Intelligence on the Graph: Graph Neural Networks for Mechanistic Drug Target Discovery

Jing Chen; Nini Fan; Yuqing Lu; Jianhua Yang; Wenchao Song; Haiyang Sheng; Yinfeng Yang; Shengxi Chen; Jinghui Wang

doi:10.1016/j.jpha.2025.101508

Article Contents

Article Navigation > Journal of Pharmaceutical Analysis > 2025 > Accepted Manu

Jing Chen, Nini Fan, Yuqing Lu, Jianhua Yang, Wenchao Song, Haiyang Sheng, Yinfeng Yang, Shengxi Chen, Jinghui Wang. Intelligence on the Graph: Graph Neural Networks for Mechanistic Drug Target Discovery[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2025.101508

Citation:

Jing Chen, Nini Fan, Yuqing Lu, Jianhua Yang, Wenchao Song, Haiyang Sheng, Yinfeng Yang, Shengxi Chen, Jinghui Wang. Intelligence on the Graph: Graph Neural Networks for Mechanistic Drug Target Discovery[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2025.101508

Citation:

Jing Chen, Nini Fan, Yuqing Lu, Jianhua Yang, Wenchao Song, Haiyang Sheng, Yinfeng Yang, Shengxi Chen, Jinghui Wang. Intelligence on the Graph: Graph Neural Networks for Mechanistic Drug Target Discovery[J]. Journal of Pharmaceutical Analysis. doi: 10.1016/j.jpha.2025.101508

PDF( 2146 KB)

Intelligence on the Graph: Graph Neural Networks for Mechanistic Drug Target Discovery

doi: 10.1016/j.jpha.2025.101508

1. School of Medical Informatics Engineering, Anhui University of Chinese Medicine, Hefei, 230012, China;
2. Global Biometrics and Data Sciences, Bristol Myers Squibb, Lawrenceville, New Jersey, USA;
3. Center for BioEnergetics, Biodesign Institute, Arizona State University, Arizona, USA;
4. School of Integrated Chinese and Western Medicine, Anhui University of Chinese Medicine, Hefei, 230012, China;
5. Anhui Provincial Key Laboratory of Chinese Medicinal Formula, Hefei, 230012, China

Funds:

Thanks for the Outstanding Youth Research Project of Anhui, Department of Education (Grant No.: 2022AH020042), the Major Scientific Research Project of Universities in Anhui province (Grant No.: 2024AH040146), the Anhui Province quality projects (Grant No.: 2023sdxx027) and the Training Action Project of Anhui Provincial Education Department (Grant Nos.: JNFX2023020 and JWFX2025017).

Received Date: Aug. 01, 2025
Accepted Date: Dec. 01, 2025
Rev Recd Date: Nov. 27, 2025
Available Online: Dec. 04, 2025

Abstract

Abstract

Drug discovery is increasingly challenged by rising costs, long development cycles and high attrition rates, with accurate target identification remaining a critical bottleneck. Although artificial intelligence (AI) has demonstrated transformative potential, the systematic application of graph neural networks (GNNs) to drug target discovery remains underexplored. To address this gap, this paper provides a comprehensive and structured analysis of recent advances in GNN-based methods for drug-target interaction (DTI) and drug-target affinity (DTA) prediction. We dissect the methodological foundations of representative architectures including graph convolutional networks (GCNs), graph attention networks (GATs) and graph autoencoders (GAEs), and compare their mechanisms, advantages and applicable scenarios in modeling complex molecular and biological systems. Also, we synthesize frontier paradigms such as multimodal data fusion, high-order graph reasoning and dynamic GNNs, which enable the capture of atom-residue interactions, multi-target coordination mechanisms and cross-scale biological features. By systematically mapping methodological innovations to biological applications, this paper offers both theoretical guidance and translational insights. The key contributions of this paper include: (1) establishing a comparative framework that clarifies when and how different GNNs architectures can be applied in drug target discovery; (2) integrating cutting-edge paradigms rarely addressed in prior reviews, such as multimodal fusion and high-order graph modeling; and (3) highlighting representative case studies that bridge algorithmic innovation with practical drug discovery outcomes. Collectively, this work provides an authoritative and forward-looking reference, promoting the development of AI-driven, efficient and interpretable drug discovery pipelines.
- Artificial intelligence,
- Graph Neural Network,
- Graph Convolutional Network,
- Drug target interaction,
- Drug target affinity

