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A review of machine learning-based methods for predicting drug–target interactions

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Abstract

The prediction of drug–target interactions (DTI) is a crucial preliminary stage in drug discovery and development, given the substantial risk of failure and the prolonged validation period associated with in vitro and in vivo experiments. In the contemporary landscape, various machine learning-based methods have emerged as indispensable tools for DTI prediction. This paper begins by placing emphasis on the data representation employed by these methods, delineating five representations for drugs and four for proteins. The methods are then categorized into traditional machine learning-based approaches and deep learning-based ones, with a discussion of representative approaches in each category and the introduction of a novel taxonomy for deep neural network models in DTI prediction. Additionally, we present a synthesis of commonly used datasets and evaluation metrics to facilitate practical implementation. In conclusion, we address current challenges and outline potential future directions in this research field.

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References

  1. Ambudkar SV, Gottesman MM, editors. ABC Transporters: Biomedical, Cellular, and Molecular Aspects, Methods in Enzymology, vol. 292. San Diego (CA): Academic Press; 1998.

  2. Al-Absi HR, Refaee MA, Rehman AU, Islam MT, Belhaouari SB, Alam T. Risk factors and comorbidities associated to cardiovascular disease in qatar: a machine learning based case-control study. IEEE Access. 2021;9:29929–41.

    Article  Google Scholar 

  3. Vermaas JV, Sedova A, Baker MB, Boehm S, Rogers DM, Larkin J, Glaser J, Smith MD, Hernandez O, Smith JC. Supercomputing pipelines search for therapeutics against covid-19. Comput Sci Eng. 2020;23(1):7–16.

    Article  Google Scholar 

  4. Peska L, Buza K, Koller J. Drug-target interaction prediction: a Bayesian ranking approach. Comput Methods Programs Biomed. 2017;152:15–21.

    Article  Google Scholar 

  5. Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M. Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics. 2008;24(13):i232–40.

    Article  Google Scholar 

  6. Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MND. A ligand-based approach for the in silico discovery of multi-target inhibitors for proteins associated with HIV infection. Mol Biosyst. 2012;8(8):2188–96.

    Article  Google Scholar 

  7. Yang Y, Zhu Z, Wang X, Zhang X, Mu K, Shi Y, Peng C, Xu Z, Zhu W. Ligand-based approach for predicting drug targets and for virtual screening against covid-19. Briefings Bioinform. 2021;22(2):1053–64.

    Article  Google Scholar 

  8. Aziz F, Cardoso VR, Bravo-Merodio L, Russ D, Pendleton SC, Williams JA, Acharjee A, Gkoutos GV. Multimorbidity prediction using link prediction. Sci Rep. 2021;11(1):16392.

    Article  Google Scholar 

  9. Valentin JP, Guillon JM, Jenkinson S, Kadambi V, Ravikumar P, Roberts S, Rosenbrier-Ribeiro L, Schmidt F, Armstrong D. In vitro secondary pharmacological profiling: an iq-drusafe industry survey on current practices. J Pharmacol Toxicol Methods. 2018;93:7–14.

    Article  Google Scholar 

  10. da Silva Rocha SF, Olanda CG, Fokoue HH, Sant’Anna CM. Virtual screening techniques in drug discovery: review and recent applications. Curr Top Med Chem. 2019;19(19):1751–67.

    Article  Google Scholar 

  11. Mousavian Z, Masoudi-Nejad A. Drug-target interaction prediction via chemogenomic space: learning-based methods. Expert Opin Drug Metab Toxicol. 2014;10(9):1273–87.

    Article  Google Scholar 

  12. Li J, Zheng S, Chen B, Butte AJ, Swamidass SJ, Lu Z. A survey of current trends in computational drug repositioning. Brief Bioinform. 2016;17(1):2–12.

    Article  Google Scholar 

  13. Chen R, Liu X, Jin S, Lin J, Liu J. Machine learning for drug-target interaction prediction. Molecules. 2018;23(9):2208.

