{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T15:41:23Z","timestamp":1788363683639,"version":"build-2803163510"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T00:00:00Z","timestamp":1730678400000},"content-version":"vor","delay-in-days":42,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,23]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Substructure-based representation learning has emerged as a powerful approach to featurize complex attributed graphs, with promising results in molecular property prediction (MPP). However, existing MPP methods mainly rely on manually defined rules to extract substructures. It remains an open challenge to adaptively identify meaningful substructures from numerous molecular graphs to accommodate MPP tasks. To this end, this paper proposes Prototype-based cOntrastive Substructure IdentificaTion (POSIT), a self-supervised framework to autonomously discover substructural prototypes across graphs so as to guide end-to-end molecular fragmentation. During pre-training, POSIT emphasizes two key aspects of substructure identification: firstly, it imposes a soft connectivity constraint to encourage the generation of topologically meaningful substructures; secondly, it aligns resultant substructures with derived prototypes through a prototype-substructure contrastive clustering objective, ensuring attribute-based similarity within clusters. In the fine-tuning stage, a cross-scale attention mechanism is designed to integrate substructure-level information to enhance molecular representations. The effectiveness of the POSIT framework is demonstrated by experimental results from diverse real-world datasets, covering both classification and regression tasks. Moreover, visualization analysis validates the consistency of chemical priors with identified substructures. The source code is publicly available at https:\/\/github.com\/VRPharmer\/POSIT.<\/jats:p>","DOI":"10.1093\/bib\/bbae565","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T12:17:37Z","timestamp":1730722657000},"source":"Crossref","is-referenced-by-count":5,"title":["Prototype-based contrastive substructure identification for molecular property prediction"],"prefix":"10.1093","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8365-0970","authenticated-orcid":false,"given":"Gaoqi","family":"He","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University , 200062 Shanghai ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University , 200062 Shanghai ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuoran","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University , 200062 Shanghai ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changbo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University , 200062 Shanghai ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University , 200062 Shanghai ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honglin","family":"Li","sequence":"additional","affiliation":[{"name":"Innovation Center for AI and Drug Discovery, East China Normal University , 200062 Shanghai ,","place":["China"]},{"name":"Shanghai Key Laboratory of New Drug Design , School of Pharmacy, , 200237 Shanghai ,","place":["China"]},{"name":"East China University of Science & Technology , School of Pharmacy, , 200237 Shanghai ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,11,4]]},"reference":[{"key":"2024110412170860800_ref1","doi-asserted-by":"publisher","first-page":"103373","DOI":"10.1016\/j.drudis.2022.103373","article-title":"Deep learning methods for molecular representation and property prediction","volume":"27","author":"Li","year":"2022","journal-title":"Drug Discov Today"},{"key":"2024110412170860800_ref2","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1038\/s41573-019-0050-3","article-title":"Rethinking drug design in the artificial intelligence era","volume":"19","author":"Petra Schneider","year":"2020","journal-title":"Nat Rev Drug Discov"},{"key":"2024110412170860800_ref3","doi-asserted-by":"publisher","first-page":"bbab340","DOI":"10.1093\/bib\/bbab340","article-title":"Graph representation learning in bioinformatics: trends, methods and applications","volume":"23","author":"Yi","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024110412170860800_ref4","doi-asserted-by":"publisher","first-page":"bbab430","DOI":"10.1093\/bib\/bbab430","article-title":"Artificial intelligence in drug discovery: Applications and techniques","volume":"23","author":"Deng","year":"2022","journal-title":"Brief Bioinform"},{"key":"2024110412170860800_ref5","doi-asserted-by":"publisher","first-page":"e1011773","DOI":"10.1371\/journal.pcbi.1011773","article-title":"Joint representation of molecular networks from multiple species improves gene classification","volume":"20","author":"Mancuso","year":"2024","journal-title":"PLoS Comput Biol"},{"key":"2024110412170860800_ref6","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"Smiles, a chemical language and information system. 1. 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