{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:44:35Z","timestamp":1792799075818,"version":"4.5.0"},"reference-count":39,"publisher":"Oxford University Press (OUP)","issue":"9","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62572185"],"award-info":[{"award-number":["62572185"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Hunan Province, China","award":["2024JJ7115"],"award-info":[{"award-number":["2024JJ7115"]}]},{"name":"Excellent Young Scholars Fund of the Education Department of Hunan Province, China","award":["25B0835"],"award-info":[{"award-number":["25B0835"]}],"id":[{"id":"https:\/\/ror.org\/05ckg3w11","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Cold-start drug\u2013target affinity prediction remains challenging because static interaction mechanisms cannot adapt to individual drug\u2013target pairs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>We propose PCIM-DTA, which constructs pair-level interaction representations and derives a pair-specific condition vector from global drug and target features. The condition vector modulates attention, pair-token features, distribution-aware recalibration, and regression parameters, while graph message passing captures higher-order dependencies. Experiments on Davis and BindingDB-Kd show that PCIM-DTA achieves competitive or superior performance under Warm, Cold-drug, Cold-target, Cold-both, and Scaffold-drug settings. Ablation studies support the contribution of each component.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and Implementation<\/jats:title>\n                    <jats:p>The datasets used in this study are publicly available, including the Davis and BindingDB-Kd datasets. The implementation code of PCIM-DTA is publicly available at https:\/\/github.com\/1322469934\/PCIM-DTA.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag646","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T14:10:49Z","timestamp":1788185449000},"source":"Crossref","is-referenced-by-count":4,"title":["PCIM-DTA: pairwise conditional interaction modeling for drug\u2013target affinity prediction under cold-start scenarios"],"prefix":"10.1093","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0402-6537","authenticated-orcid":false,"given":"Ziyou","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Hunan Institute of Technology , Hengyang, Hunan 421002,","place":["China"]},{"name":"Hunan Chuanshan Electronic Technology Co., Ltd , Hengyang, Hunan 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