    Article  Google Scholar 

  14. Zhang W, Lin W, Zhang D, Wang S, Shi J, Niu Y. Recent advances in the machine learning-based drug-target interaction prediction. Curr Drug Metabol. 2019;20(3):194–202.

    Article  Google Scholar 

  15. Bagherian M, Sabeti E, Wang K, Sartor MA, Nikolovska-Coleska Z, Najarian K. Machine learning approaches and databases for prediction of drug-target interaction: a survey paper. Brief Bioinform. 2021;22(1):247–69.

    Article  Google Scholar 

  16. Stokes JM, Yang K, Swanson K, Jin W, Cubillos-Ruiz A, Donghia NM, MacNair CR, French S, Carfrae LA, Bloom-Ackermann Z, et al. A deep learning approach to antibiotic discovery. Cell. 2020;180(4):688–702.

    Article  Google Scholar 

  17. Thafar MA, Alshahrani M, Albaradei S, Gojobori T, Essack M, Gao X. Affinity2vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning. Sci Rep. 2022;12(1):4751.

    Article  Google Scholar 

  18. Honda S, Shi S, Ueda H.R. Smiles transformer: Pre-trained molecular fingerprint for low data drug discovery. arXiv preprint arXiv:1911.04738 2019.

  19. Wang YB, You ZH, Yang S, Yi HC, Chen ZH, Zheng K. A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network. BMC Med Inform Decis Mak. 2020;20(2):1–9.

    Google Scholar 

  20. Zhao ZY, Huang WZ, Zhan XK, Pan J, Huang YA, Zhang SW, Yu CQ et al. An ensemble learning-based method for inferring drug-target interactions combining protein sequences and drug fingerprints. BioMed Res Int. 2021;2021.

  21. Zhao Z, Bourne PE. Harnessing systematic protein–ligand interaction fingerprints for drug discovery. Drug Discov Today. 2022;27(10):103319.

    Article  Google Scholar 

  22. Shao J, Gong Q, Yin Z, Yin Z, Pan W, Pandiyan S, Wang L. S2dv: converting smiles to a drug vector for predicting the activity of anti-hbv small molecules. Brief Bioinform. 2022;23(2):bbab593.

    Article  Google Scholar 

  23. Kurata H, Tsukiyama S, Manavalan B. iacvp: markedly enhanced identification of anti-coronavirus peptides using a dataset-specific word2vec model. Brief Bioinform. 2022;23(4):bbac265.

    Article  Google Scholar 

  24. Lim J, Ryu S, Kim JW, Kim WY. Molecular generative model based on conditional variational autoencoder for de novo molecular design. J Chem. 2018;10(1):1–9.

    Google Scholar 

  25. Li P, Wang J, Qiao Y, Chen H, Yu Y, Yao X, Gao P, Xie G, Song S. Learn molecular representations from large-scale unlabeled molecules for drug discovery. arXiv preprint arXiv:2012.11175. 2020.

  26. Zhang J, Liu B. A review on the recent developments of sequence-based protein feature extraction methods. Curr Bioinform. 2019;14(3):190–9.

    Article  Google Scholar 

  27. Cui F, Zhang Z, Zou Q. Sequence representation approaches for sequence-based protein prediction tasks that use deep learning. Brief Funct Genom. 2021;20(1):61–73.

    Article  Google Scholar 

  28. Zhang YF, Wang X, Kaushik AC, Chu Y, Shan X, Zhao MZ, Xu Q, Wei DQ. Spvec: a word2vec-inspired feature representation method for drug-target interaction prediction. Front Chem. 2020;7:895.

    Article  Google Scholar 

  29. Wang H, Wang J, Dong C, Lian Y, Liu D, Yan Z. A novel approach for drug-target interactions prediction based on multimodal deep autoencoder. Front Pharmacol. 2020;10:1592.

