{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:59:27Z","timestamp":1785340767931,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":30,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302004,62532017"],"award-info":[{"award-number":["62302004,62532017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,30]]},"DOI":"10.1145\/3807503.3819370","type":"proceedings-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T02:55:27Z","timestamp":1785293727000},"page":"1-6","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multi-View Fusion Framework Integrating Graph Representations and Pre-trained Models for Drug-Target Mechanisms of Action Prediction"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2157-9931","authenticated-orcid":false,"given":"Fei","family":"Wang","sequence":"first","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-9326-1459","authenticated-orcid":false,"given":"Yang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7658-3326","authenticated-orcid":false,"given":"Yue","family":"Chen","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1605-8082","authenticated-orcid":false,"given":"Hai","family":"Chen","sequence":"additional","affiliation":[{"name":"Anhui Chest Hospital, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2293-9524","authenticated-orcid":false,"given":"Dongliang","family":"Yang","sequence":"additional","affiliation":[{"name":"Anhui Chest Hospital, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9901-1732","authenticated-orcid":false,"given":"Xiujuan","family":"Lei","sequence":"additional","affiliation":[{"name":"Shaanxi Normal University, Xi'an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4593-9332","authenticated-orcid":false,"given":"Fang-Xiang","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Saskatchewan, Saskatoon, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3855-7133","authenticated-orcid":false,"given":"Yansen","family":"Su","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3024-1705","authenticated-orcid":false,"given":"Junfeng","family":"Xia","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,28]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"publisher","unstructured":"Peizhen Bai Filip Miljkovi\u0107 Bino John et\u00a0al. 2023. Interpretable bilinear attention network with domain adaptation improves drug\u2013target prediction. Nat Mach Intell 5 2 (2023) 126\u2013136. 10.1038\/s42256-022-00605-1","DOI":"10.1038\/s42256-022-00605-1"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","unstructured":"Jilong Bian Xi Zhang Xiying Zhang et\u00a0al. 2023. MCANet: shared-weight-based MultiheadCrossAttention network for drug\u2013target interaction prediction. Brief Bioinform 24 2 (2023) bbad082. 10.1093\/bib\/bbad082","DOI":"10.1093\/bib\/bbad082"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"publisher","unstructured":"Nadav Brandes Dan Ofer Yam Peleg et\u00a0al. 2022. ProteinBERT: a universal deep-learning model of protein sequence and function. Bioinformatics 38 8 (2022) 2102\u20132110. 10.1093\/bioinformatics\/btac020","DOI":"10.1093\/bioinformatics\/btac020"},{"key":"e_1_3_3_1_5_2","unstructured":"Seyone Chithrananda Gabriel Grand and Bharath Ramsundar. 2020. ChemBERTa: large-scale self-supervised pretraining for molecular property prediction. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2010.09885 (2020). https:\/\/doi.org\/arXiv:2010.09885v2"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"publisher","unstructured":"John Jumper Richard Evans Alexander Pritzel et\u00a0al. 2021. Highly accurate protein structure prediction with AlphaFold. nature 596 7873 (2021) 583\u2013589. 10.1038\/s41586-021-03819-2","DOI":"10.1038\/s41586-021-03819-2"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"publisher","unstructured":"Shuichi Kawashima and Minoru Kanehisa. 2000. AAindex: Amino Acid Index Database. Nucleic acids research 27 1 (2000) 368\u2013369. 10.1093\/nar\/27.1.368","DOI":"10.1093\/nar\/27.1.368"},{"key":"e_1_3_3_1_8_2","unstructured":"Gregory Landrum. 2019. RDKit: Open-source cheminformatics. http:\/\/www.rdkit.org."