Tripathy RK, Frohock Z, Wang H, Cary GA, Keegan S, et al. 2025. Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks. npj Systems Biology and Applications 11: 43

[122] Hu EY, Oleshko S, Firmani S, Cheng H, Zhu Z, et al. 2026. Enhancing link prediction in biomedical knowledge graphs with BioPathNet. Nature Biomedical Engineering

[123] Lan Y, He S, Liu K, Zeng X, Liu S, et al. 2021. Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion. BMC Medical Informatics and Decision Making 21: 335

[124] Messina F, Giombini E, Agrati C, Vairo F, Ascoli Bartoli T, et al. 2020. COVID-19: viral-host interactome analyzed by network based-approach model to study pathogenesis of SARS-CoV-2 infection. Journal of Translational Medicine 18: 233

[125] Sun H, Song Z, Chen Q, Wang M, Tang F, et al. 2023. MMiKG: a knowledge graph-based platform for path mining of microbiota-mental diseases interactions. Brief Bioinform 24: bbad340

[126] Goetz SL, Glen AK, Glusman G. 2025. MicrobiomeKG: bridging microbiome research and host health through knowledge graphs. Frontiers in Systems Biology 5: 1544432

[127] Öztürk H, Özgür A, Ozkirimli E. 2018. DeepDTA: deep drug-target binding affinity prediction. Bioinformatics 34: i821-i829

[128] Karimi M, Wu D, Wang Z, Shen Y. 2019. DeepAffinity: interpretable deep learning of compound-protein affinity through unified recurrent and convolutional neural networks. Bioinformatics 35: 3329-3338

[129] Nguyen T, Le H, Quinn TP, Nguyen T, Le TD, et al. 2021. GraphDTA: predicting drug-target binding affinity with graph neural networks. Bioinformatics 37: 1140-1147

[130] Luo Y, Zhao X, Zhou J, Yang J, Zhang Y, et al. 2017. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nature Communications 8: 573

[131] Mohamed SK, Novacek V, Nounu A. 2020. Discovering protein drug targets using knowledge graph embeddings. Bioinformatics 36: 603-610

[132] Luo Y, Duan G, Zhao Q, Bi X, Wang J. 2024. DTKGIN: predicting drug-target interactions based on knowledge graph and intent graph. Methods 226: 21-27

[133] Zhang C, Zang T, Zhao T. 2024. KGE-UNIT: toward the unification of molecular interactions prediction based on knowledge graph and multi-task learning on drug discovery. Briefings in Bioinformatics 25: bbae043

[134] Zhao X, Wang Q, Zhang Y, He C, Yin M, et al. 2025. CBKG-DTI: multi-level knowledge distillation and biomedical knowledge graph for drug-target interaction prediction. IEEE Journal of Biomedical and Health Informatics 29: 2284-2296

[135] Mou J, Yan Y, Jiang B, Yang F, Pan Z, et al. 2026. LKE-DTA: predicting drug-target binding affinity with large language model representations and knowledge graph embeddings. Molecular Diversity 30: 2465-2478

[136] Ranjan A, Kumar H, Kumari D, Anand A, Misra R. 2023. Molecule generation toward target protein (SARS-CoV-2) using reinforcement learning-based graph neural network via knowledge graph . Network Modeling and Analysis in Health Informatics and Bioinformatics 12: 13

[137] Yu JL, Zhou C, Ning XL, Mou J, Meng FB, et al. 2025. Knowledge-guided diffusion model for 3D ligand-pharmacophore mapping. Nature Communications 16: 2269

[138] To VT, Van Nguyen PC, Truong GB, Phan TM, Phan TL, et al. 2025. KGG: knowledge-guided graph self-supervised learning to enhance molecular property predictions. Journal of Chemical Information and Modeling 65: 9443-9458

[139] Segall MD. 2012. Multi-parameter optimization: identifying high quality compounds with a balance of properties. Current Pharmaceutical Design 18: 1292-1310

[140] Chen XH, Ruan Y, Liu YG, Duan XY, Jiang F, et al. 2023. Transporter proteins knowledge graph construction and its application in drug development. Computational and Structural Biotechnology Journal 21: 2973-2984

[141] Verhellen J. 2022. Graph-based molecular Pareto optimisation. Chemical Science 13: 7526-7535

[142] Yi JC, Yang ZY, Zhao WT, Yang ZJ, Zhang XC, et al. 2024. ChemMORT: an automatic ADMET optimization platform using deep learning and multi-objective particle swarm optimization. Briefings in Bioinformatics 25: bbae008