    Article  Google Scholar 

  30. Jiang M, Li Z, Zhang S, Wang S, Wang X, Yuan Q, Wei Z. Drug-target affinity prediction using graph neural network and contact maps. RSC Adv. 2020;10(35):20701–12.

    Article  Google Scholar 

  31. Wang P, Zheng S, Jiang Y, Li C, Liu J, Wen C, Patronov A, Qian D, Chen H, Yang Y. Structure-aware multimodal deep learning for drug-protein interaction prediction. J Chem Inform Model. 2022;62(5):1308–17.

    Article  Google Scholar 

  32. Zhao Y, Zheng K, Guan B, Guo M, Song L, Gao J, Qu H, Wang Y, Shi D, Zhang Y. Dldti: a learning-based framework for drug-target interaction identification using neural networks and network representation. J Transl Med. 2020;18:1–15.

    Article  Google Scholar 

  33. Lee H, Kim W. Comparison of target features for predicting drug-target interactions by deep neural network based on large-scale drug-induced transcriptome data. Pharmaceutics. 2019;11(8):377.

    Article  Google Scholar 

  34. Li X, Xiong H, Li X, Wu X, Zhang X, Liu J, Bian J, Dou D. Interpretable deep learning: interpretation, interpretability, trustworthiness, and beyond. Knowl Inform Syst. 2022;64(12):3197–234.

    Article  Google Scholar 

  35. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database hallmark gene set collection. Cell Syst. 2015;1(6):417–25.

    Article  Google Scholar 

  36. Thafar MA, Olayan RS, Ashoor H, Albaradei S, Bajic VB, Gao X, Gojobori T, Essack M. Dtigems+: drug-target interaction prediction using graph embedding, graph mining, and similarity-based techniques. J Cheminform. 2020;12(1):1–17.

    Article  Google Scholar 

  37. Mohamed SK, Nováček V, Nounu A. Discovering protein drug targets using knowledge graph embeddings. Bioinformatics. 2020;36(2):603–10.

    Article  Google Scholar 

  38. Thafar MA, Olayan RS, Albaradei S, Bajic VB, Gojobori T, Essack M, Gao X. Dti2vec: drug-target interaction prediction using network embedding and ensemble learning. J Cheminform. 2021;13(1):1–18.

    Article  Google Scholar 

  39. Perlman L, Gottlieb A, Atias N, Ruppin E, Sharan R. Combining drug and gene similarity measures for drug-target elucidation. J Comput Biol. 2011;18(2):133–45.

    Article  Google Scholar 

  40. Perlman L, Gottlieb A, Atias N, Ruppin E, Sharan R. Combining drug and gene similarity measures for drug-target elucidation. J Comput Biol. 2011;18(2):133–45.

    Article  Google Scholar 

  41. Shi JY, Yiu SM In: 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (IEEE, 2015): 1636–1641

  42. Shi JY, Li JX, Lu HM, Zhang Y. In: Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques: 5th International Conference, IScIDE 2015, Suzhou, June 14–16, 2015, Revised Selected Papers, Part II 5 (Springer, 2015): 477–486

  43. Shi JY, Yiu SM, Li Y, Leung HC, Chin FY. Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering. Methods. 2015;83:98–104.

    Article  Google Scholar 

  44. Buza K, Peška L. Drug-target interaction prediction with bipartite local models and hubness-aware regression. Neurocomputing. 2017;260:284–93.

    Article  Google Scholar 

  45. Pahikkala T, Airola A, Pietilä S, Shakyawar S, Szwajda A, Tang J, Aittokallio T. Toward more realistic drug-target interaction predictions. Brief Bioinform. 2015;16(2):325–37.

    Article  Google Scholar 

  46. He T, Heidemeyer M, Ban F, Cherkasov A, Ester M. Simboost: a read-across approach for predicting drug-target binding affinities using gradient boosting machines. J Cheminform. 2017;9(1):1–14.

