{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:02:36Z","timestamp":1774353756142,"version":"3.50.1"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T00:00:00Z","timestamp":1714953600000},"content-version":"vor","delay-in-days":5,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372326"],"award-info":[{"award-number":["62372326"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62372116"],"award-info":[{"award-number":["62372116"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,5,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Molecular property prediction (MPP) is a fundamental but challenging task in the computer-aided drug discovery process. More and more recent works employ different graph-based models for MPP, which have achieved considerable progress in improving prediction performance. However, current models often ignore relationships between molecules, which could be also helpful for MPP.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>For this sake, in this article we propose a graph structure learning (GSL) based MPP approach, called GSL-MPP. Specifically, we first apply graph neural network (GNN) over molecular graphs to extract molecular representations. Then, with molecular fingerprints, we construct a molecule similarity graph (MSG). Following that, we conduct GSL on the MSG, i.e. molecule-level GSL, to get the final molecular embeddings, which are the results of fuzing both GNN encoded molecular representations and the relationships among molecules. That is, combining both intra-molecule and inter-molecule information. Finally, we use these molecular embeddings to perform MPP. Extensive experiments on 10 various benchmark datasets show that our method could achieve state-of-the-art performance in most cases, especially on classification tasks. Further visualization studies also demonstrate the good molecular representations of our method.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Source code is available at https:\/\/github.com\/zby961104\/GSL-MPP.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae304","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T23:23:07Z","timestamp":1715037787000},"source":"Crossref","is-referenced-by-count":23,"title":["Molecular property prediction based on graph structure learning"],"prefix":"10.1093","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6838-5172","authenticated-orcid":false,"given":"Bangyi","family":"Zhao","sequence":"first","affiliation":[{"name":"Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University , Shanghai 200438, China"}]},{"given":"Weixia","family":"Xu","sequence":"additional","affiliation":[{"name":"Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University , Shanghai 200438, China"}]},{"given":"Jihong","family":"Guan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Technology, Tongji University , Shanghai 201804, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1949-2768","authenticated-orcid":false,"given":"Shuigeng","family":"Zhou","sequence":"additional","affiliation":[{"name":"Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University , Shanghai 200438, China"}]}],"member":"286","published-online":{"date-parts":[[2024,5,6]]},"reference":[{"key":"2024052304525476400_btae304-B1","author":"Chen","year":"2021"},{"key":"2024052304525476400_btae304-B2","first-page":"19314","article-title":"Iterative deep graph learning for graph neural networks: better and robust 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