{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T07:02:19Z","timestamp":1780729339582,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819214617","type":"print"},{"value":"9789819214624","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-92-1462-4_40","type":"book-chapter","created":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T06:46:11Z","timestamp":1780728371000},"page":"508-520","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SGF-Net: Fusing SMILES, Graph, and\u00a0Fingerprints for\u00a0Molecular Property Prediction"],"prefix":"10.1007","author":[{"given":"Ting","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Linxing","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiashuang","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiping","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,7]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Bolton, E.E., Wang, Y., Thiessen, P.A., Bryant, S.H.: PubChem: integrated platform of small molecules and biological activities. Ann. Rep. Comput. Chem. 4, 217\u2013241 (2008)","DOI":"10.1016\/S1574-1400(08)00012-1"},{"key":"40_CR2","doi-asserted-by":"crossref","unstructured":"Cai, H., Zhang, H., Zhao, D., Wu, J., Wang, L.: FP-GNN: a versatile deep learning architecture for enhanced molecular property prediction. Briefings Bioinf. 23(6), bbac408 (2022)","DOI":"10.1093\/bib\/bbac408"},{"issue":"6","key":"40_CR3","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1021\/ci010132r","volume":"42","author":"JL Durant","year":"2002","unstructured":"Durant, J.L., Leland, B.A., Henry, D.R., Nourse, J.G.: Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comput. Sci. 42(6), 1273\u20131280 (2002)","journal-title":"J. Chem. Inf. Comput. Sci."},{"issue":"2","key":"40_CR4","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1038\/s42256-021-00438-4","volume":"4","author":"X Fang","year":"2022","unstructured":"Fang, X., Liu, L., Lei, J., He, D., Zhang, S., Zhou, J., et al.: Geometry-enhanced molecular representation learning for property prediction. Nat. Mach. Intell. 4(2), 127\u2013134 (2022)","journal-title":"Nat. Mach. Intell."},{"key":"40_CR5","unstructured":"Gilmer, J., Schoenholz, S.S., Riley, P.F., Vinyals, O., Dahl, G.E.: Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263\u20131272. PMLR (2017)"},{"key":"40_CR6","doi-asserted-by":"crossref","unstructured":"Guo, Z., Yu, W., Zhang, C., Jiang, M., Chawla, N.V.: GraSeq: graph and sequence fusion learning for molecular property prediction. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 435\u2013443 (2020)","DOI":"10.1145\/3340531.3411981"},{"key":"40_CR7","doi-asserted-by":"crossref","unstructured":"Han, S., et\u00a0al.: HimGNN: a novel hierarchical molecular graph representation learning framework for property prediction. Briefings Bioinf. 24(5), bbad305 (2023)","DOI":"10.1093\/bib\/bbad305"},{"issue":"1","key":"40_CR8","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1021\/acs.jcim.3c01250","volume":"64","author":"E Heid","year":"2024","unstructured":"Heid, E., et al.: Chemprop: a machine learning package for chemical property prediction. J. Chem. Inf. Model. 64(1), 9\u201317 (2024)","journal-title":"J. Chem. Inf. Model."},{"key":"40_CR9","unstructured":"Honda, S., Shi, S., Ueda, H.R.: Smiles transformer: pre-trained molecular fingerprint for low data drug discovery (2019). arXiv preprint arXiv:1911.04738"},{"key":"40_CR10","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: International Conference on Learning Representations (2017)"},{"key":"40_CR11","unstructured":"Liu, S., Demirel, M.F., Liang, Y.: N-gram graph: simple unsupervised representation for graphs, with applications to molecules. In: Advances in Neural Information Processing Systems, vol. 32, pp. 8464\u20138476 (2019)"},{"key":"40_CR12","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D.: RoBERTa: a robustly optimized BERT pretraining approach (2019). arXiv preprint arXiv:1907.11692"},{"issue":"29","key":"40_CR13","doi-asserted-by":"publisher","first-page":"861","DOI":"10.21105\/joss.00861","volume":"3","author":"L McInnes","year":"2018","unstructured":"McInnes, L., Healy, J., Saul, N., Gro\u00dfberger, L.: UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3(29), 861 (2018)","journal-title":"J. Open Source Softw."},{"issue":"5","key":"40_CR14","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers, D., Hahn, M.: Extended-connectivity fingerprints. J. Chem. Inf. Model. 50(5), 742\u2013754 (2010)","journal-title":"J. Chem. Inf. Model."},{"key":"40_CR15","unstructured":"Rong, Y., Bian, Y., Xu, T., Xie, W., Wei, Y., Huang, W.: Self-supervised graph transformer on large-scale molecular data. In: Advances in Neural Information Processing Systems, vol. 33, pp. 12559\u201312571 (2020)"},{"key":"40_CR16","doi-asserted-by":"crossref","unstructured":"Simonovsky, M., Komodakis, N.: Dynamic edge-conditioned filters in convolutional neural networks on graphs. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3693\u20133702 (2017)","DOI":"10.1109\/CVPR.2017.11"},{"issue":"6","key":"40_CR17","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1038\/s41573-019-0024-5","volume":"18","author":"J Vamathevan","year":"2019","unstructured":"Vamathevan, J., et al.: Applications of machine learning in drug discovery and development. Nat. Rev. Drug Discovery 18(6), 463\u2013477 (2019)","journal-title":"Nat. Rev. Drug Discovery"},{"key":"40_CR18","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)"},{"key":"40_CR19","doi-asserted-by":"crossref","unstructured":"Weininger, D.: Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28(1), 31\u201336 (1988)","DOI":"10.1021\/ci00057a005"},{"issue":"2","key":"40_CR20","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1039\/C7SC02664A","volume":"9","author":"Z Wu","year":"2018","unstructured":"Wu, Z., et al.: MoleculeNet: a benchmark for molecular machine learning. Chem. Sci. 9(2), 513\u2013530 (2018)","journal-title":"Chem. Sci."},{"issue":"23","key":"40_CR21","doi-asserted-by":"publisher","first-page":"21303","DOI":"10.1021\/acs.jmedchem.4c02193","volume":"67","author":"X Yang","year":"2024","unstructured":"Yang, X., et al.: MPCD: a multitask graph transformer for molecular property prediction by integrating common and domain knowledge. J. Med. Chem. 67(23), 21303\u201321316 (2024)","journal-title":"J. Med. Chem."},{"key":"40_CR22","unstructured":"Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D.: Do transformers really perform badly for graph representation? In: Advances in Neural Information Processing Systems, vol. 34, pp. 28877\u201328888 (2021)"},{"key":"40_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, R., et al.: MvMRL: a multi-view molecular representation learning method for molecular property prediction. Briefings Bioinf. 25(4), bbae298 (2024)","DOI":"10.1093\/bib\/bbae298"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-1462-4_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T06:46:14Z","timestamp":1780728374000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-1462-4_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819214617","9789819214624"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-1462-4_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"7 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pakdd2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}