{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T15:50:00Z","timestamp":1773589800190,"version":"3.50.1"},"reference-count":51,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T00:00:00Z","timestamp":1771200000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Bio & Medical Technology Development Program of the National Research Foundation"},{"name":"Korean government"},{"DOI":"10.13039\/501100002551","name":"Seoul National University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002551","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Predicting immunoglobulin\u2013antigen (Ig\u2013Ag) binding remains a significant challenge due to the paucity of experimentally resolved complexes and the limited accuracy of de novo Ig structure prediction.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We introduce IgPose, a generalizable framework for Ig\u2013Ag pose identification and scoring, built on a generative data-augmentation pipeline. To mitigate data scarcity, we constructed the Structural Immunoglobulin Decoy Database (SIDD), a comprehensive repository of high-fidelity synthetic decoys. IgPose integrates equivariant graph neural networks, ESM-2 embeddings, and gated recurrent units to synergistically capture both geometric and evolutionary features. We implemented interface-focused k-hop sampling with biologically guided pooling to enhance generalization across diverse interfaces. The framework comprises two sub-networks\u2014IgPoseClassifier for binding pose discrimination and IgPoseScore for DockQ score estimation\u2014and achieves robust performance on curated internal test sets and the CASP-16 benchmark compared to physics and deep learning baselines. IgPose serves as a versatile computational tool for high-throughput antibody discovery pipelines by providing accurate pose filtering and ranking.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>IgPose is available on GitHub (https:\/\/github.com\/arontier\/igpose).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag076","type":"journal-article","created":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T12:44:35Z","timestamp":1770900275000},"source":"Crossref","is-referenced-by-count":0,"title":["IgPose: a generative data-augmented pipeline for robust immunoglobulin\u2013antigen binding prediction"],"prefix":"10.1093","volume":"42","author":[{"given":"Tien-Cuong","family":"Bui","sequence":"first","affiliation":[{"name":"Arontier Co., Ltd. R&D Department, , Seoul, 06735,","place":["Republic of Korea"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2902-4677","authenticated-orcid":false,"given":"Injae","family":"Chung","sequence":"additional","affiliation":[{"name":"Arontier Co., Ltd. R&D Department, , Seoul, 06735,","place":["Republic of Korea"]}]},{"given":"Wonjun","family":"Lee","sequence":"additional","affiliation":[{"name":"Arontier Co., Ltd. R&D Department, , Seoul, 06735,","place":["Republic of Korea"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7416-4591","authenticated-orcid":false,"given":"Junsu","family":"Ko","sequence":"additional","affiliation":[{"name":"Arontier Co., Ltd. R&D Department, , Seoul, 06735,","place":["Republic of Korea"]}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1174-4358","authenticated-orcid":false,"given":"Juyong","family":"Lee","sequence":"additional","affiliation":[{"name":"Arontier Co., Ltd. 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