    Article  Google Scholar 

  47. Yu H, Chen J, Xu X, Li Y, Zhao H, Fang Y, Li X, Zhou W, Wang W, Wang Y. A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data. PLoS ONE. 2012;7(5): e37608.

    Article  Google Scholar 

  48. Fu G, Ding Y, Seal A, Chen B, Sun Y, Bolton E. Predicting drug target interactions using meta-path-based semantic network analysis. BMC Bioinform. 2016;17(1):1–10.

    Article  Google Scholar 

  49. Wang MY, Li P, Qiao Pl, et al. The virtual screening of the drug protein with a few crystal structures based on the adaboost-svm. Comput Math Methods Med. 2016:2016

  50. Olayan RS, Ashoor H, Bajic VB. Ddr: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches. Bioinformatics. 2018;34(7):1164–73.

    Article  Google Scholar 

  51. Yu H, Chen J, Xu X, Li Y, Zhao H, Fang Y, Li X, Zhou W, Wang W, Wang Y. A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data. PLoS ONE. 2012;7(5): e37608.

    Article  Google Scholar 

  52. Ezzat A, Wu M, Li XL, Kwoh CK. Computational prediction of drug-target interactions using chemogenomic approaches: an empirical survey. Brief Bioinform. 2019;20(4):1337–57.

    Article  Google Scholar 

  53. Xia Z, Wu LY, Zhou X, Wong ST. In: BMC systems biology, vol. 4 (BioMed Central, 2010): 1–16

  54. Van Laarhoven T, Nabuurs SB, Marchiori E. Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics. 2011;27(21):3036–43.

    Article  Google Scholar 

  55. Wang Y, Zeng J. Predicting drug-target interactions using restricted Boltzmann machines. Bioinformatics. 2013;29(13):i126–34.

    Article  Google Scholar 

  56. Aghakhani S, Qabaja A, Alhajj R. Integration of k-means clustering algorithm with network analysis for drug-target interactions network prediction. Int J Data Min Bioinform. 2018;20(3):185–212.

    Article  Google Scholar 

  57. Yang F, Xue F, Zhang Y, Karypis G. Kernelized multitask learning method for personalized signaling adverse drug reactions. IEEE Trans Knowl Data Eng. 2021;35(2):1681–94.

    Google Scholar 

  58. Zheng Y, Tang P, Qiu W, Wang H, Guo J, Huang Z. in International conference on database systems for advanced applications (Springer, 2023):336–352

  59. Yin XX, Jian Y, Zhang Y, Zhang Y, Wu J, Lu H, Su MY. Automatic breast tissue segmentation in mris with morphology snake and deep denoiser training via extended stein’s unbiased risk estimator. Health Inform Sci Syst. 2021;9:1–21.

    Google Scholar 

  60. Ma T, Xiao C, Zhou J, Wang F. Drug similarity integration through attentive multi-view graph auto-encoders. arXiv preprint arXiv:1804.10850. 2018.

  61. Vázquez J, López M, Gibert E, Herrero E, Luque FJ. Merging ligand-based and structure-based methods in drug discovery: an overview of combined virtual screening approaches. Molecules. 2020;25(20):4723.

    Article  Google Scholar 

  62. Ravì D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, Yang GZ. Deep learning for health informatics. IEEE J Biomed Health Inform. 2016;21(1):4–21.

    Article  Google Scholar 

  63. Abbasi K, Razzaghi P, Poso A, Ghanbari-Ara S, Masoudi-Nejad A. Deep learning in drug target interaction prediction: current and future perspectives. Curr Med Chem. 2021;28(11):2100–13.

    Article  Google Scholar 

  64. Thafar M, Raies AB, Albaradei S, Essack M, Bajic VB. Comparison study of computational prediction tools for drug-target binding affinities. Front Chem. 2019;7:782.

    Article  Google Scholar 

  65. Nikraftar Z, Keyvanpour MR. A comparative analytical review on machine learning methods in Drugtarget interactions prediction. Curr Comput-Aided Drug Des. 2023;19(5):325–55.

