{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T06:15:20Z","timestamp":1784614520494,"version":"3.55.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T00:00:00Z","timestamp":1719360000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J Comput Intell Syst"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Drug\u2013target interactions is essential for advancing pharmaceuticals. Traditional drug\u2013target interaction studies rely on labor-intensive laboratory techniques. Still, recent advancements in computing power have elevated the importance of deep learning methods, offering faster, more precise, and cost-effective screening and prediction. Nonetheless, general deep learning methods often yield low-confidence results due to the complex nature of drugs and proteins, bias, limited labeled data, and feature extraction challenges. To address these challenges, a novel two-stage pre-trained framework is proposed for drug\u2013target interactions prediction. In the first stage, pre-trained molecule and protein models develop a comprehensive feature representation, enhancing the framework\u2019s ability to handle drug and protein diversity. This also reduces bias, improving prediction accuracy. In the second stage, a transformer with bilinear pooling and a fully connected layer enables predictions based on feature vectors. Comprehensive experiments were conducted using public datasets from DrugBank and Epigenetic-regulators datasets to evaluate the framework\u2019s effectiveness. The results demonstrate that the proposed framework outperforms the state-of-the-art methods regarding accuracy, area under the receiver operating characteristic curve, recall, and area under the precision-recall curve. The code is available at:\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/DHCGroup\/MocFormer\">https:\/\/github.com\/DHCGroup\/MocFormer<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1007\/s44196-024-00561-1","type":"journal-article","created":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T03:02:12Z","timestamp":1719370932000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["MocFormer: A Two-Stage Pre-training-Driven Transformer for Drug\u2013Target Interactions Prediction"],"prefix":"10.1007","volume":"17","author":[{"given":"Yi-Lun","family":"Zhang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen-Tao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-Hui","family":"Guan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deepak Kumar","family":"Jain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian-Yang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Swalpa Kumar","family":"Roy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,6,26]]},"reference":[{"issue":"10","key":"561_CR1","doi-asserted-by":"publisher","first-page":"939","DOI":"10.4155\/fmc-2019-0307","volume":"12","author":"N Berdigaliyev","year":"2020","unstructured":"Berdigaliyev, N., Aljofan, M.: An overview of drug discovery and development. Future Med. Chem. 12(10), 939\u2013947 (2020). https:\/\/doi.org\/10.4155\/fmc-2019-0307","journal-title":"Future Med. Chem."},{"issue":"9","key":"561_CR2","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1111\/jphp.13273","volume":"72","author":"J-P Jourdan","year":"2020","unstructured":"Jourdan, J.-P., Bureau, R., Rochais, C., Dallemagne, P.: Drug repositioning: a brief overview. J. Pharm. Pharmacol. 72(9), 1145\u20131151 (2020). https:\/\/doi.org\/10.1111\/jphp.13273","journal-title":"J. Pharm. Pharmacol."},{"key":"561_CR3","first-page":"132","volume":"2018","author":"H Lim","year":"2018","unstructured":"Lim, H., Poleksic, A., Xie, L.: Exploring landscape of drug\u2013target-pathway-side effect associations. AMIA Summits Transl. Sci. Proc. 2018, 132 (2018)","journal-title":"AMIA Summits Transl. Sci. Proc."