{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:30:48Z","timestamp":1757619048809,"version":"3.44.0"},"publisher-location":"Singapore","reference-count":15,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819500260"},{"type":"electronic","value":"9789819500277"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025]]},"DOI":"10.1007\/978-981-95-0027-7_30","type":"book-chapter","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T14:15:03Z","timestamp":1752675303000},"page":"342-354","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DTI Prediction Based on Lightweight MoE"],"prefix":"10.1007","author":[{"given":"Fang","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Juanjuan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Qiang","sequence":"additional","affiliation":[]},{"given":"Zihang","family":"Yuan","sequence":"additional","affiliation":[]},{"given":"Yafeng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yaheng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Geng","sequence":"additional","affiliation":[]},{"given":"YiFang","family":"Zheng","sequence":"additional","affiliation":[]},{"given":"Yuanchen","family":"Gao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"issue":"1","key":"30_CR1","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1162\/neco.1991.3.1.79","volume":"3","author":"RA Jacobs","year":"1991","unstructured":"Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Comput. 3(1), 79\u201387 (1991)","journal-title":"Neural Comput."},{"key":"30_CR2","doi-asserted-by":"crossref","unstructured":"Qi, H., Yu, T., Yu, W., et al. Drug\u2013target affinity prediction with extended graph learning-convolutional networks. BMC Bioinform. 25, 75 (2024)","DOI":"10.1186\/s12859-024-05698-6"},{"key":"30_CR3","doi-asserted-by":"crossref","unstructured":"Tsubaki, M., Tomii, K., Sese, J. Compound\u2013protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. Bioinformatics 35 (2019)","DOI":"10.1093\/bioinformatics\/bty535"},{"issue":"6","key":"30_CR4","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\u2013target interaction prediction. Bioinformatics 37(6), 830\u2013836 (2021)","journal-title":"Bioinformatics"},{"key":"30_CR5","doi-asserted-by":"publisher","unstructured":"Yang, Z., Zhong, W., Zhao, L., et al.: MGraphDTA: deep multiscale graph neural network for explainable drug\u2013target binding affinity prediction. Chem. Sci. 13 (2022). https:\/\/doi.org\/10.1039\/D1SC05180F","DOI":"10.1039\/D1SC05180F"},{"key":"30_CR6","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30, pp. 5998\u20136008 (2022). https:\/\/arxiv.org\/abs\/1706.03762"},{"key":"30_CR7","doi-asserted-by":"crossref","unstructured":"Meng, W., Xu, X., Xiao, Z., Gao, L., Yu, L.: Cancer drug sensitivity prediction based on deep transfer learning. Int. J. Mol. Sci. 26(6), 2468 (2025)","DOI":"10.3390\/ijms26062468"},{"issue":"6","key":"30_CR8","doi-asserted-by":"publisher","first-page":"2384","DOI":"10.3390\/ijms26062384","volume":"26","author":"D Sun","year":"2025","unstructured":"Sun, D., Hu, Y., Peng, J., Wang, S.: Construction of T-Cell-related prognostic risk models and prediction of tumor immune microenvironment regulation in pancreatic adenocarcinoma via integrated analysis of single-cell RNA-Seq and bulk RNA-Seq. Int. J. Mol. Sci. 26(6), 2384 (2025)","journal-title":"Int. J. Mol. Sci."},{"key":"30_CR9","doi-asserted-by":"crossref","unstructured":"Wang, X., Deng, W., Meng, Z., et al.: Hybrid-attention mechanism based heterogeneous graph representation learning. Expert Syst. Appl. 250, 123963 (2024)","DOI":"10.1016\/j.eswa.2024.123963"},{"key":"30_CR10","doi-asserted-by":"crossref","unstructured":"Zhu, G., Zhu, Z., Chen, H., Yuan, C., Huang, Y.: HAGNN: hybrid aggregation for heterogeneous graph neural networks. In: IEEE Transactions on Neural Networks and Learning Systems. Advance Online Publication (2024).","DOI":"10.1109\/TNNLS.2024.3519427"},{"key":"30_CR11","doi-asserted-by":"crossref","unstructured":"Ren, Z.H., You, Z.H., Zou, Q., et al.: DeepMPF: deep learning framework for predicting drug\u2013target interactions based on multi-modal representation with meta-path semantic analysis. J. Transl. Med. 21, 48 (2023)","DOI":"10.1186\/s12967-023-03876-3"},{"key":"30_CR12","doi-asserted-by":"crossref","unstructured":"Hu, Z., Dong, Y., Wang, K., Sun, Y.: Heterogeneous graph transformer. In: Proceedings of the Web Conference 2020, pp. 2704\u20132710(2020).","DOI":"10.1145\/3366423.3380027"},{"key":"30_CR13","unstructured":"DeepSeek Team: DeepSeekMoE: efficient and scalable mixture-of-experts architecture for large language models. CSDN Blog (2025). https:\/\/blog.csdn.net\/deepseek_team\/article\/DeepSeekMoE_2025"},{"issue":"7","key":"30_CR14","doi-asserted-by":"publisher","first-page":"2496","DOI":"10.1021\/acs.jcim.3c01208","volume":"64","author":"R Gorantla","year":"2024","unstructured":"Gorantla, R., Kubincov\u00e1, A., Wei\u00dfe, A.Y., Mey, A.S.J.S.: From proteins to ligands: decoding deep learning methods for binding affinity prediction. J. Chem. Inf. Model. 64(7), 2496\u20132507 (2024)","journal-title":"J. Chem. Inf. Model."},{"key":"30_CR15","doi-asserted-by":"publisher","first-page":"130052","DOI":"10.1016\/j.neucom.2025.130052","volume":"637","author":"M Zheng","year":"2025","unstructured":"Zheng, M., et al.: MLC-DTA: drug-target affinity prediction based on multi-level contrastive learning and equivariant graph neural networks. Neurocomputing 637, 130052 (2025)","journal-title":"Neurocomputing"}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0027-7_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,7]],"date-time":"2025-09-07T12:40:16Z","timestamp":1757248816000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0027-7_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819500260","9789819500277"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0027-7_30","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"17 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article (1991).","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}