FullText(HTML)

References(144)

References

[1]	S.M. Paul, D.S. Mytelka, C.T. Dunwiddie, et al., How to improve R&D productivity: The pharmaceutical industry’s grand challenge, Nat. Rev. Drug Discov. 9 (2010) 203-214.[PubMed].
[2]	J. Vamathevan, D. Clark, P. Czodrowski, et al., Applications of machine learning in drug discovery and development, Nat. Rev. Drug Discov. 18 (2019) 463-477.[PubMed].
[3]	A. Mullard, New drugs cost US$2.6 billion to develop, Nat. Rev. Drug Discov. 13 (2014), 877.[LinkOut].
[4]	D. Sun, W. Gao, H. Hu, et al., Why 90% of clinical drug development fails and how to improve it?Acta Pharm. Sin. B 12 (2022) 3049-3062.[PubMed].
[5]	A. Mullard, The drug-maker’s guide to the galaxy, Nature 549 (2017) 445-447.[PubMed].
[6]	J.M. Reichert, Trends in development and approval times for new therapeutics in the United States, Nat. Rev. Drug Discov. 2 (2003) 695-702.[PubMed].
[7]	Y. You, X. Lai, Y. Pan, et al., Artificial intelligence in cancer target identification and drug discovery, Signal Transduct. Target. Ther. 7 (2022), 156.[PubMed].
[8]	A.V. Sadybekov, V. Katritch, Computational approaches streamlining drug discovery, Nature 616 (2023) 673-685.[PubMed].
[9]	G. Sliwoski, S. Kothiwale, J. Meiler, et al., Computational methods in drug discovery, Pharmacol. Rev. 66 (2013) 334-395.[PubMed].
[10]	H. Chen, O. Engkvist, Y. Wang, et al., The rise of deep learning in drug discovery, Drug Discov. Today 23 (2018) 1241-1250.[PubMed].
[11]	S. Dara, S. Dhamercherla, S.S. Jadav, et al., Machine learning in drug discovery: A review, Artif. Intell. Rev. 55 (2022) 1947-1999.[PubMed].
[12]	S. Vatansever, A. Schlessinger, D. Wacker, et al., Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions, Med. Res. Rev. 41 (2021) 1427-1473.[PubMed].
[13]	C.J. Henrich, J.A. Beutler, Matching the power of high throughput screening to the chemical diversity of natural products, Nat. Prod. Rep. 30 (2013) 1284-1298.[PubMed].
[14]	A. Schneuing, C. Harris, Y. Du, et al., Structure-based drug design with equivariant diffusion models, Nat. Comput. Sci. 4 (2024) 899-909.[PubMed].
[15]	W. Zheng, N. Thorne, J.C. McKew, Phenotypic screens as a renewed approach for drug discovery, Drug Discov. Today 18 (2013) 1067-1073.[PubMed].
[16]	J.L. Medina-Franco, M.A. Giulianotti, G.S. Welmaker, et al., Shifting from the single to the multitarget paradigm in drug discovery, Drug Discov. Today 18 (2013) 495-501.[PubMed].
[17]	F. Zhong, X. Wu, R. Yang, et al., Drug target inference by mining transcriptional data using a novel graph convolutional network framework, Protein Cell 13 (2022) 281-301.[PubMed].
[18]	Z. Lu, G. Song, H. Zhu, et al., DTIAM: A unified framework for predicting drug-target interactions, binding affinities and drug mechanisms, Nat. Commun. 16 (2025), 2548.[PubMed].
[19]	P. Bai, F. Miljkovic, B. John, et al., Interpretable bilinear attention network with domain adaptation improves drug-target prediction, Nat. Mach. Intell. 5 (2023) 126-136.[LinkOut].
[20]	Z. Zhang, L. Chen, F. Zhong, et al., Graph neural network approaches for drug-target interactions, Curr. Opin. Struct. Biol. 73 (2022), 102327.[PubMed].
[21]	G. Corso, H. Stark, S. Jegelka, et al., Graph neural networks, Nat. Rev. Meth. Primers 4 (2024), 17.[LinkOut].
[22]	H. Zhang, B. Wu, X. Yuan, et al., Trustworthy graph neural networks: Aspects, methods, and trends, Proc. IEEE 112 (2024) 97-139.[LinkOut].
[23]	T.N. Kipf, M. Welling, Semi-supervised classification with graph convolutional networks, arXiv. 2016. https://arxiv.org/abs/1609.02907.