},{"issue":"4","key":"561_CR4","doi-asserted-by":"publisher","first-page":"476","DOI":"10.3390\/molecules21040476","volume":"21","author":"M Himmat","year":"2016","unstructured":"Himmat, M., Salim, N., Al-Dabbagh, M.M., Saeed, F., Ahmed, A.: Adapting document similarity measures for ligand-based virtual screening. Molecules 21(4), 476 (2016)","journal-title":"Molecules"},{"key":"561_CR5","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L.U., Polosukhin, I.: Attention is all you need. In: Proceedings of Advances in Neural Information Processing Systems, vol. 30 (2017)"},{"key":"561_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2019.103159","volume":"93","author":"K Sachdev","year":"2019","unstructured":"Sachdev, K., Gupta, M.K.: A comprehensive review of feature based methods for drug target interaction prediction. J. Biomed. Inform. 93, 103159 (2019). https:\/\/doi.org\/10.1016\/j.jbi.2019.103159","journal-title":"J. Biomed. Inform."},{"issue":"6","key":"561_CR7","doi-asserted-by":"publisher","first-page":"1007129","DOI":"10.1371\/journal.pcbi.1007129","volume":"15","author":"I Lee","year":"2019","unstructured":"Lee, I., Keum, J., Nam, H.: Deepconv-dti: prediction of drug\u2013target interactions via deep learning with convolution on protein sequences. PLoS Comput. Biol. 15(6), 1007129 (2019). https:\/\/doi.org\/10.1371\/journal.pcbi.1007129","journal-title":"PLoS Comput. Biol."},{"issue":"12","key":"561_CR8","doi-asserted-by":"publisher","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","volume":"33","author":"Z Li","year":"2022","unstructured":"Li, Z., Liu, F., Yang, W., Peng, S., Zhou, J.: A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans. Neural Netw. Learn. Syst. 33(12), 6999\u20137019 (2022). https:\/\/doi.org\/10.1109\/TNNLS.2021.3084827","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"5","key":"561_CR9","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers, D., Hahn, M.: Extended-connectivity fingerprints. J. Chem. Inf. Model. 50(5), 742\u2013754 (2010). https:\/\/doi.org\/10.1021\/ci100050t","journal-title":"J. Chem. Inf. Model."},{"issue":"2","key":"561_CR10","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1093\/bioinformatics\/bty535","volume":"35","author":"M Tsubaki","year":"2018","unstructured":"Tsubaki, M., Tomii, K., Sese, J.: Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics 35(2), 309\u2013318 (2018). https:\/\/doi.org\/10.1093\/bioinformatics\/bty535","journal-title":"Bioinformatics"},{"issue":"25","key":"561_CR11","doi-asserted-by":"publisher","first-page":"5633","DOI":"10.1021\/acs.jpca.1c02419","volume":"125","author":"W Chen","year":"2021","unstructured":"Chen, W., Chen, G., Zhao, L., Chen, C.Y.-C.: Predicting drug\u2013target interactions with deep-embedding learning of graphs and sequences. J. Phys. Chem. A 125(25), 5633\u20135642 (2021). https:\/\/doi.org\/10.1021\/acs.jpca.1c02419","journal-title":"J. Phys. Chem. A"},{"key":"561_CR12","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"561_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"561_CR14","doi-asserted-by":"publisher","unstructured":"Ni, Z.-L., Bian, G.-B., Zhou, X.-H., Hou, Z.-G., Xie, X.-L., Wang, C., Zhou, Y.-J., Li, R.-Q., Li, Z.: Raunet: residual attention u-net for semantic segmentation of cataract surgical instruments. In: Proceedings of International Conference on Neural Information Processing, pp. 139\u2013149 (2019). https:\/\/doi.org\/10.1007\/978-3-030-36711-4_13","DOI":"10.1007\/978-3-030-36711-4_13"},{"issue":"11","key":"561_CR15","doi-asserted-by":"publisher","first-page":"4043","DOI":"10.3390\/s22114043","volume":"22","author":"M Liu","year":"2022","unstructured":"Liu, M., Zou, W., Wang, W., Jin, C.-B., Chen, J., Piao, C.: Multi-conditional constraint generative adversarial network-based mr imaging from ct scan data. Sensors 22(11), 4043 (2022). https:\/\/doi.org\/10.3390\/s22114043","journal-title":"Sensors"},{"key":"561_CR16","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., Sun, M.: Graph neural networks: a review of methods and applications. AI Open 1, 57\u201381 (2020). https:\/\/doi.org\/10.1016\/j.aiopen.2021.01.001","journal-title":"AI Open"},{"issue":"6","key":"561_CR17","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1093\/bioinformatics\/btaa880","volume":"37","author":"K