    Article  Google Scholar 

  66. Liu X, Yan M, Deng L, Li G, Ye X, Fan D, Pan S, Xie Y. Survey on graph neural network acceleration: An algorithmic perspective. arXiv preprint arXiv:2202.04822. 2022

  67. Huang L, Lin J, Liu R, Zheng Z, Meng L, Chen X, Li X, Wong KC. Coadti: multi-modal co-attention based framework for drug–target interaction annotation. Brief Bioinform. 2022;23(6):bbac446.

    Article  Google Scholar 

  68. Hua Y, Song X, Feng Z, Wu X. Mfr-dta: a multi-functional and robust model for predicting drug–target binding affinity and region. Bioinformatics. 2023;39(2):btad056.

    Article  Google Scholar 

  69. Gim M, Choe J, Baek S, Park J, Lee C, Ju M, Lee S, Kang J, Kang J. Arkdta: attention regularization guided by non-covalent interactions for explainable drug–target binding affinity prediction. Bioinformatics. 2023;39(1):i448–57.

    Article  Google Scholar 

  70. Yuan Y, Chang S, Zhang Z, Li Z, Li S, Xie P, Yau WP, Lin H, Cai W, Zhang Y, et al. A novel strategy for prediction of human plasma protein binding using machine learning techniques. Chemom Intell Lab Syst. 2020;199: 103962.

    Article  Google Scholar 

  71. Askr H, Elgeldawi E, Aboul Ella H, Elshaier YA, Gomaa MM, Hassanien AE. Deep learning in drug discovery: an integrative review and future challenges. Artif Intell Rev. 2023;56(7):5975–6037.

    Article  Google Scholar 

  72. Öztürk H, Özgür A, Ozkirimli E. Deepdta: deep drug-target binding affinity prediction. Bioinformatics. 2018;34(17):i821–9.

    Article  Google Scholar 

  73. Öztürk H, Ozkirimli E, Özgür A. Widedta: prediction of drug-target binding affinity. arXiv preprint arXiv:1902.04166. 2019.

  74. Lee I, Keum J, Nam H. Deepconv-dti: prediction of drug-target interactions via deep learning with convolution on protein sequences. PLoS Comput Biol. 2019;15(6): e1007129.

    Article  Google Scholar 

  75. Zheng X, He S, Song X, Zhang Z, Bo X. In: Artificial neural networks and machine learning-ICANN 2018: 27th international conference on artificial neural networks, Rhodes, October 4–7, 2018, Proceedings, Part I 27 (Springer, 2018): 104–114

  76. Rifaioglu AS, Cetin Atalay R, Cansen Kahraman D, Doğan T, Martin M, Atalay V. Mdeepred: novel multi-channel protein featurization for deep learning-based binding affinity prediction in drug discovery. Bioinformatics. 2021;37(5):693–704.

    Article  Google Scholar 

  77. Nguyen T, Le H, Quinn TP, Nguyen T, Le TD, Venkatesh S. Graphdta: predicting drug-target binding affinity with graph neural networks. Bioinformatics. 2021;37(8):1140–7.

    Article  Google Scholar 

  78. Wu Y, Gao M, Zeng M, Zhang J, Li M. Bridgedpi: a novel graph neural network for predicting drug-protein interactions. Bioinformatics. 2022;38(9):2571–8.

    Article  Google Scholar 

  79. Zhang H, Hu J, Zhang X. In: International conference on intelligent computing (Springer, 2022): 533–546

  80. Yang Z, Zhong W, Zhao L, Chen CYC. Mgraphdta: deep multiscale graph neural network for explainable drug-target binding affinity prediction. Chem Sci. 2022;13(3):816–33.

