{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:42:19Z","timestamp":1753875739359,"version":"3.41.2"},"reference-count":54,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T00:00:00Z","timestamp":1736726400000},"content-version":"vor","delay-in-days":52,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001321","name":"National Research Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100014188","name":"MSIT","doi-asserted-by":"publisher","award":["RS-2023-00229822"],"award-info":[{"award-number":["RS-2023-00229822"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,11,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Identifying new compounds that interact with a target is a crucial time-limiting step in the initial phases of drug discovery. Compound\u2013protein complex structure-based affinity prediction models can expedite this process; however, their dependence on high-quality three-dimensional (3D) complex structures limits their practical application. Prediction models that do not require 3D complex structures for binding-affinity estimation offer a theoretically attractive alternative; however, accurately predicting affinity without interaction information presents significant challenges. We introduce BlendNet, a framework that employs a knowledge transfer strategy to improve affinity prediction accuracy by learning the interdependent relationships between compounds and proteins without relying on 3D complex structures. Compared with state-of-the-art models for affinity prediction, BlendNet demonstrated superior performance across various cold-start cases. The ability of BlendNet to interpret compound\u2013protein interactions without utilizing complex structure data highlights its potential to accelerate and streamline drug development.<\/jats:p>","DOI":"10.1093\/bib\/bbae712","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T13:44:59Z","timestamp":1736775899000},"source":"Crossref","is-referenced-by-count":0,"title":["Exploring the potential of compound\u2013protein complex structure-free models in virtual screening using BlendNet"],"prefix":"10.1093","volume":"26","author":[{"given":"Sangmin","family":"Seo","sequence":"first","affiliation":[{"name":"Department of Computer Science, Yonsei University , Yonsei-ro 50, Seodaemun-gu, 03722, Seoul ,","place":["Republic of Korea"]},{"name":"UBLBio Corporation , Yeongtong-ro 237, Suwon, 16679, Gyeonggi-do ,","place":["Republic of Korea"]}]},{"given":"Hwanhee","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Computer Science, 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