Huang","year":"2021","unstructured":"Huang, K., Xiao, C., Glass, L.M., Sun, J.: MolTrans: molecular interaction transformer for drug-target interaction prediction. Bioinformatics 37(6), 830\u2013836 (2021). https:\/\/doi.org\/10.1093\/bioinformatics\/btaa880","journal-title":"Bioinformatics"},{"issue":"4","key":"561_CR18","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1093\/bib\/bbac272","volume":"23","author":"M Yazdani-Jahromi","year":"2022","unstructured":"Yazdani-Jahromi, M., Yousefi, N., Tayebi, A., Kolanthai, E., Neal, C.J., Seal, S., Garibay, O.O.: AttentionSiteDTI: an interpretable graph-based model for drug\u2013target interaction prediction using NLP sentence-level relation classification. Brief. Bioinform. 23(4), 272 (2022). https:\/\/doi.org\/10.1093\/bib\/bbac272","journal-title":"Brief. Bioinform."},{"issue":"3","key":"561_CR19","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1093\/bioinformatics\/btab715","volume":"38","author":"Q Zhao","year":"2021","unstructured":"Zhao, Q., Zhao, H., Zheng, K., Wang, J.: HyperAttentionDTI: improving drug\u2013protein interaction prediction by sequence-based deep learning with attention mechanism. Bioinformatics 38(3), 655\u2013662 (2021). https:\/\/doi.org\/10.1093\/bioinformatics\/btab715","journal-title":"Bioinformatics"},{"issue":"ja","key":"561_CR20","doi-asserted-by":"publisher","first-page":"0","DOI":"10.1021\/acs.jcim.7b00616","volume":"0","author":"S Jaeger","year":"2018","unstructured":"Jaeger, S., Fulle, S., Turk, S.: Mol2vec: unsupervised machine learning approach with chemical intuition. J. Chem. Inf. Model. 0(ja), 0 (2018). https:\/\/doi.org\/10.1021\/acs.jcim.7b00616","journal-title":"J. Chem. Inf. Model."},{"key":"561_CR21","doi-asserted-by":"crossref","unstructured":"Zhou, G., et al.: Uni-mol: a universal 3d molecular representation learning framework. In: Proceedings of The Eleventh International Conference on Learning Representations (2023)","DOI":"10.26434\/chemrxiv-2022-jjm0j-v4"},{"issue":"12","key":"561_CR22","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1038\/s42256-022-00580-7","volume":"4","author":"J Ross","year":"2022","unstructured":"Ross, J., Belgodere, B., Chenthamarakshan, V., Padhi, I., Mroueh, Y., Das, P.: Large-scale chemical language representations capture molecular structure and properties. Nat. Mach. Intell. 4(12), 1256\u20131264 (2022). https:\/\/doi.org\/10.1038\/s42256-022-00580-7","journal-title":"Nat. Mach. Intell."},{"issue":"6637","key":"561_CR23","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1126\/science.ade2574","volume":"379","author":"Z Lin","year":"2023","unstructured":"Lin, Z., Akin, H., Rao, R., Hie, B., Zhu, Z., Lu, W., Smetanin, N., Verkuil, R., Kabeli, O., Shmueli, Y., Santos Costa, A., Fazel-Zarandi, M., Sercu, T., Candido, S., Rives, A.: Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379(6637), 1123\u20131130 (2023). https:\/\/doi.org\/10.1126\/science.ade2574","journal-title":"Science"},{"issue":"5","key":"561_CR24","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1002\/cpz1.113","volume":"1","author":"C Dallago","year":"2021","unstructured":"Dallago, C., Sch\u00fctze, K., Heinzinger, M., Olenyi, T., Littmann, M., Lu, A.X., Yang, K.K., Min, S., Yoon, S., Morton, J.T., Rost, B.: Learned embeddings from deep learning to visualize and predict protein sets. Curr. Protoc. 1(5), 113 (2021). https:\/\/doi.org\/10.1002\/cpz1.113","journal-title":"Curr. Protoc."},{"issue":"1","key":"561_CR25","doi-asserted-by":"publisher","first-page":"18200","DOI":"10.1038\/s41598-022-23014-1","volume":"12","author":"B Wei","year":"2022","unstructured":"Wei, B., Zhang, Y., Gong, X.: Deeplpi: a novel deep learning-based model for protein\u2013ligand interaction prediction for drug repurposing. Sci. Rep. 12(1), 18200 (2022). https:\/\/doi.org\/10.1038\/s41598-022-23014-1","journal-title":"Sci. Rep."},{"issue":"1","key":"561_CR26","doi-asserted-by":"publisher","first-page":"1989","DOI":"10.1038\/s41467-023-37572-z","volume":"14","author":"A Chatterjee","year":"2023","unstructured":"Chatterjee, A., Walters, R., Shafi, Z., Ahmed, O.S., Sebek, M., Gysi, D., Yu, R., Eliassi-Rad, T., Barab\u00e1si, A.