    Article  Google Scholar 

  81. Mukherjee S, Ghosh M, Basuchowdhuri P. In: Proceedings of the 2022 SIAM international conference on data mining (SDM) (SIAM, 2022): 729–737

  82. Zhao Q, Xiao F, Yang M, Li Y, Wang J. In: 2019 IEEE international conference on bioinformatics and biomedicine (BIBM) (IEEE, 2019): 64–69

  83. Chen L, Tan X, Wang D, Zhong F, Liu X, Yang T, Luo X, Chen K, Jiang H, Zheng M. Transformercpi: improving compound-protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments. Bioinformatics. 2020;36(16):4406–14.

    Article  Google Scholar 

  84. Ghimire A, Tayara H, Xuan Z, Chong KT. Csatdta: prediction of drug-target binding affinity using convolution model with self-attention. Int J Mol Sci. 2022;23(15):8453.

    Article  Google Scholar 

  85. Monteiro NR, Oliveira JL, Arrais JP. Dtitr: end-to-end drug-target binding affinity prediction with transformers. Comput Biol Med. 2022;147: 105772.

    Article  Google Scholar 

  86. Cowen L, Ideker T, Raphael BJ, Sharan R. Network propagation: a universal amplifier of genetic associations. Nat Rev Genet. 2017;18(9):551–62.

    Article  Google Scholar 

  87. Luo Y, Zhao X, Zhou J, Yang J, Zhang Y, Kuang W, Peng J, Chen L, Zeng J. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nat Commun. 2017;8(1):573.

    Article  Google Scholar 

  88. Eslami Manoochehri H, Nourani M. Drug-target interaction prediction using semi-bipartite graph model and deep learning. BMC bioinform. 2020;21:1–16.

    Google Scholar 

  89. Wang Z, Zhou M, Arnold C. Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing. Bioinformatics. 2020;36(1):i525–33.

    Article  Google Scholar 

  90. Li X, Ma D, Ren Y, Luo J, Li Y. Large-scale prediction of drug-protein interactions based on network information. Curr Comput-Aided Drug Des. 2022;18(1):64–72.

    Article  Google Scholar 

  91. Peng J, Li J, Shang X. A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network. BMC Bioinform. 2020;21(13):1–13.

    Google Scholar 

  92. Peng J, Wang Y, Guan J, Li J, Han R, Wei Z, Shang X. An end-to-end heterogeneous graph representation learning-based framework for drug–target interaction prediction. Brief Bioinform. 2021;22(5):bbaa430.

    Article  Google Scholar 

  93. Zhou D, Xu Z, Li W, Xie X, Peng S. Multidti: drug–target interaction prediction based on multi-modal representation learning to bridge the gap between new chemical entities and known heterogeneous network. Bioinformatics. 2021;37(23):4485–92.

    Article  Google Scholar 

  94. Wang S, Du Z, Ding M, Rodriguez-Paton A, Song T. Kg-dti: a knowledge graph based deep learning method for drug-target interaction predictions and Alzheimer’s disease drug repositions. Appl Intell. 2022;52(1):846–57.

    Article  Google Scholar 

  95. Wang W, Liang S, Yu M, Liu D, Zhang H, Wang X, Zhou Y. Gchn-dti: predicting drug-target interactions by graph convolution on heterogeneous networks. Methods. 2022;206:101–7.

    Article  Google Scholar 

  96. Tunyasuvunakool K, Adler J, Wu Z, Green T, Zielinski M, Žídek A, Bridgland A, Cowie A, Meyer C, Laydon A, et al. Highly accurate protein structure prediction for the human proteome. Nature. 2021;596(7873):590–6.

    Article  Google Scholar 

  97. Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR, Wang J, Cong Q, Kinch LN, Schaeffer RD, et al. Accurate prediction of protein structures and interactions using a three-track neural network. Science. 2021;373(6557):871–6.

    Article  Google Scholar 

  98. Liebeschuetz JW, Cole JC, Korb O. Pose prediction and virtual screening performance of gold scoring functions in a standardized test. J Comput-Aided Mol Des. 2012;26:737–48.