[24]	M.M. Bronstein, J. Bruna, Y. LeCun, et al., Geometric deep learning: Going beyond euclidean data, IEEE Signal Process. Mag. 34 (2017) 18-42.[LinkOut].
[25]	S. Zhang, H. Tong, J. Xu, et al., Graph convolutional networks: A comprehensive review, Comput Soc Netw 6 (2019), 11.[PubMed].
[26]	Z. Wu, S. Pan, F. Chen, et al., A comprehensive survey on graph neural networks, IEEE Trans. Neural Netw. Learn. Syst. 32 (2021) 4-24.[LinkOut].
[27]	Z Zhang, P Cui, W Zhu, Deep learning on graphs: A survey, IEEE Trans. Knowl. Data Eng. 34 (2022) 249-270.[LinkOut].
[28]	M. Gori, G. Monfardini, F. Scarselli, A new model for learning in graph domains, Proceedings of 2005 IEEE International Joint Conference on Neural Networks, 2005. July 31 - August 4, 2005, Montreal, QC, Canada. IEEE, (2005) 729–734.[LinkOut]
[29]	S. Brin, L. Page, The anatomy of a large-scale hypertextual Web search engine, Comput. Netw. ISDN Syst. 30 (1998) 107-117.[LinkOut].
[30]	J. Bruna, W. Zaremba, A. Szlam, et al., Spectral networks and locally connected networks on graphs, arXiv. 2013. https://arxiv.org/abs/1312.6203.
[31]	M. Defferrard, X. Bresson, P. Vandergheynst, Convolutional neural networks on graphs with fast localized spectral filtering, Neural Information Processing Systems., (1997)[LinkOut]
[32]	W. Hamilton, Z. Ying , J. Leskovec, Proceedings of the Thirty-First Advances in neural information processing systems, Dec 4–9, 2017, California, America, 2017, pp. 671–681.
[33]	L. Zhang, C. Wang, Y. Zhang, et al., GPCNDTA: Prediction of drug-target binding affinity through cross-attention networks augmented with graph features and pharmacophores, Comput. Biol. Med. 166 (2023), 107512.[PubMed].
[34]	M. Sun, S. Zhao, C. Gilvary, et al., Graph convolutional networks for computational drug development and discovery, Brief. Bioinform. 21 (2020) 919-935.[PubMed].
[35]	W. Torng, R.B. Altman, Graph convolutional neural networks for predicting drug-target interactions, J Chem Inf Model 59 (2019) 4131-4149.[PubMed].
[36]	Q. Liu, Z. Hu, R. Jiang, et al., DeepCDR: A hybrid graph convolutional network for predicting cancer drug response, Bioinformatics 36 (2020) i911-i918.[PubMed].
[37]	D. Bahdanau, K. Cho, Y. Bengio, Neural machine translation by jointly learning to align and translate, arXiv. 2014. https://arxiv.org/abs/1409.0473.
[38]	A. Vaswani, N. Shazeer, N. Parmar, et al., Proceedings of the Thirty-First Advances in neural information processing systems, Dec 4–9, 2017, California, America, 2017, pp. 3058–3068.
[39]	P. Velickovic, G. Cucurull, A. Casanova, et al., Graph attention networks, arXiv. 2017. https://arxiv.org/abs/1710.10903.
[40]	S. Brody, U. Alon, E. Yahav, How attentive are graph attention networks?, arXiv. 2021. https://arxiv.org/abs/2105.14491.
[41]	S. Bai, F. Zhang, P.H.S. Torr, Hypergraph convolution and hypergraph attention, Pattern Recognit. 110 (2021), 107637.[LinkOut].
[42]	Y. Weng, X. Chen, L. Chen, et al., GAIN: Graph attention & interaction network for inductive semi-supervised learning over large-scale graphs, IEEE Trans. Knowl. Data Eng. 34 (2022) 4257-4269.[LinkOut].
[43]	Z. Xiong, D. Wang, X. Liu, et al., Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism, J. Med. Chem. 63 (2020) 8749-8760.[PubMed].
[44]	Q. Lv, G. Chen, Z. Yang, et al., Meta learning with graph attention networks for low-data drug discovery, IEEE Trans. Neural Netw. Learn. Syst. 35 (2024) 11218-11230.[LinkOut].
