{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T22:38:50Z","timestamp":1769639930096,"version":"3.49.0"},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"vor","delay-in-days":16,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute of Information & Communications Technology Planning & Evaluation"},{"name":"Korean government"},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Korean government","award":["2018R1A5A1060031"],"award-info":[{"award-number":["2018R1A5A1060031"]}]},{"name":"Korean government","award":["2022R1F1A1065664"],"award-info":[{"award-number":["2022R1F1A1065664"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The usefulness of supervised molecular property prediction (MPP) is well-recognized in many applications. However, the insufficiency and the imbalance of labeled data make the learning problem difficult. Moreover, the reliability of the predictions is also a huddle in the deployment of MPP models in safety-critical fields.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose the Evidential Meta-model for Molecular Property Prediction (EM3P2) method that returns uncertainty estimates along with its predictions. Our EM3P2 trains an evidential graph isomorphism network classifier using multi-task molecular property datasets under the model-agnostic meta-learning (MAML) framework while addressing the problem of data imbalance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>\u2003<\/jats:title>\n                  <jats:p>Our results showed better prediction performances compared to existing meta-MPP models. Furthermore, we showed that the uncertainty estimates returned by our EM3P2 can be used to reject uncertain predictions for applications that require higher confidence.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Source code available for download at https:\/\/github.com\/Ajou-DILab\/EM3P2.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad604","type":"journal-article","created":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T19:46:34Z","timestamp":1697571994000},"source":"Crossref","is-referenced-by-count":3,"title":["Evidential meta-model for molecular property prediction"],"prefix":"10.1093","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2632-7648","authenticated-orcid":false,"given":"Kyung Pyo","family":"Ham","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence, Ajou University , Suwon 16499, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9066-5756","authenticated-orcid":false,"given":"Lee","family":"Sael","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence, Ajou University , Suwon 16499, Republic of Korea"},{"name":"Department of Software and Computer Engineering, Ajou University , Suwon 16499, Republic of Korea"}]}],"member":"286","published-online":{"date-parts":[[2023,10,17]]},"reference":[{"key":"2023102421212950600_btad604-B1","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1021\/acscentsci.6b00367","article-title":"Low data drug discovery with one-shot learning","volume":"3","author":"Altae-Tran","year":"2017","journal-title":"ACS Cent Sci"},{"key":"2023102421212950600_btad604-B2","first-page":"13329","author":"Bao","year":"2021"},{"key":"2023102421212950600_btad604-B3","first-page":"1","author":"Crisostomi","year":"2022"},{"key":"2023102421212950600_btad604-B4","first-page":"73","author":"Dempster","year":"2008"},{"key":"2023102421212950600_btad604-B5","first-page":"1126","author":"Finn","year":"2017"},{"key":"2023102421212950600_btad604-B6","first-page":"1321","author":"Guo","year":"2017"},{"key":"2023102421212950600_btad604-B7","first-page":"2559","author":"Guo","year":"2021"},{"key":"2023102421212950600_btad604-B8","first-page":"1621","volume-title":"Jeju Island, Republic of Korea","author":"Ham","year":"2022"},{"key":"2023102421212950600_btad604-B9","first-page":"5149","article-title":"Meta-learning in neural networks: a survey","volume":"44","author":"Hospedales","year":"2021","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2023102421212950600_btad604-B10","author":"Hu","year":"2020"},{"key":"2023102421212950600_btad604-B11","doi-asserted-by":"crossref","first-page":"4573","DOI":"10.1093\/bioinformatics\/btac550","article-title":"MultiGran-SMILES: multi-granularity SMILES learning for molecular property prediction","volume":"38","author":"Jiang","year":"2022","journal-title":"Bioinformatics"},{"key":"2023102421212950600_btad604-B12","volume-title":"Subjective Logic. 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