},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","unstructured":"Jonghyun Lee Dae\u00a0Won Jun Ildae Song et\u00a0al. 2024. DLM-DTI: a dual language model for the prediction of drug-target interaction with hint-based learning. J Cheminform 16 1 (2024) 14. 10.1186\/s13321-024-00808-1","DOI":"10.1186\/s13321-024-00808-1"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","unstructured":"M. Li J. Zhou J. Hu et\u00a0al. 2021. DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science. ACS Omega 6 41 (2021) 27233\u201327238. 10.1021\/acsomega.1c04017","DOI":"10.1021\/acsomega.1c04017"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","unstructured":"Xiaokun Li Qiang Yang Long Xu et\u00a0al. 2024. DrugMGR: a deep bioactive molecule binding method to identify compounds targeting proteins. Bioinformatics 40 4 (2024) btae176. 10.1093\/bioinformatics\/btae176","DOI":"10.1093\/bioinformatics\/btae176"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"publisher","unstructured":"Zongquan Li Pengxuan Ren Hao Yang et\u00a0al. 2024. TEFDTA: a transformer encoder and fingerprint representation combined prediction method for bonded and non-bonded drug\u2013target affinities. Bioinformatics 40 1 (2024) btad778. 10.1093\/bioinformatics\/btad778","DOI":"10.1093\/bioinformatics\/btad778"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"publisher","unstructured":"Zeming Lin Halil Akin Roshan Rao et\u00a0al. 2023. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379 6637 (2023) 1123\u20131130. 10.1126\/science.ade2574","DOI":"10.1126\/science.ade2574"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","unstructured":"Zhangli Lu Guoqiang Song Huimin Zhu et\u00a0al. 2025. DTIAM: a unified framework for predicting drug-target interactions binding affinities and drug mechanisms. Nat Commun 16 1 (2025) 2548. 10.1038\/s41467-025-57828-0","DOI":"10.1038\/s41467-025-57828-0"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"publisher","unstructured":"Nelson\u00a0RC Monteiro Jos\u00e9\u00a0L Oliveira and Joel\u00a0P Arrais. 2022. DTITR: End-to-end drug\u2013target binding affinity prediction with transformers. Comput Biol Med 147 (2022) 105772. 10.1016\/j.compbiomed.2022.105772","DOI":"10.1016\/j.compbiomed.2022.105772"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"publisher","unstructured":"Feng Pan Chong Yin Si-Qi Liu et\u00a0al. 2024. BindingSiteDTI: differential-scale binding site modelling for drug\u2013target interaction prediction. Bioinformatics 40 5 (2024) btae308. 10.1093\/bioinformatics\/btae308","DOI":"10.1093\/bioinformatics\/btae308"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"publisher","unstructured":"Fred\u00a0Zhangzhi Peng Chentong Wang Tong Chen et\u00a0al. 2025. PTM-Mamba: A ptm-aware protein language model with bidirectional gated mamba blocks. Nat Methods 22 (2025) 945\u2013949. 10.1038\/s41592-025-02656-9","DOI":"10.1038\/s41592-025-02656-9"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","unstructured":"Lihong Peng Xin Liu Long Yang et\u00a0al. 2025. BINDTI: a bi-directional intention network for drug-target interaction identification based on attention mechanisms. IEEE J Biomed Health Inform 29 3 (2025) 1602\u20131612. 10.1109\/JBHI.2024.3375025","DOI":"10.1109\/JBHI.2024.3375025"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","unstructured":"David Rogers and Mathew Hahn. 2010. Extended-connectivity fingerprints. J Chem Inf Model 50 5 (2010) 742\u2013754. 10.1021\/ci100050t","DOI":"10.1021\/ci100050t"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"publisher","unstructured":"Min-Gang Su Julia Tzu-Ya Weng Justin Bo-Kai Hsu et\u00a0al. 2017. Investigation and identification of functional post-translational modification sites associated with drug binding and protein-protein interactions. BMC Syst Biol 11 (2017) 69\u201380. 10.1186\/s12918-017-0506-1","DOI":"10.1186\/s12918-017-0506-1"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"publisher","unstructured":"Fei Wang Xianglong Cheng Xin Xia et\u00a0al. 2024. Adaptive space search-based molecular evolution optimization algorithm. Bioinformatics 40 7 (2024) btae446. 