[45]	X. Su, L. Hu, Z. You, et al., Attention-based knowledge graph representation learning for predicting drug-drug interactions, Brief. Bioinform. 23 (2022), bbac140.[PubMed].
[46]	S. Huang, M. Wang, X. Zheng, et al., Hierarchical and dynamic graph attention network for drug-disease association prediction, IEEE J. Biomed. Health Inform. 28 (2024) 2416-2427.[LinkOut].
[47]	D. Charte, F. Charte, S. Garcia, et al., A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines, Inf. Fusion 44 (2018) 78-96.[LinkOut].
[48]	P. Vincent, H. Larochelle, I. Lajoie, et al., Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion, Mach. Learn. Res. 11 (2010), 3371-3408.
[49]	P. Vincent, H. Larochelle, Y. Bengio, et al., Extracting and composing robust features with denoising autoencoders, Proceedings of the 25th International Conference on Machine Learning - ICML '08. July 5-9, 2008. Helsinki, Finland. ACM, (2008): 1096-1103.[LinkOut]
[50]	C Zhou, R.C. Paffenroth, Anomaly detection with robust deep autoencoders, Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Halifax NS Canada. ACM, (2017): 665-674.[LinkOut]
[51]	J. Mehta, A. Majumdar, RODEO: Robust DE-aliasing autoencOder for real-time medical image reconstruction, Pattern Recognit. 63 (2017) 499-510.[LinkOut].
[52]	D Wang, P Cui, W Zhu, Structural deep network embedding, Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco California USA. ACM, (2016): 1225-1234.[LinkOut]
[53]	T.N. Kipf, M. Welling, Variational graph auto-encoders, arXiv. 2016. https://arxiv.org/abs/1611.07308.
[54]	A. Bojchevski, S. Gunnemann, Deep Gaussian embedding of graphs: Unsupervised inductive learning via ranking, ArXiv Mach. Learn., (2022)[LinkOut].
[55]	S. Pan, R. Hu, G. Long, et al., Adversarially regularized graph autoencoder for graph embedding, arXiv. 2018. https://arxiv.org/abs/1802.04407.
[56]	W Chiang, X Liu, S Si, et al., Cluster-GCN: An efficient algorithm for training deep and large graph convolutional networks, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Anchorage AK USA. ACM, (2019): 257-266.[LinkOut]
[57]	C. Sun, P. Xuan, T. Zhang, et al., Graph convolutional autoencoder and generative adversarial network-based method for predicting drug-target interactions, IEEE/ACM Trans. Comput. Biol. Bioinform. 19 (2022) 455-464.[PubMed].
[58]	Y. Wang, Y. Gao, J. Wang, et al., MSGCA: Drug-disease associations prediction based on multi-similarities graph convolutional autoencoder, IEEE J. Biomed. Health Inform. 27 (2023) 3686-3694.[LinkOut].
[59]	Y. Dai, C. Guo, W. Guo, et al., Drug-drug interaction prediction with Wasserstein Adversarial Autoencoder-based knowledge graph embeddings, Brief. Bioinform. 22 (2021), bbaa256.[PubMed].
[60]	P. Xuan, M. Fan, H. Cui, et al., GVDTI: Graph convolutional and variational autoencoders with attribute-level attention for drug-protein interaction prediction, Brief. Bioinform. 23 (2022), bbab453.[PubMed].
[61]	A. L’Heureux, K. Grolinger, H.F. Elyamany, et al., Machine learning with big data: Challenges and approaches, IEEE Access 5 (2017) 7776-7797.[LinkOut].
[62]	M.J. Keiser, V. Setola, J.J. Irwin, et al., Predicting new molecular targets for known drugs, Nature 462 (2009) 175-181.[LinkOut].
[63]	U. Veleiro, J. de la Fuente, G. Serrano, et al., GeNNius: An ultrafast drug-target interaction inference method based on graph neural networks, Bioinformatics 40 (2024), btad774.[PubMed].
[64]	T. Zhao, Y. Hu, L.R. Valsdottir, et al., Identifying drug-target interactions based on graph convolutional network and deep neural network, Brief. Bioinform. 22 (2021) 2141-2150.[PubMed].