-L., Menichetti, G.: Improving the generalizability of protein\u2013ligand binding predictions with ai-bind. Nat. Commun. 14(1), 1989 (2023). https:\/\/doi.org\/10.1038\/s41467-023-37572-z","journal-title":"Nat. Commun."},{"key":"561_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2024.108339","volume":"173","author":"M Gao","year":"2024","unstructured":"Gao, M., et al.: Graphormerdti: a graph transformer-based approach for drug\u2013target interaction prediction. Comput. Biol. Med. 173, 108339 (2024). https:\/\/doi.org\/10.1016\/j.compbiomed.2024.108339","journal-title":"Comput. Biol. Med."},{"issue":"1","key":"561_CR28","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1093\/nar\/gkad976","volume":"52","author":"C Knox","year":"2024","unstructured":"Knox, C., Wilson, M., Klinger, C.M., Franklin, M., Oler, E., Wilson, A., Pon, A., Cox, J., Chin, N.E., Strawbridge, S.A., et al.: Drugbank 6.0: the drugbank knowledgebase for 2024. Nucleic Acids Res. 52(1), 1265\u20131275 (2024). https:\/\/doi.org\/10.1093\/nar\/gkad976","journal-title":"Nucleic Acids Res."},{"key":"561_CR29","unstructured":"Landrum, G., et al.: Rdkit: open-source cheminformatics software (2016)"},{"issue":"4","key":"561_CR30","doi-asserted-by":"publisher","first-page":"1401","DOI":"10.1021\/acs.jproteome.6b00618","volume":"16","author":"M Wen","year":"2017","unstructured":"Wen, M., Zhang, Z., Niu, S., Sha, H., Yang, R., Yun, Y., Lu, H.: Deep-learning-based drug\u2013target interaction prediction. J. Proteome Res. 16(4), 1401\u20131409 (2017). https:\/\/doi.org\/10.1021\/acs.jproteome.6b00618","journal-title":"J. Proteome Res."},{"issue":"1","key":"561_CR31","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-023-00689-w","volume":"15","author":"H Atas Guvenilir","year":"2023","unstructured":"Atas Guvenilir, H., Do\u011fan, T.: How to approach machine learning-based prediction of drug\/compound\u2013target interactions. J. Cheminform. 15(1), 1\u201336 (2023). https:\/\/doi.org\/10.1186\/s13321-023-00689-w","journal-title":"J. Cheminform."},{"key":"561_CR32","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00605-1","author":"P Bai","year":"2023","unstructured":"Bai, P., Miljkovi\u0107, F., John, B., Lu, H.: Interpretable bilinear attention network with domain adaptation improves drug\u2013target prediction. Nat. Mach. Intell. (2023). https:\/\/doi.org\/10.1038\/s42256-022-00605-1","journal-title":"Nat. Mach. Intell."},{"key":"561_CR33","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-37572-z","author":"A Chatterjee","year":"2023","unstructured":"Chatterjee, A., Walters, R., Shafi, Z., Ahmed, O.S., Sebek, M., Gysi, D., Yu, R., Eliassi-Rad, T., Barab\u00e1si, A.-L., Menichetti, G.: Improving the generalizability of protein\u2013ligand binding predictions with AI-bind. Nat. Commun. (2023). https:\/\/doi.org\/10.1038\/s41467-023-37572-z","journal-title":"Nat. Commun."}],"container-title":["International Journal of Computational Intelligence Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00561-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44196-024-00561-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44196-024-00561-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,26]],"date-time":"2024-06-26T03:05:14Z","timestamp":1719371114000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44196-024-00561-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,26]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["561"],"URL":"https:\/\/doi.org\/10.1007\/s44196-024-00561-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2023.09.13.557595","asserted-by":"object"}]},"ISSN":["1875-6883"],"issn-type":[{"value":"1875-6883","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,26]]},"assertion":[{"value":"2 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declared that there are no potential conflict of interest concerning the research, authorship, and publication of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"165"}}