{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T14:00:19Z","timestamp":1780322419013,"version":"3.54.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T00:00:00Z","timestamp":1759104000000},"content-version":"vor","delay-in-days":29,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Starlit South Lake Leading Elite Program","award":["2023A303005"],"award-info":[{"award-number":["2023A303005"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82204772"],"award-info":[{"award-number":["82204772"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LD25H090002"],"award-info":[{"award-number":["LD25H090002"]}]},{"name":"\u2018Pioneer\u2019 and \u2018Leading Goose\u2019 R&D Program of Zhejiang","award":["2024C03106"],"award-info":[{"award-number":["2024C03106"]}]},{"name":"Noncommunicable Chronic Diseases-National Science and Technology Major Project","award":["2024ZD0530704"],"award-info":[{"award-number":["2024ZD0530704"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,8,31]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties are critical determinants of the pharmacokinetic and safety profiles of drug candidates. Accurate and early-stage prediction of ADMET characteristics is essential for reducing late-stage attrition rates, lowering development costs, and accelerating the drug discovery process. Recent advances in deep learning have shown great promise in molecular property prediction, especially with the emergence of Transformer-based architectures that can effectively model long-range dependencies in molecular representations. However, most existing methods rely heavily on atom-level encodings (e.g. smiles or molecular graphs), which often lack structural interpretability and generalization across heterogeneous tasks. Previously, we developed a de novo and flexible molecular representation framework named MSformer (available at https:\/\/github.com\/ZJUFanLab\/MSformer), which demonstrated success in bioactivity prediction. We have now adapted and specialized this architecture for ADMET property prediction. This adapted implementation, designated as MSformer-ADMET, extends the framework\u2019s capabilities to pharmacokinetic and toxicity endpoints while maintaining its flexible, fragmentation-based approach to molecular representation learning. MSformer-ADMET is fine-tuned on 22 tasks collected from the Therapeutics Data Commons (TDC), covering both classification and regression settings. Results demonstrate that MSformer-ADMET achieves superior performance across a wide range of ADMET endpoints, consistently outperforming conventional smiles-based and graph-based models. Notably, we further conducted interpretability analyses by leveraging the model\u2019s attention distributions and fragment-to-atom mappings, allowing the identification of key structural fragments that are highly associated with molecular properties. This post hoc interpretability provides more transparent insights into the structure\u2013property relationship. Collectively, results demonstrate that MSformer-ADMET is a highly effective and broadly applicable model for ADMET prediction.<\/jats:p>","DOI":"10.1093\/bib\/bbaf506","type":"journal-article","created":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T14:37:25Z","timestamp":1759156645000},"source":"Crossref","is-referenced-by-count":3,"title":["Advancing ADMET prediction through multiscale fragment-aware pretraining with MSformer-ADMET"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-7421-3316","authenticated-orcid":false,"given":"Huihui","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Sciences , , Zhejiang University, Hangzhou, 310058 ,","place":["China"]},{"name":"College of Pharmaceutical Sciences , , Zhejiang University, Hangzhou, 310058 ,","place":["China"]},{"name":"Zhejiang Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University , Jiaxing, 314100 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Zhejiang University, Hangzhou, 310058 ,","place":["China"]},{"name":"College of Pharmaceutical Sciences , , Zhejiang University, Hangzhou, 310058 ,","place":["China"]},{"name":"Zhejiang Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University , Jiaxing, 314100 ,","place":["China"]},{"name":"State Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University , Jiaxing, 314100 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanrong","family":"Zheng","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Sciences , , Zhejiang University, Hangzhou, 310058 ,","place":["China"]},{"name":"College of Pharmaceutical Sciences , , Zhejiang University, Hangzhou, 310058 ,","place":["China"]},{"name":"Zhejiang Collaborative Innovation Center for the Brain Diseases with Integrative Medicine, Zhejiang Key Laboratory of 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