[65]	Y. Yamanishi, M. Araki, A. Gutteridge, et al., Prediction of drug-target interaction networks from the integration of chemical and genomic spaces, Bioinformatics 24 (2008) i232-i240.[PubMed].
[66]	G. Alanis-Lobato, M.A. Andrade-Navarro, M.H. Schaefer, HIPPIE v2.0: Enhancing meaningfulness and reliability of protein-protein interaction networks, Nucleic Acids Res. 45 (2017) D408-D414.[PubMed].
[67]	D.S. Wishart, Y.D. Feunang, A.C. Guo, et al., DrugBank 5.0: A major update to the DrugBank database for 2018, Nucleic Acids Res. 46 (2018) D1074-D1082.[PubMed].
[68]	L. Jiang, J. Sun, Y. Wang, et al., Identifying drug-target interactions via heterogeneous graph attention networks combined with cross-modal similarities, Brief. Bioinform. 23 (2022), bbac016.[PubMed].
[69]	Y. Luo, X. Zhao, J. Zhou, et al., A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information, Nat. Commun. 8 (2017), 573.[PubMed].
[70]	M. Li, X. Cai, S. Xu, et al., Metapath-aggregated heterogeneous graph neural network for drug-target interaction prediction, Brief. Bioinform. 24 (2023), bbac578.[PubMed].
[71]	U. Consortium, UniProt: A worldwide hub of protein knowledge, Nucleic Acids Res. 47 (2019) D506-D515.[PubMed].
[72]	Y. Su, Z. Hu, F. Wang, et al., AMGDTI: Drug-target interaction prediction based on adaptive meta-graph learning in heterogeneous network, Brief. Bioinform. 25 (2023), bbad474.[PubMed].
[73]	Y. Zheng, H. Peng, X. Zhang, et al., Predicting drug targets from heterogeneous spaces using anchor graph hashing and ensemble learning, 2018 International Joint Conference on Neural Networks (IJCNN). July 8-13, 2018, Rio de Janeiro, Brazil. IEEE, (2018) 1–7.[LinkOut]
[74]	K. Shao, Y. Zhang, Y. Wen, et al., DTI-HETA: Prediction of drug-target interactions based on GCN and GAT on heterogeneous graph, Brief. Bioinform. 23 (2022), bbac109.[PubMed].
[75]	S. He, Y. Wen, X. Yang, et al., PIMD: An integrative approach for drug repositioning using multiple characterization fusion, Genomics Proteomics Bioinformatics 18 (2020) 565-581.[PubMed].
[76]	Z. Liu, Q. Chen, W. Lan, et al., SSLDTI: A novel method for drug-target interaction prediction based on self-supervised learning, Artif. Intell. Med. 149 (2024), 102778.[PubMed].
[77]	F. Wan, L. Hong, A. Xiao, et al., NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions, Bioinformatics 35 (2019) 104-111.[PubMed].
[78]	X. Zeng, S. Zhu, X. Liu, et al., deepDR: A network-based deep learning approach to in silico drug repositioning, Bioinformatics 35 (2019) 5191-5198.[PubMed].
[79]	P. Xu, Z. Wei, C. Li, et al., Drug-target prediction based on dynamic heterogeneous graph convolutional network, IEEE J. Biomed. Health Inform. 28 (2024) 6997-7005.[LinkOut].
[80]	Y. Jin, J. Lu, R. Shi, et al., EmbedDTI: Enhancing the molecular representations via sequence embedding and graph convolutional network for the prediction of drug-target interaction, Biomolecules 11 (2021), 1783.[PubMed].
[81]	M.I. Davis, J.P. Hunt, S. Herrgard, et al., Comprehensive analysis of kinase inhibitor selectivity, Nat. Biotechnol. 29 (2011) 1046-1051.[PubMed].
[82]	J. Tang, A. Szwajda, S. Shakyawar, et al., Making sense of large-scale kinase inhibitor bioactivity data sets: A comparative and integrative analysis, J. Chem. Inf. Model. 54 (2014) 735-743.[PubMed].
[83]	Q. Kim, J.H. Ko, S. Kim, et al., Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction, Bioinformatics 37 (2021) 3428-3435.[PubMed].
