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However, novel drug development constantly generates new DDIE classes that suffer from a severe scarcity of labels, making zero-shot learning an essential solution for emerging cases. Unfortunately, the long-tailed distribution of existing DDI datasets causes overfitting to head classes and poor generalization to tail classes, thereby severely impairing zero-shot performance. To address this limitation, we propose a novel framework, ProAlign-DDI, which integrates a Substructure-Guided Semantic Adaptive Aggregation Module (SAAM) with a Prototype Alignment Module (PAM). In this framework, SAAM adaptively captures task-relevant features to produce high-quality aggregated representations. Subsequently, PAM employs class-level semantic prototypes to regularize these representations. This alignment strategy not only optimizes the global feature space but also alleviates the bias inherent in long-tailed DDI datasets. Comprehensive experiments show that ProAlign-DDI achieves competitive performance in conventional zero-shot learning and more balanced performance in generalized zero-shot learning, confirming its effectiveness in inferring unseen DDIE classes.<\/jats:p>","DOI":"10.1021\/acs.jcim.6c01621","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T16:02:26Z","timestamp":1788969746000},"page":"11670-11682","source":"Crossref","is-referenced-by-count":0,"title":["ProAlign-DDI: A\nZero-Shot Drug\u2013Drug Interaction\nEvent Prediction Framework with Prototype Alignment"],"prefix":"10.1021","volume":"66","author":[{"given":"Zhaofeng","family":"Niu","sequence":"first","affiliation":[{"name":"Qufu Normal University , , ,","place":["Rizhao, China, 276826"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoya","family":"Zhao","sequence":"additional","affiliation":[{"name":"Qufu Normal University , , ,","place":["Rizhao, China, 276826"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqi","family":"Fan","sequence":"additional","affiliation":[{"name":"Qufu Normal University , , ,","place":["Rizhao, China, 276826"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuzhuo","family":"Yuan","sequence":"additional","affiliation":[{"name":"Qufu Normal University , , ,","place":["Rizhao, China, 276826"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Defu","family":"Qiu","sequence":"additional","affiliation":[{"name":"Qufu Normal University , , ,","place":["Rizhao, China, 276826"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4565-6031","authenticated-orcid":true,"given":"Daohui","family":"Ge","sequence":"additional","affiliation":[{"name":"Qufu Normal University , , ,","place":["Rizhao, China, 276826"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"2026092810080311600_cit1","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2407.00891","article-title":"Zeroddi: A zero-shot drug-drug interaction event\nprediction method with semantic enhanced learning and dual-modal uniform\nalignment","author":"Wang","year":"2024","journal-title":"arXiv"},{"key":"2026092810080311600_cit2","doi-asserted-by":"publisher","first-page":"3997","DOI":"10.1038\/s41467-025-59431-9","article-title":"Predicting rare drug-drug interaction events with dual-granular\nstructure-adaptive and pair variational representation","volume":"16","author":"Ren","year":"2025","journal-title":"Nat. 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