{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T15:18:30Z","timestamp":1761837510548,"version":"build-2065373602"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T00:00:00Z","timestamp":1758499200000},"content-version":"vor","delay-in-days":0,"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":["62502050","62532017","62322215","U24A20257"],"award-info":[{"award-number":["62502050","62532017","62322215","U24A20257"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Program","award":["JCYJ20241202130212016","KQTD20200820113106007"],"award-info":[{"award-number":["JCYJ20241202130212016","KQTD20200820113106007"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2025-JYB-XJSJJ008"],"award-info":[{"award-number":["2025-JYB-XJSJJ008"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"High-Performance Computing Center of Central South University and the High-Performance Computing Clusters","award":["PL-17161"],"award-info":[{"award-number":["PL-17161"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Metabolic stability is crucial in the early stage of drug discovery and development. Drug candidate screening and optimization can be streamlined through the accurate prediction of stability. Functional groups within drug molecules are known as pharmacophores, which bind directly to receptors or biological macromolecules to produce biological effects, thereby affecting metabolic stability. Therefore, determining metabolic stability via the pharmacophore groups remains a significant challenge.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>To address these issues, we propose a Pharmacophore-guided Hypergraph representation framework for predicting metabolic Stability (HyperPhS). In this study, we introduce a hypergraph-based method to extract features from metabolic pharmacophores with multi-view representation and contrastive learning. In particular, we introduce a pharmacophore-based contrastive learning encoder that captures the consistency between functional and nonfunctional structures. Our method applies ChatGPT simultaneously to metabolites and heterogeneous encoders and integrates multimodal representations by using attention-driven fusion modules coupled with fully connected neural networks. On the HLM dataset, HyperPhS achieves outstanding performance with 87.6% in AUC and 62.6% in MCC, alongside an external test AUC of 88.3%. In addition, pharmacophore groups studied by HyperPhS are validated for their interpretability through case studies. Overall, HyperPhS is an effective and interpretable tool for determining metabolic stability, identifying critical functional groups, and optimizing compounds.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The code and data are available at https:\/\/github.com\/xiaoyiliu-usc\/HyperPhS.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf524","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T12:02:04Z","timestamp":1758283324000},"source":"Crossref","is-referenced-by-count":0,"title":["HyperPhS: a pharmacophore-guided multimodal representation framework for metabolic stability prediction through contrastive hypergraph learning"],"prefix":"10.1093","volume":"41","author":[{"given":"Xiaoyi","family":"Liu","sequence":"first","affiliation":[{"name":"School of Chinese Materia Medica, Beijing University of Chinese Medicine , Beijing 100029,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Na","family":"Zhang","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Central South University , Changsha 410083,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenglong","family":"Kang","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Central South University , Changsha 410083,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengwei","family":"Ai","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Central South University , Changsha 410083,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongpeng","family":"Yang","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, University of South Carolina , Columbia, SC 29208,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jijun","family":"Tang","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Shenzhen University of Advanced Technology , Nanshan 518055,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8346-0798","authenticated-orcid":false,"given":"Fei","family":"Guo","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Central South University , Changsha 410083,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,9,22]]},"reference":[{"author":"Beltagy I, Lo K, Cohan A.","key":"2025103011080097200_btaf524-B1"},{"key":"2025103011080097200_btaf524-B2","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbac408","article-title":"FP-GNN: a versatile deep learning architecture for enhanced molecular property 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