[84]	P. Battaglia, R. Pascanu, M. Lai, et al., Proceedings of the Thirtieth Advances in neural information processing systems, Dec 5–11, 2016, Barcelona, Spain, 2016, pp. 2244–2252.
[85]	K.Y. Gao, A. Fokoue, H. Luo, et al., Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, July 13–19, 2018, Stockholm, Sweden, 2018, pp. 3371–3377.
[86]	H. Liu, J. Sun, J. Guan, et al., Improving compound-protein interaction prediction by building up highly credible negative samples, Bioinformatics 31 (2015) i221-i229.[PubMed].
[87]	M. Tsubaki, K. Tomii, S. Jun, Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences, Bioinformatics 35 (2019) 309-318.[PubMed].
[88]	D.S. Wishart, C. Knox, A.C. Guo, et al., DrugBank: A knowledgebase for drugs, drug actions and drug targets, Nucleic Acids Res 36 (2008) D901-D906.[PubMed].
[89]	S. Gunther, M. Kuhn, M. Dunkel, et al., SuperTarget and Matador: Resources for exploring drug-target relationships, Nucleic Acids Res. 36 (2008) D919-D922.[PubMed].
[90]	L. Chen, X. Tan, D. Wang, et al., TransformerCPI: Improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments, Bioinformatics 36 (2020) 4406-4414.[PubMed].
[91]	R. Ying, D. Bourgeois, J. You, et al., GNNExplainer: Generating explanations for graph neural networks, Adv Neural Inf Process. Syst 32 (2019) 9240-9251.[PubMed].
[92]	H. Wang, F. Huang, Z. Xiong, et al., A heterogeneous network-based method with attentive meta-path extraction for predicting drug-target interactions, Brief. Bioinform. 23 (2022), bbac184.[PubMed].
[93]	Y. Li, G. Qiao, K. Wang, et al., Drug-target interaction predication via multi-channel graph neural networks, Brief. Bioinform. 23 (2022), bbab346.[PubMed].
[94]	C. Knox, V. Law, T. Jewison, et al., DrugBank 3.0: A comprehensive resource for ‘omics’ research on drugs, Nucleic Acids Res. 39 (2011) D1035-D1041.[PubMed].
[95]	T.S. Keshava Prasad, R. Goel, K. Kandasamy, et al., Human protein reference database: 2009 update, Nucleic Acids Res 37 (2009) D767-D772.[PubMed].
[96]	S. Wang, P. Shan, Y. Zhao, et al., GanDTI: A multi-task neural network for drug-target interaction prediction, Comput. Biol. Chem. 92 (2021), 107476.[LinkOut].
[97]	Y. Zhu, C. Ning, N. Zhang, et al., GSRF-DTI: A framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph, BMC Biol. 22 (2024), 156.[PubMed].
[98]	D.S. Wishart, C. Knox, A.C. Guo, et al., DrugBank: A comprehensive resource for in silico drug discovery and exploration, Nucleic Acids Res. 34 (2006) D668-D672.[PubMed].
[99]	M. Zitnik, R. Sosic, J. Leskovec, BioSNAP Datasets: Stanford biomedical network dataset collection. https://snap.stanford.edu/biodata. (Accessed 1 May 2018).
[100]	J. Hu, W. Yu, C. Pang, et al., DrugormerDTI: Drug Graphormer for drug-target interaction prediction, Comput. Biol. Med. 161 (2023), 106946.[PubMed].
[101]	Z. Yang, W. Zhong, L. Zhao, et al., MGraphDTA: Deep multiscale graph neural network for explainable drug-target binding affinity prediction, Chem. Sci. 13 (2022) 816-833.[LinkOut].
[102]	J.T. Metz, E.F. Johnson, N.B. Soni, et al., Navigating the kinome, Nat Chem Biol 7 (2011) 200-202.[PubMed].
[103]	M. Jiang, S. Wang, S. Zhang, et al., Sequence-based drug-target affinity prediction using weighted graph neural networks, BMC Genomics 23 (2022), 449.[PubMed].
[104]	M.M. Mysinger, M. Carchia, J.J. Irwin, et al., Directory of useful decoys, enhanced (DUD-E): Better ligands and decoys for better benchmarking, J. Med. Chem. 55 (2012) 6582-6594.[PubMed].
[105]	M. Jiang, Z. Li, S. Zhang, et al., Drug-target affinity prediction using graph neural network and contact maps, RSC Adv. 10 (2020) 20701-20712.[PubMed].