[143] Paliwal S, de Giorgio A, Neil D, Michel JB, Lacoste AM. 2020. Preclinical validation of therapeutic targets predicted by tensor factorization on heterogeneous graphs. Scientific Reports 10: 18250

[144] Han S, Lee JE, Kang S, So M, Jin H, et al. 2024. Standigm ASK™: knowledge graph and artificial intelligence platform applied to target discovery in idiopathic pulmonary fibrosis. Briefings in Bioinformatics 25: bbae035

[145] Richardson P, Griffin I, Tucker C, Smith D, Oechsle O, et al. 2020. Baricitinib as potential treatment for 2019-nCoV acute respiratory disease. The Lancet 395: e30-e31

[146] Marconi VC, Ramanan AV, de Bono S, Kartman CE, Krishnan V, et al. 2021. Efficacy and safety of baricitinib for the treatment of hospitalised adults with COVID-19 (COV-BARRIER): a randomised, double-blind, parallel-group, placebo-controlled phase 3 trial. Lancet Respiratory Medicine 9: 1407-1418

[147] Kalil AC, Patterson TF, Mehta AK, Tomashek KM, Wolfe CR, et al. 2021. Baricitinib plus remdesivir for hospitalized adults with Covid-19. New England Journal of Medicine 384: 795-807

[148] Potter E, Reyes M, Naples J, Dal Pan G. 2025. FDA adverse event reporting system (FAERS) essentials: a guide to understanding, applying, and interpreting adverse event data reported to FAERS. Journal of Clinical Pharmacology & Therapeutics 118: 567-582

[149] Kuhn M, Letunic I, Jensen LJ, Bork P. 2016. The SIDER database of drugs and side effects. Nucleic Acids Research 44: D1075-D1079

[150] Davis AP, Wiegers TC, Johnson RJ, Sciaky D, Wiegers J, et al. 2023. Comparative toxicogenomics database (CTD): update 2023. Nucleic Acids Research 51: D1257-D1262

[151] Dix DJ, Houck KA, Martin MT, Richard AM, Setzer RW, et al. 2007. The ToxCast program for prioritizing toxicity testing of environmental chemicals. Toxicological Sciences 95: 5-12

[152] Ochoa D, Hercules A, Carmona M, Suveges D, Gonzalez-Uriarte A, et al. 2021. Open Targets Platform: supporting systematic drug-target identification and prioritisation. Nucleic Acids Research 49: D1302-D1310

[153] Bean DM, Wu H, Iqbal E, Dzahini O, Ibrahim ZM, et al. 2017. Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records. Scientific Reports 7: 16416

[154] Zhang F, Sun B, Diao X, Zhao W, Shu T. 2021. Prediction of adverse drug reactions based on knowledge graph embedding. BMC Medical Informatics and Decision Making 21: 38

[155] Yao J, Sun W, Jian Z, Wu Q, Wang X. 2022. Effective knowledge graph embeddings based on multidirectional semantics relations for polypharmacy side effects prediction. Bioinformatics 38: 2315-2322

[156] Xuan P, Cheng T, Cui H, Gu J, Jin Q, et al. 2025. Multi-knowledge graph and multi-view entity feature learning for predicting drug-related side effects. Journal of Chemical Information and Modeling 65: 5124-5138

[157] Yadav M, Ahlawat P, Singh V. 2025. A graph-based transformer neural network for multi-label ADR prediction. Arabian Journal for Science and Engineering 50: 7323-7341

[158] Venkataraman M, Chand Rao G, Madavareddi JK, Maddi SR. 2025. Leveraging machine learning models in evaluating ADMET properties for drug discovery and development: review article. ADMET and DMPK 13: 2772

[159] Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, et al. 2019. Drug repurposing: progress, challenges and recommendations. Nature Reviews Drug Discovery 18: 41-58

[160] Ashburn TT, Thor KB. 2004. Drug repositioning: identifying and developing new uses for existing drugs. Nature Reviews Drug Discovery 3: 673-683

[161] Gottlieb A, Stein GY, Ruppin E, Sharan R. 2011. PREDICT: a method for inferring novel drug indications with application to personalized medicine. Molecular Systems Biology 7: MSB201126

[162] Morselli Gysi D, do Valle Í, Zitnik M, Ameli A, Gan X, et al. 2021. Network medicine framework for identifying drug-repurposing opportunities for COVID-19. Proceedings of the National Academy of Sciences of the United States of America 118: e2025581118

