{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:36:40Z","timestamp":1761176200985,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Machine-learned interactions between drugs and human protein targets play a crucial role in efficient and accurate drug discovery. However, the drug-target mechanism of action (DTMoA) prediction is actually a multi-class classification problem, which follows a long-tailed class distribution. Existing methods simply address whether the drugs and targets can interact and rarely consider these deep mechanisms. In this paper, we introduce TED-DTMoA, a novel DTMoA prediction framework that incorporates the divide-and-conquer strategy with tri-comparison options. Specifically, to reduce the learning difficulty of tail classes, we propose an expertise-based divide-and-conquer decision approach that combines the results of multiple independent expertise models for sub-tasks decomposed from the original prediction task. In addition, to enhance the discrimination of similar mechanism classes, we devise a tri-comparison learning strategy that defines the sub-task as the classification of triple options, such as expanding the classification task for classes A and B to include an extra \u201cNeither of them\u201d option. Extensive experiments conducted on various DTMoA datasets quantitatively demonstrate the proposed method achieves an approximately 13% performance improvement compared with advanced baselines. Moreover, our method exhibits an obvious superiority on the tail classes. Further analysis of the evolvability and generalization reveals the significant potential to be deployed in real-world scenes.<\/jats:p>","DOI":"10.3233\/faia251079","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:50:51Z","timestamp":1761126651000},"source":"Crossref","is-referenced-by-count":0,"title":["TED-DTMoA: Tri-Comparison Expertise Decision for Drug-Target Mechanism of Action"],"prefix":"10.3233","author":[{"given":"Lingxiang","family":"Jia","sequence":"first","affiliation":[{"name":"State Key Laboratory of Blockchain and Data Security, Zhejiang University"},{"name":"Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security"}]},{"given":"Zipeng","family":"Zhong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Blockchain and Data Security, Zhejiang University"}]},{"given":"Shaolun","family":"Yao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Blockchain and Data Security, Zhejiang University"}]},{"given":"Jie","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Blockchain and Data Security, Zhejiang University"},{"name":"School of Software Technology, Zhejiang University"},{"name":"Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security"}]},{"given":"Mingli","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Blockchain and Data Security, Zhejiang University"},{"name":"Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security"}]},{"given":"Zunlei","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Blockchain and Data Security, Zhejiang University"},{"name":"School of Software Technology, Zhejiang University"},{"name":"Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA251079","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:50:51Z","timestamp":1761126651000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA251079"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia251079","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}