[106]	J. Liao, H. Chen, L. Wei, et al., GSAML-DTA: An interpretable drug-target binding affinity prediction model based on graph neural networks with self-attention mechanism and mutual information, Comput. Biol. Med. 150 (2022), 106145.[PubMed].
[107]	X. Zeng, K. Zhong, B. Jiang, et al., Fusing sequence and structural knowledge by heterogeneous models to accurately and interpretively predict drug-target affinity, Molecules 28 (2023), 8005.[PubMed].
[108]	R. Wang, X. Fang, Y. Lu, et al., The PDBbind database: Methodologies and updates, J Med Chem 48 (2005) 4111-4119.[PubMed].
[109]	P. Zhang, Z. Wei, C. Che, et al., DeepMGT-DTI: Transformer network incorporating multilayer graph information for Drug-Target interaction prediction, Comput. Biol. Med. 142 (2022), 105214.[PubMed].
[110]	C. Tang, C. Zhong, D. Chen, et al., Drug-target interactions prediction using marginalized denoising model on heterogeneous networks, BMC Bioinformatics 21 (2020), 330.[PubMed].
[111]	M. Kanehisa, S. Goto, M. Hattori, et al., From genomics to chemical genomics: New developments in KEGG, Nucleic Acids Res. 34 (2006) D354-D357.[PubMed].
[112]	Y. Wang, S.H. Bryant, T. Cheng, et al., PubChem BioAssay: 2017 update, Nucleic Acids Res. 45 (2017) D955-D963.[PubMed].
[113]	T. Nguyen, H. Le, T.P. Quinn, et al., GraphDTA: Predicting drug-target binding affinity with graph neural networks, Bioinformatics 37 (2021) 1140-1147.[PubMed].
[114]	S. Zhang, M. Jiang, S. Wang, et al., SAG-DTA: Prediction of drug-target affinity using self-attention graph network, Int J Mol Sci 22 (2021), 8993.[PubMed].
[115]	X. Wang, Y. Liu, F. Lu, et al., Dipeptide frequency of word frequency and graph convolutional networks for DTA prediction, Front. Bioeng. Biotechnol. 8 (2020), 267.[PubMed].
[116]	Y. Liu, X. Xia, Y. Gong, et al., SSR-DTA: Substructure-aware multi-layer graph neural networks for drug-target binding affinity prediction, Artif. Intell. Med. 157 (2024), 102983.[PubMed].
[117]	H. He, G. Chen, C.Y. Chen, NHGNN-DTA: A node-adaptive hybrid graph neural network for interpretable drug-target binding affinity prediction, Bioinformatics 39 (2023), btad355.[PubMed].
[118]	S. Wang, X. Song, Y. Zhang, et al., MSGNN-DTA: Multi-scale topological feature fusion based on graph neural networks for drug-target binding affinity prediction, Int J Mol Sci 24 (2023), 8326.[PubMed].
[119]	X. Jiang, R. Zhu, P. Ji, et al., Co-embedding of nodes and edges with graph neural networks, IEEE Trans. Pattern Anal. Mach. Intell. 45 (2023) 7075-7086.[LinkOut].
[120]	K. Wang, M. Li, Fusion-based deep learning architecture for detecting drug-target binding affinity using target and drug sequence and structure, IEEE J. Biomed. Health Inform. 27 (2023) 6112-6120.[PubMed].
[121]	A.S. Rifaioglu, R. Cetin Atalay, D. Cansen Kahraman, et al., MDeePred: Novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery, Bioinformatics 37 (2021) 693-704.[PubMed].
[122]	Q. Feng, E. Dueva, A. Cherkasov, et al., Padme: A deep learning-based framework for drug-target interaction prediction, arXiv. 2018. https://arxiv.org/abs/1807.09741.
[123]	X Tang, X Lei, Y Zhang, Prediction of drug-target affinity using attention neural network, Int. J. Mol. Sci. 25 (2024), 5126.[LinkOut].
[124]	R.A. Rossi, R Zhou, N.K. Ahmed, Deep inductive graph representation learning, IEEE Trans. Knowl. Data Eng. 32 (2020) 438-452.[LinkOut].