[163] Boudin M, Diallo G, Drancé M, Mougin F. 2023. The OREGANO knowledge graph for computational drug repurposing. Scientific Data 10: 871

[164] Bang D, Lim S, Lee S, Kim S. 2023. Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers. Nature Communications 14: 3570

[165] Huang K, Chandak P, Wang Q, Havaldar S, Vaid A, et al. 2024. A foundation model for clinician-centered drug repurposing. Nature Medicine 30: 3601-3613

[166] Lou P, Fang A, Zhao W, Yao K, Yang Y, et al. 2023. Potential target discovery and drug repurposing for coronaviruses: study involving a knowledge graph-based approach. Journal of Medical Internet Research 25: e45225

[167] Zeng X, Song X, Ma T, Pan X, Zhou Y, et al. 2020. Repurpose open data to discover therapeutics for COVID-19 using deep learning. Journal of Proteome Research 19: 4624-4636

[168] Mehrabani M, Lakizadeh A, Siahpirani AF, Salimi M, Zare-Mirakabad F, et al. 2025. SynergyGraph: predicting cell line specific drug combination synergy scores using knowledge graph representation and hypergraph modeling. Scientific Reports 16: 3131

[169] Wang Q, Wu B, Xu M, Liu X, Mao Y, et al. 2026. HyperSynergyX: synergistic drug combination prediction via hypergraph modeling and knowledge graph-enhanced retrieval-augmented generation . IEEE Journal of Biomedical and Health Informatics 1-15

[170] Lin S, Chen W, Chen G, Zhou S, Wei DQ, et al. 2022. MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning . Journal of Cheminformatics 14: 81

[171] Wang X, Huang Y, Zhu H, Mao D, Zhu X, et al. 2026. Hgtsynergy: a transfer learning method for predicting anticancer synergistic drug combinations based on a drug-drug interaction heterogeneous graph. BMC Bioinformatics 27: 35

[172] Xu H, Lin J, Woicik A, Liu Z, Ma J, et al. 2025. Pisces: a multi-modal data augmentation approach for drug combination synergy prediction. Cell Genomics 5: 100892

[173] Yu Y, Huang K, Zhang C, Glass LM, Sun J, et al. 2021. SumGNN: multi-typed drug interaction prediction via efficient knowledge graph summarization . Bioinformatics 37: 2988-2995

[174] Hong Y, Luo P, Jin S, Liu X. 2022. LaGAT: link-aware graph attention network for drug-drug interaction prediction. Bioinformatics 38: 5406-5412

[175] Zhong J, Zhao H, Zhao Q, Wang J. 2024. A knowledge graph-based method for drug-drug interaction prediction with contrastive learning. IEEE/ACM Transactions on Computational Biology and Bioinformatics 21: 2485-2495

[176] Asada M, Miwa M, Sasaki Y. 2023. Integrating heterogeneous knowledge graphs into drug-drug interaction extraction from the literature. Bioinformatics 39: btac754

[177] Wang M, Wang H, Liu X, Ma X, Wang B. 2021. Drug-drug interaction predictions via knowledge graph and text embedding: instrument validation study. JMIR Medical Informatics 9: e28277

[178] Li Y, Liu JM, Li YC, You HR, You ZH, et al. 2026. KA-DDI: a knowledge-adaptive contrastive learning framework for drug-drug interaction prediction. Expert Systems with Applications 308: 131076

[179] Li D, Yang Y, Cui Z, Yin H, Hu P, et al. 2026. LLM-DDI: leveraging large language models for drug-drug interaction prediction on biomedical knowledge graph. IEEE Journal of Biomedical and Health Informatics 30: 773-781

[180] Putman TE, Schaper K, Matentzoglu N, Rubinetti VP, Alquaddoomi FS, et al. 2024. The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species. Nucleic Acids Research 52: D938-D949

[181] Santos A, Colaco AR, Nielsen AB, Niu L, Strauss M, et al. 2022. A knowledge graph to interpret clinical proteomics data. Nature Biotechnology 40: 692-702

[182] Fang C, Arango Argoty GA, Kagiampakis I, Khalid MH, Jacob E, et al. 2024. Integrating knowledge graphs into machine learning models for survival prediction and biomarker discovery in patients with non-small-cell lung cancer. Journal of Translational Medicine 22: 726