    Article  Google Scholar 

  99. Clark JJ, Orban ZJ, Carlson HA. Predicting binding sites from unbound versus bound protein structures. Sci Rep. 2020;10(1):15856.

    Article  Google Scholar 

  100. Akdel M, Pires DE, Pardo EP, Jänes J, Zalevsky AO, Mészáros B, Bryant P, Good LL, Laskowski RA, Pozzati G, et al. A structural biology community assessment of alphafold2 applications. Nat Struct Mol Biol. 2022;29(11):1056–67.

    Article  Google Scholar 

  101. Laskowski RA, Watson JD, Thornton JM. Protein function prediction using local 3d templates. J Mol Biol. 2005;351(3):614–26.

    Article  Google Scholar 

  102. Zhang Z, Chen L, Zhong F, Wang D, Jiang J, Zhang S, Jiang H, Zheng M, Li X. Graph neural network approaches for drug-target interactions. Curr Opin Struct Biol. 2022;73: 102327.

    Article  Google Scholar 

  103. Jiménez J, Skalic M, Martinez-Rosell G, De Fabritiis G. K deep: protein-ligand absolute binding affinity prediction via 3d-convolutional neural networks. J Chem Inform Model. 2018;58(2):287–96.

    Article  Google Scholar 

  104. Tsubaki M, Tomii K, Sese J. Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics. 2019;35(2):309–18.

    Article  Google Scholar 

  105. Lim J, Ryu S, Park K, Choe YJ, Ham J, Kim WY. Predicting drug-target interaction using a novel graph neural network with 3d structure-embedded graph representation. J Chem Inform Model. 2019;59(9):3981–8.

    Article  Google Scholar 

  106. Li S, Zhou J, Xu T, Huang L, Wang F, Xiong H, Huang W, Dou D, Xiong H. In: Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data mining 2021: 975–985

  107. Aykent S, Xia T. In: Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining 2022: 4–14

  108. Stärk H, Ganea O, Pattanaik L, Barzilay R, Jaakkola T. In: International conference on machine learning (PMLR, 2022), pp. 20503–20521

  109. Zhou G, Gao Z, Ding Q, Zheng H, Xu H, Wei Z, Zhang L, Ke G. Uni-mol: a universal 3d molecular representation learning framework 2023.

  110. da Silva Rocha SF, Olanda CG, Fokoue HH, Sant’Anna CM. Virtual screening techniques in drug discovery: review and recent applications. Curr Top Med Chem. 2019;19(19):1751–67.

    Article  Google Scholar 

  111. Peng J, Li J, Shang X. A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network. BMC Bioinform. 2020;21(13):1–13.

    Google Scholar 

  112. Li Y, Qiao G, Gao X, Wang G. Supervised graph co-contrastive learning for drug-target interaction prediction. Bioinformatics. 2022;38(10):2847–54.

    Article  Google Scholar 

  113. Wu J, Lv X, Jiang S. In: Advances in intelligent automation and soft computing (Springer, 2022), pp. 376–383

  114. Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J. Bindingdb in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 2016;44(D1):D1045–53.

    Article  Google Scholar 

  115. Schomburg I, Chang A, Ebeling C, Gremse M, Heldt C, Huhn G. Brenda, the enzyme database: updates and major new developments. Nucleic acids Res. 2004;32(1):D431–3.

    Article  Google Scholar 

  116. Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D, Mutowo P, Atkinson F, Bellis LJ, Cibrián-Uhalte E, et al. The Chembl database in 2017. Nucleic Acids Res. 2017;45(D1):D945–54.

    Article  Google Scholar 

  117. Wishart DS, Feunang YD, Guo AC, Lo EJ, Sajed T, Johnson D, Li C, Sayeeda Z, Sayeeda Z, et al. Drugbank 5.0: a major update to the drugbank database for 2018. Nucleic acids Res. 2018;46(D1):D1074–82.