[125]	R. Wang, X. Fang, Y. Lu, et al., The PDBbind database: Collection of binding affinities for protein-ligand complexes with known three-dimensional structures, J. Med. Chem. 47 (2004) 2977-2980.[PubMed].
[126]	M. Su, Q. Yang, Y. Du, et al., Comparative assessment of scoring functions: The CASF-2016 update, J Chem Inf Model 59 (2019) 895-913.[PubMed].
[127]	Z. Zhu, Z. Yao, X. Zheng, et al., Drug-target affinity prediction method based on multi-scale information interaction and graph optimization, Comput. Biol. Med. 167 (2023), 107621.[PubMed].
[128]	T. Baltrusaitis, C. Ahuja, L.P. Morency, Multimodal machine learning: A survey and taxonomy, IEEE Trans. Pattern Anal. Mach. Intell. 41 (2019) 423-443.[LinkOut].
[129]	X. Xia, C. Zhu, F. Zhong, et al., MDTips: A multimodal-data-based drug-target interaction prediction system fusing knowledge, gene expression profile, and structural data, Bioinformatics 39 (2023), btad411.[PubMed].
[130]	S.A. Danziger, J. Zeng, Y. Wang, et al., Choosing where to look next in a mutation sequence space: Active Learning of informative p53 cancer rescue mutants, Bioinformatics 23 (2007) i104-i114.[PubMed].
[131]	C. Morris, M. Ritzert, M. Fey, et al., Weisfeiler and leman go neural: Higher-order graph neural networks, Proc. AAAI Conf. Artif. Intell. 33 (2019) 4602-4609.[LinkOut].
[132]	Z Zhu, Z Yao, G Qi, et al., Associative learning mechanism for drug-target interaction prediction, CAAI Trans. Intell. Technol. 8 (2023) 1558-1577.[LinkOut].
[133]	J. Deng, Z. Yang, I. Ojima, et al., Artificial intelligence in drug discovery: Applications and techniques, Brief. Bioinform. 23 (2022), bbab430.[PubMed].
[134]	H. Ozturk, A. Ozgur, E. Ozkirimli, DeepDTA: Deep drug-target binding affinity prediction, Bioinformatics 34 (2018) i821-i829.[PubMed].
[135]	L. Wu, H. Lin, C. Tan, et al., Self-supervised learning on graphs: Contrastive, generative, or predictive, IEEE Trans. Knowl. Data Eng. 35 (2023) 4216-4235.[LinkOut].
[136]	Z. Zhu, Y. Ding, G. Qi, et al., Drug-target affinity prediction using rotary encoding and information retention mechanisms, Eng. Appl. Artif. Intell. 147 (2025), 110239.[LinkOut].
[137]	Z. Zhu, X. Zheng, G. Qi, et al., Drug-target binding affinity prediction model based on multi-scale diffusion and interactive learning, Expert Syst. Appl. 255 (2024), 124647.[LinkOut].
[138]	M. Najm, C.A. Azencott, B. Playe, et al., Drug target identification with machine learning: How to choose negative examples, Int J Mol Sci 22 (2021), 5118.[PubMed].
[139]	L. Zhou, Z. Li, J. Yang, et al., Revealing drug-target interactions with computational models and algorithms, Molecules 24 (2019), 1714.[PubMed].
[140]	L. Yu, W. Qiu, W. Lin, et al., HGDTI: Predicting drug-target interaction by using information aggregation based on heterogeneous graph neural network, BMC Bioinformatics 23 (2022), 126.[PubMed].
[141]	E. Svensson, P.J. Hoedt, S. Hochreiter, et al., HyperPCM: Robust task-conditioned modeling of drug-target interactions, J Chem Inf Model 64 (2024) 2539-2553.[PubMed].
[142]	B. Liu, K. Pliakos, C. Vens, et al., Drug-target interaction prediction via an ensemble of weighted nearest neighbors with interaction recovery, Appl. Intell. 52 (2022) 3705-3727.[LinkOut].
[143]	T.M. Nguyen, T. Nguyen, T. Tran, Mitigating cold-start problems in drug-target affinity prediction with interaction knowledge transferring, Brief. Bioinform. 23 (2022), bbac269.[PubMed].
[144]	Z. Chu, F. Huang, H. Fu, et al., Hierarchical graph representation learning for the prediction of drug-target binding affinity, Inf. Sci. 613 (2022) 507-523.[LinkOut].