10.1093\/bioinformatics\/btae446","DOI":"10.1093\/bioinformatics\/btae446"},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"publisher","unstructured":"Fei Wang Yulian Ding Xiujuan Lei et\u00a0al. 2022. Machine learning and deep learning strategies in drug repositioning. Curr Bioinform 17 3 (2022) 217\u2013237. 10.2174\/1574893616666211119093100","DOI":"10.2174\/1574893616666211119093100"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"publisher","unstructured":"Fei Wang Xiujuan Lei Bo Liao et\u00a0al. 2022. Predicting drug\u2013drug interactions by graph convolutional network with multi-kernel. Brief Bioinform 23 1 (2022) bbab511. 10.1093\/bib\/bbab511","DOI":"10.1093\/bib\/bbab511"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"publisher","unstructured":"Jingru Wang Yihang Xiao Xuequn Shang et\u00a0al. 2024. Predicting drug\u2013target binding affinity with cross-scale graph contrastive learning. Brief Bioinform 25 1 (2024) bbad516. 10.1093\/bib\/bbad516","DOI":"10.1093\/bib\/bbad516"},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"publisher","unstructured":"Hongjie Wu Junkai Liu Tengsheng Jiang et\u00a0al. 2024. AttentionMGT-DTA: A multi-modal drug-target affinity prediction using graph transformer and attention mechanism. Neural Netw 169 (2024) 623\u2013636. 10.1016\/j.neunet.2023.11.018","DOI":"10.1016\/j.neunet.2023.11.018"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"publisher","unstructured":"Jana Zecha Florian\u00a0P Bayer Svenja Wiechmann et\u00a0al. 2023. Decrypting drug actions and protein modifications by dose-and time-resolved proteomics. Science 380 6640 (2023) 93\u2013101. 10.1126\/science.ade3925","DOI":"10.1126\/science.ade3925"},{"key":"e_1_3_3_1_27_2","doi-asserted-by":"publisher","unstructured":"Xiaoting Zeng Weilin Chen and Baiying Lei. 2024. CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction. BMC Bioinformatics 25 1 (2024) 141. 10.1186\/s12859-024-05753-2","DOI":"10.1186\/s12859-024-05753-2"},{"key":"e_1_3_3_1_28_2","doi-asserted-by":"publisher","unstructured":"Bo-Wei Zhao Xiao-Rui Su Peng-Wei Hu et\u00a0al. 2023. iGRLDTI: an improved graph representation learning method for predicting drug\u2013target interactions over heterogeneous biological information network. Bioinformatics 39 8 (2023) btad451. 10.1093\/bioinformatics\/btad451","DOI":"10.1093\/bioinformatics\/btad451"},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"publisher","unstructured":"Haitao Zhao Wei Chen Hao Huang et\u00a0al. 2023. A robotic platform for the synthesis of colloidal nanocrystals. Nat Synth 2 6 (2023) 505\u2013514. 10.1038\/s44160-023-00250-5","DOI":"10.1038\/s44160-023-00250-5"},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"publisher","unstructured":"Ying Zhou Yintao Zhang Donghai Zhao et\u00a0al. 2024. TTD: therapeutic target database describing target druggability information. Nucleic Acids Res 52 D1 (2024) D1465\u2013D1477. 10.1093\/nar\/gkad751","DOI":"10.1093\/nar\/gkad751"},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"publisher","unstructured":"Zhecheng Zhou Qingquan Liao Jinhang Wei et\u00a0al. 2024. Revisiting drug\u2013protein interaction prediction: a novel global\u2013local perspective. Bioinformatics 40 5 (2024) btae271. 10.1093\/bioinformatics\/btae271","DOI":"10.1093\/bioinformatics\/btae271"}],"event":{"name":"BCB '26: 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics","location":"Rende (CS) Italy","acronym":"BCB '26","sponsor":["SIGBio ACM Special Interest Group on Bioinformatics"]},"container-title":["Proceedings of the 17th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3807503.3819370","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T15:14:17Z","timestamp":1785338057000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3807503.3819370"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,30]]},"references-count":30,"alternative-id":["10.1145\/3807503.3819370","10.1145\/3807503"],"URL":"https:\/\/doi.org\/10.1145\/3807503.3819370","relation":{},"subject":[],"published":{"date-parts":[[2026,6,30]]},"assertion":[{"value":"2026-07-28","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}