{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T21:53:00Z","timestamp":1770846780266,"version":"3.50.1"},"reference-count":34,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T00:00:00Z","timestamp":1765756800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,12,15]]},"DOI":"10.1109\/bibm66473.2025.11356892","type":"proceedings-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T21:19:40Z","timestamp":1769721580000},"page":"715-720","source":"Crossref","is-referenced-by-count":0,"title":["Phi-Former: A Pairwise Hierarchical Approach for Compound-Protein Interactions Prediction"],"prefix":"10.1109","author":[{"given":"Zhe","family":"Wang","sequence":"first","affiliation":[{"name":"Hong Kong University of Science and Technology,Hong Kong,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zijing","family":"Liu","sequence":"additional","affiliation":[{"name":"International Digital Economy Academy (IDEA),Shenzhen,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chencheng","family":"Xu","sequence":"additional","affiliation":[{"name":"Princeton University,Princeton,NJ,USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Yao","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology,Department of Mathematics,Hong Kong,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2022.02.023"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2021.03.004"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1038\/msb.2011.5"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/978-1-60761-274-2_10","volume-title":"Chemogenomics: Methods and Applications","author":"Brennan","year":"2009"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1002\/jmr.744"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1021\/ct800011m"},{"key":"ref7","first-page":"20503","article-title":"Equibind: Geometric deep learning for drug binding structure prediction","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"St\u00e4rk","year":"2022"},{"key":"ref8","doi-asserted-by":"crossref","DOI":"10.1101\/2022.06.06.495043","article-title":"Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction","volume-title":"bioRxiv","author":"Lu","year":"2022"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1021\/acsomega.3c00085"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac446"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1609.02907"},{"key":"ref12","first-page":"1024","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref13","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Gilmer","year":"2017"},{"key":"ref14","article-title":"How powerful are graph neural networks?","author":"Xu","year":"2018","journal-title":"arXiv preprint"},{"key":"ref15","first-page":"28877","article-title":"Do transformers really perform badly for graph representation?","volume":"34","author":"Ying","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref16","article-title":"One transformer can understand both 2d & 3d molecular data","author":"Luo","year":"2022","journal-title":"arXiv preprint"},{"key":"ref17","doi-asserted-by":"crossref","DOI":"10.26434\/chemrxiv-2022-jjm0j-v4","article-title":"Uni-mol: A universal 3d molecular representation learning framework","volume-title":"Proc. Int. Conf. Learn. Represent.","author":"Zhou","year":"2023"},{"key":"ref18","first-page":"25581","article-title":"Molecular representation learning via heterogeneous motif graph neural networks","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Yu","year":"2022"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1038\/s42004-023-00825-5"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2023.107844"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0258628"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btp433"},{"key":"ref23","doi-asserted-by":"crossref","DOI":"10.1101\/086033","article-title":"Deep learning with feature embedding for compound-protein interaction prediction","volume-title":"bioRxiv","author":"Wan","year":"2016"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1021\/ci00057a005"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1016\/j.cels.2020.03.002"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.1c01830"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btaa921"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1021\/acsomega.9b01997"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab527"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.7b00650"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467311"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00026"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1021\/jm030580l"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00545"}],"event":{"name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","location":"Wuhan, China","start":{"date-parts":[[2025,12,15]]},"end":{"date-parts":[[2025,12,18]]}},"container-title":["2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11355913\/11355975\/11356892.pdf?arnumber=11356892","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T20:54:20Z","timestamp":1770843260000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11356892\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,15]]},"references-count":34,"URL":"https:\/\/doi.org\/10.1109\/bibm66473.2025.11356892","relation":{},"subject":[],"published":{"date-parts":[[2025,12,15]]}}}