{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T03:33:38Z","timestamp":1784432018526,"version":"3.55.0"},"reference-count":48,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2022,12,16]],"date-time":"2022-12-16T00:00:00Z","timestamp":1671148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"Macao Polytechnic University","award":["RP\/FCA-01\/2022"],"award-info":[{"award-number":["RP\/FCA-01\/2022"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2019B020210003"],"award-info":[{"award-number":["2019B020210003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Graph neural networks based on deep learning methods have been extensively applied to the molecular property prediction because of its powerful feature learning ability and good performance. However, most of them are black boxes and cannot give the reasonable explanation about the underlying prediction mechanisms, which seriously reduce people\u2019s trust on the neural network-based prediction models. Here we proposed a novel graph neural network named iteratively focused graph network (IFGN), which can gradually identify the key atoms\/groups in the molecule that are closely related to the predicted properties by the multistep focus mechanism. At the same time, the combination of the multistep focus mechanism with visualization can also generate multistep interpretations, thus allowing us to gain a deep understanding of the predictive behaviors of the model. For all studied eight datasets, the IFGN model achieved good prediction performance, indicating that the proposed multistep focus mechanism also can improve the performance of the model obviously besides increasing the interpretability of built model. For researchers to use conveniently, the corresponding website (http:\/\/graphadmet.cn\/works\/IFGN) was also developed and can be used free of charge.<\/jats:p>","DOI":"10.1093\/bib\/bbac534","type":"journal-article","created":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T02:34:46Z","timestamp":1671244486000},"source":"Crossref","is-referenced-by-count":19,"title":["Predicting molecular properties based on the interpretable graph neural network with multistep focus mechanism"],"prefix":"10.1093","volume":"24","author":[{"given":"Yanan","family":"Tian","sequence":"first","affiliation":[{"name":"Faculty of Applied Science, Macao Polytechnic University , Macao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6893-2013","authenticated-orcid":false,"given":"Xiaorui","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology , Macao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojun","family":"Yao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Quality Research in Chinese Medicines, Macau University of Science and Technology , Macao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9284-3667","authenticated-orcid":false,"given":"Huanxiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Applied Science, Macao Polytechnic University , Macao, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Quality Management, Guangdong Provincial Center for Disease Prevention and Control , Guangzhou, 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