    Article  Google Scholar 

  118. Günther S, Kuhn M, Dunkel M, Campillos M, Senger C, Petsalaki E, Ahmed J, Urdiales EG, Gewiess A. Supertarget and matador: resources for exploring drug-target relationships. Nucleic Acids Res. 2007;36(1):D919–22.

    Article  Google Scholar 

  119. Wang R, Fang X, Lu Y, Yang CY, Wang S. The pdbbind database: methodologies and updates. J Med Chem. 2005;48(12):4111–9.

    Article  Google Scholar 

  120. Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, et al. Pubchem substance and compound databases. Nucleic Acids Res. 2016;44(D1):D1202–13.

    Article  Google Scholar 

  121. Kuhn M, Szklarczyk D, Pletscher-Frankild S, Blicher TH, Von Mering C, Jensen LJ, Bork P. Stitch 4: integration of protein-chemical interactions with user data. Nucleic Acids Res. 2014;42(D1):D401–7.

    Article  Google Scholar 

  122. Günther S, Kuhn M, Dunkel M, Campillos M, Senger C, Petsalaki E, Ahmed J, Urdiales EG, Gewiess A, Jensen LJ, et al. Supertarget and matador: resources for exploring drug-target relationships. Nucleic Acids Res. 2007;36(1):D919–22.

    Article  Google Scholar 

  123. Magariños MP, Carmona SJ, Crowther GJ, Ralph SA, Roos DS, Shanmugam D, Van Voorhis WC, Agüero F. Tdr targets: a chemogenomics resource for neglected diseases. Nucleic Acids Res. 2012;40(D1):D1118–27.

    Article  Google Scholar 

  124. Chen X, Ji ZL, Chen YZ. Ttd: therapeutic target database. Nucleic Acids Res. 2002;30(1):412–5.

    Article  Google Scholar 

  125. Tang J, Szwajda A, Shakyawar S, Xu T, Hintsanen P, Wennerberg K, Aittokallio T. Making sense of large-scale kinase inhibitor bioactivity data sets: a comparative and integrative analysis. J Chem Inform Model. 2014;54(3):735–43.

    Article  Google Scholar 

  126. Ezzat A, Wu M, Li XL, Kwoh CK. Drug-target interaction prediction via class imbalance-aware ensemble learning. BMC Bioinform. 2016;17(19):267–76.

    Google Scholar 

  127. Samara KA, Al Aghbari Z, Abusafia A. Glimpse: a glioblastoma prognostication model using ensemble learning’a surveillance, epidemiology, and end results study. Health Inform Sci Syst. 2021;9:1–13.

    Google Scholar 

  128. Hu L, Fu C, Ren Z, Cai Y, Yang J, Xu S, Xu W, Tang D. Sselm-neg: spherical search-based extreme learning machine for drug-target interaction prediction. BMC Bioinform. 2023;24(1):38.

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 62376065), Natural Science Foundation of Guangdong (No. 2022A1515010102) and Joint Research Fund of Guangzhou and University (No. 2024A03J0323).

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  1. Wen Shi and Hong Yang have contributed equally to this work.

Authors and Affiliations

  1. Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China

    Wen Shi, Hong Yang & Xiao-Xia Yin

  2. State Key Laboratory of Proteomics, National Center for Protein Sciences (Beijing), Beijing, 102206, China

    Linhai Xie

  3. School of Computer Science and Technology, Zhejiang Normal University, Jinhua, 321004, China

    Wen Shi & Yanchun Zhang

  4. Department of New Networks, Peng Cheng Laboratory, Shenzhen, 518000, China

    Yanchun Zhang

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  1. Wen Shi
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  2. Hong Yang
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Correspondence to Linhai Xie or Yanchun Zhang.

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Shi, W., Yang, H., Xie, L. et al. A review of machine learning-based methods for predicting drug–target interactions. Health Inf Sci Syst 12, 30 (2024). https://doi.org/10.1007/s13755-024-00287-6

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