[183] Ping P, Watson K, Han J, Bui A. 2017. Individualized knowledge graph: a viable informatics path to precision medicine. Circulation Research 120: 1078-1080

[184] Yu Y, Wang Y, Xia Z, Zhang X, Jin K, et al. 2019. PreMedKB: an integrated precision medicine knowledgebase for interpreting relationships between diseases, genes, variants and drugs. Nucleic Acids Research 47: D1090-D1101

[185] Yun CH, Mengwasser KE, Toms AV, Woo MS, Greulich H, et al. 2008. The T790M mutation in EGFR kinase causes drug resistance by increasing the affinity for ATP. Proceedings of the National Academy of Sciences of the United States of America 105: 2070-2075

[186] Hatcher JM, Bahcall M, Choi HG, Gao Y, Sim T, et al. 2015. Discovery of inhibitors that overcome the G1202R anaplastic lymphoma kinase resistance mutation. Journal of Medicinal Chemistry 58: 9296-9308

[187] Gibbons DL, Pricl S, Kantarjian H, Cortes J, Quintás-Cardama A. 2012. The rise and fall of gatekeeper mutations? The BCR-ABL1 T315I paradigm . Cancer 118: 293-299

[188] Naseem U, Thapa S, Zhang Q, Hu L, Masood A, et al. 2023. Reducing knowledge noise for improved semantic analysis in biomedical natural language processing applications. Proceedings of the 5th Clinical Natural Language Processing Workshop. Toronto, Canada, 2023 . Stroudsburg, PA, USA: Association for Computational Linguistics. pp. 272-277 doi: 10.18653/v1/2023.clinicalnlp-1.32

[189] Zhu Y. Thesis proposal: uncertainty in knowledge graph embeddings. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 4: Student Research Workshop), Albuquerque, USA: Association for Computational Linguistics. pp. 40-47 doi: 10.18653/v1/2025.naacl-srw.4

[190] Lin P, Song Q, Wu Y. 2018. Fact checking in knowledge graphs with ontological subgraph patterns. Data Science and Engineering 3: 341-358

[191] Wang R, Zhuang C. 2025. Graph neural networks driven acceleration in drug discovery. Acta Pharmaceutica Sinica B 15: 6163-6177

[192] Liu C, Jiang D, Cai Y, Li H. 2026. A review of deep learning and large language models for cold start problem in recommender systems. WIREs Data Mining and Knowledge Discovery 16: e70068

[193] Shi Y, Xu W, Hu P. 2025. Out of distribution learning in bioinformatics: advancements and challenges. Briefings in Bioinformatics 26: bbaf294

[194] Pernice WM, Doron M, Quach A, Pratapa A, Kenjeyev S, et al. 2023. Out of distribution generalization via interventional style transfer in single-cell microscopy . 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Vancouver, BC, Canada, 2023. US: IEEE. pp. 4326-4335 doi: 10.1109/cvprw59228.2023.00455

[195] Li H, Wang X, Zhang Z, Zhu W. 2025. OOD-GNN: out-of-distribution generalized graph neural network: (extended abstract). 2024 IEEE 40th International Conference on Data Engineering (ICDE). Utrecht, Netherlands, 2024. US: IEEE. pp. 5681-5682 doi: 10.1109/ICDE60146.2024.00472

[196] Negro A, Montagna F, Teng MN, Neal T, Thomas S, et al. 2023. Analysis of the evolution of COVID-19 disease understanding through temporal knowledge graphs. Frontiers in Research Metrics and Analytics 8: 1204801

[197] Dong L, Zhao D, Zhang X, Li X, Kang X, et al. 2023. Anchors-based incremental embedding for growing knowledge graphs. IEEE Transactions on Knowledge and Data Engineering 35: 3458-3470

[198] McMahan HB, Moore E, Ramage D, Hampson S, Agüera y Arcas B. 2016. Communication-efficient learning of deep networks from decentralized data. arXiv 00: 1602.05629

[199] Chen MY, Zhang W, Yuan ZG, Jia YT, Chen HJ. 2021. FedE: embedding knowledge graphs in federated setting. Proceedings of the 10th International Joint Conference on Knowledge Graphs, Virtual Event, Thailand, 2021. New York, NY, USA: Association for Computing Machinery. pp. 80-88 doi: 10.1145/3502223.3502233

[200] Yang M, Huang D, Wan W, Jin M. 2025. Federated learning for privacy-preserving medical data sharing in drug development. Applied and Computational Engineering 108: 7-13