{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T01:01:15Z","timestamp":1791939675949,"version":"4.3.4"},"reference-count":26,"publisher":"American Chemical Society (ACS)","issue":"17","license":[{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["CFA-205059"],"award-info":[{"award-number":["CFA-205059"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["PJT 190272"],"award-info":[{"award-number":["PJT 190272"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["PJT204042"],"award-info":[{"award-number":["PJT204042"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000024","name":"Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["PLL 185683"],"award-info":[{"award-number":["PLL 185683"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000196","name":"Canada Foundation for Innovation","doi-asserted-by":"publisher","award":["43481"],"award-info":[{"award-number":["43481"]}],"id":[{"id":"10.13039\/501100000196","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001804","name":"Canada Research Chairs","doi-asserted-by":"publisher","award":["CRC-2021-00482"],"award-info":[{"award-number":["CRC-2021-00482"]}],"id":[{"id":"10.13039\/501100001804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["ALLRP 602759-24"],"award-info":[{"award-number":["ALLRP 602759-24"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2026-07632"],"award-info":[{"award-number":["RGPIN-2026-07632"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Molecular property prediction is a key task in AI-driven drug discovery, yet the prevalence and impact of label imbalances in molecular property regression remain poorly understood. Through a systematic benchmark of widely used molecular property data sets, we show that target values are often highly imbalanced and that prediction errors are consistently concentrated in sparsely represented regions of the label space. We further evaluate representative imbalance-learning approaches developed for general regression tasks and find that their effectiveness on molecular data sets is limited. To address this challenge, we propose interval-aware mixture-of-experts (IA-MoE), a plug-in framework that partitions the continuous target space into intervals and promotes expert specialization across different regions of the label distribution. IA-MoE can be seamlessly integrated with diverse molecular encoders without modifying their underlying architectures. Experiments on five molecular property benchmarks and four graph neural network backbones demonstrate that IA-MoE consistently improves the predictive performance in underrepresented regions while maintaining or improving overall accuracy. Our findings establish label imbalance as an important challenge in molecular property regression and highlight interval-aware expert learning as an effective strategy for addressing this challenge.<\/jats:p>","DOI":"10.1021\/acs.jcim.6c01847","type":"journal-article","created":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T05:11:26Z","timestamp":1788325886000},"page":"10795-10805","source":"Crossref","is-referenced-by-count":1,"title":["Toward Imbalanced\nMolecular Property Regression: A\nBenchmark Study and Interval-Aware Mixture of Experts"],"prefix":"10.1021","volume":"66","author":[{"given":"Yan","family":"Sun","sequence":"first","affiliation":[{"name":"Western University , , 1400 Western Road , , ,","place":["London, ON, Canada, N6G 2 V4"]},{"name":"Western University , , 1151 Richmond Street , , ,","place":["London, ON, Canada, N6A 3K7"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Shi","sequence":"additional","affiliation":[{"name":"University of Toronto , , 155 College Street , , ,","place":["Toronto, ON, Canada, M5T 3M7"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alana","family":"Deng","sequence":"additional","affiliation":[{"name":"Simon Fraser University , , , ,","place":["Burnaby, BC, Canada, V5A 1S6"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zihao","family":"Jing","sequence":"additional","affiliation":[{"name":"Western University , , 1151 Richmond Street , , ,","place":["London, ON, Canada, N6A 3K7"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7541-9127","authenticated-orcid":true,"given":"Carson","family":"Leung","sequence":"additional","affiliation":[{"name":"University of Manitoba , , , ,","place":["Winnipeg, MB, Canada, R3T 2N2"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9546-2245","authenticated-orcid":true,"given":"Pingzhao","family":"Hu","sequence":"additional","affiliation":[{"name":"Western University , , 1400 Western Road , , ,","place":["London, ON, Canada, N6G 2 V4"]},{"name":"Western University , , 1151 Richmond Street , , ,","place":["London, ON, Canada, N6A 3K7"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,8,28]]},"reference":[{"key":"2026091409081194300_cit1","unstructured":"Gilmer, J.; Schoenholz, S. 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ChEMBL 6019, 2015."},{"key":"2026091409081194300_cit15","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.addr.2012.09.019","article-title":"Experimental and\ncomputational approaches to estimate solubility and permeability in\ndrug discovery and development settings","volume":"64","author":"Lipinski","year":"2012","journal-title":"Adv.\nDrug Delivery Rev."},{"key":"2026091409081194300_cit16","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1007\/978-3-642-40669-0_33","article-title":"Smote for regression","volume-title":"Progress in Artificial Intelligence","author":"Torgo","year":"2013"},{"key":"2026091409081194300_cit17","unstructured":"Branco, P.; Torgo, L.; Ribeiro, R. P.\n          SMOGN: a pre-processing approach\nfor imbalanced regression. In  First international\nworkshop on learning with imbalanced domains: Theory and applications2017; pp 36\u201350."},{"key":"2026091409081194300_cit18","unstructured":"Nogueira, B.; Jiang, M.; Chawla, N. 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