{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T07:10:00Z","timestamp":1773472200459,"version":"3.50.1"},"reference-count":85,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T00:00:00Z","timestamp":1648857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100005150","name":"Chinese Academy of Medical Sciences","doi-asserted-by":"publisher","award":["2021-I2M-1-056"],"award-info":[{"award-number":["2021-I2M-1-056"]}],"id":[{"id":"10.13039\/501100005150","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFC0901901"],"award-info":[{"award-number":["2016YFC0901901"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2017YFC0907503"],"award-info":[{"award-number":["2017YFC0907503"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81601573"],"award-info":[{"award-number":["81601573"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Computational methods have been widely applied to resolve various core issues in drug discovery, such as molecular property prediction. In recent years, a data-driven computational method-deep learning had achieved a number of impressive successes in various domains. In drug discovery, graph neural networks (GNNs) take molecular graph data as input and learn graph-level representations in non-Euclidean space. An enormous amount of well-performed GNNs have been proposed for molecular graph learning. Meanwhile, efficient use of molecular data during training process, however, has not been paid enough attention. Curriculum learning (CL) is proposed as a training strategy by rearranging training queue based on calculated samples' difficulties, yet the effectiveness of CL method has not been determined in molecular graph learning. In this study, inspired by chemical domain knowledge and task prior information, we proposed a novel CL-based training strategy to improve the training efficiency of molecular graph learning, called CurrMG. Consisting of a difficulty measurer and a training scheduler, CurrMG is designed as a plug-and-play module, which is model-independent and easy-to-use on molecular data. Extensive experiments demonstrated that molecular graph learning models could benefit from CurrMG and gain noticeable improvement on five GNN models and eight molecular property prediction tasks (overall improvement is 4.08%). We further observed CurrMG\u2019s encouraging potential in resource-constrained molecular property prediction. These results indicate that CurrMG can be used as a reliable and efficient training strategy for molecular graph learning.<\/jats:p><jats:p>Availability: The source code is available in https:\/\/github.com\/gu-yaowen\/CurrMG.<\/jats:p>","DOI":"10.1093\/bib\/bbac099","type":"journal-article","created":{"date-parts":[[2022,3,2]],"date-time":"2022-03-02T12:07:56Z","timestamp":1646222876000},"source":"Crossref","is-referenced-by-count":19,"title":["An efficient curriculum learning-based strategy for molecular graph learning"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0924-5939","authenticated-orcid":false,"given":"Yaowen","family":"Gu","sequence":"first","affiliation":[{"name":"Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100020, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9834-9774","authenticated-orcid":false,"given":"Si","family":"Zheng","sequence":"additional","affiliation":[{"name":"Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100020, China"},{"name":"Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6122-8426","authenticated-orcid":false,"given":"Zidu","family":"Xu","sequence":"additional","affiliation":[{"name":"Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100020, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5284-2259","authenticated-orcid":false,"given":"Qijin","family":"Yin","sequence":"additional","affiliation":[{"name":"Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China"}]},{"given":"Liang","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Antibiotic Bioengineering of National Health and Family Planning Commission (NHFPC), Institute of Medicinal Biotechnology (IMB), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100020, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6391-8343","authenticated-orcid":false,"given":"Jiao","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Medical Information (IMI), Chinese Academy of Medical Sciences and Peking Union Medical College (CAMS & PUMC), Beijing 100020, 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