{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T01:32:56Z","timestamp":1793151176111,"version":"4.5.0"},"reference-count":35,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["31920260085"],"award-info":[{"award-number":["31920260085"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62462057"],"award-info":[{"award-number":["62462057"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004775","name":"Natural Science Foundation of Gansu Province","doi-asserted-by":"publisher","award":["24JRRA158"],"award-info":[{"award-number":["24JRRA158"]}],"id":[{"id":"10.13039\/501100004775","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computational Biology and Chemistry"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.compbiolchem.2026.109310","type":"journal-article","created":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T23:49:50Z","timestamp":1786492190000},"page":"109310","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["SMFP: A self-supervised multimodal molecular feature fusion method with bidirectional cross-attention for molecular property prediction"],"prefix":"10.1016","volume":"125","author":[{"given":"Xingxing","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5648-2323","authenticated-orcid":false,"given":"Jing","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yugang","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.compbiolchem.2026.109310_b1","series-title":"Learning attributed graph representations with communicative message passing transformer","author":"Chen","year":"2021"},{"key":"10.1016\/j.compbiolchem.2026.109310_b2","series-title":"ChemBERTa: Large-scale self-supervised pretraining for molecular property prediction","author":"Chithrananda","year":"2020"},{"issue":"2","key":"10.1016\/j.compbiolchem.2026.109310_b3","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1038\/s42256-021-00438-4","article-title":"Geometry-enhanced molecular representation learning for property prediction","volume":"4","author":"Fang","year":"2022","journal-title":"Nat. Mach. Intell."},{"issue":"5","key":"10.1016\/j.compbiolchem.2026.109310_b4","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1038\/s42256-023-00654-0","article-title":"Knowledge graph-enhanced molecular contrastive learning with functional prompt","volume":"5","author":"Fang","year":"2023","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.compbiolchem.2026.109310_b5","series-title":"International Conference on Machine Learning","first-page":"1263","article-title":"Neural message passing for quantum chemistry","author":"Gilmer","year":"2017"},{"key":"10.1016\/j.compbiolchem.2026.109310_b6","doi-asserted-by":"crossref","unstructured":"Guo, Z., Yu, W., Zhang, C., Jiang, M., Chawla, N.V., 2020. 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.","DOI":"10.1145\/3340531.3411981"},{"key":"10.1016\/j.compbiolchem.2026.109310_b7","series-title":"Masked molecule modeling: A new paradigm of molecular representation learning for chemistry understanding","author":"He","year":"2022"},{"key":"10.1016\/j.compbiolchem.2026.109310_b8","doi-asserted-by":"crossref","unstructured":"Hou, Z., Liu, X., Cen, Y., Dong, Y., Yang, H., Wang, C., Tang, J., 2022. GraphMAE: Self-supervised masked graph autoencoders. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pp. 594\u2013604.","DOI":"10.1145\/3534678.3539321"},{"key":"10.1016\/j.compbiolchem.2026.109310_b9","series-title":"Strategies for pre-training graph neural networks","author":"Hu","year":"2019"},{"key":"10.1016\/j.compbiolchem.2026.109310_b10","article-title":"N-gram graph: Simple unsupervised representation for graphs, with applications to molecules","volume":"32","author":"Liu","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.compbiolchem.2026.109310_b11","series-title":"Roberta: A robustly optimized bert pretraining approach","author":"Liu","year":"2019"},{"key":"10.1016\/j.compbiolchem.2026.109310_b12","unstructured":"Liu, S., Wang, H., Liu, W., Lasenby, J., Guo, H., Tang, J., 2022. Pre-training Molecular Graph Representation with 3D Geometry. In: International Conference on Learning Representations. ICLR."},{"issue":"17","key":"10.1016\/j.compbiolchem.2026.109310_b13","doi-asserted-by":"crossref","first-page":"3948","DOI":"10.1021\/acs.jcim.2c00521","article-title":"SMICLR: Contrastive learning on multiple molecular representations for semisupervised and unsupervised representation learning","volume":"62","author":"Pinheiro","year":"2022","journal-title":"J. Chem. Inf. Model."},{"issue":"7","key":"10.1016\/j.compbiolchem.2026.109310_b14","doi-asserted-by":"crossref","DOI":"10.1007\/s11432-024-4457-2","article-title":"Molecular pretraining models towards molecular property prediction","volume":"68","author":"Qiao","year":"2025","journal-title":"Sci. China Inf. Sci."},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109310_b15","doi-asserted-by":"crossref","first-page":"4382","DOI":"10.1038\/s41467-025-59634-0","article-title":"A self-conformation-aware pre-training framework for molecular property prediction with substructure interpretability","volume":"16","author":"Qiao","year":"2025","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compbiolchem.2026.109310_b16","series-title":"Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More","author":"Ramsundar","year":"2019"},{"key":"10.1016\/j.compbiolchem.2026.109310_b17","first-page":"12559","article-title":"Self-supervised graph transformer on large-scale molecular data","volume":"33","author":"Rong","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"4","key":"10.1016\/j.compbiolchem.2026.109310_b18","doi-asserted-by":"crossref","first-page":"bbae256","DOI":"10.1093\/bib\/bbae256","article-title":"Complementary multi-modality molecular self-supervised learning via non-overlapping masking for property prediction","volume":"25","author":"Shen","year":"2024","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.compbiolchem.2026.109310_b19","series-title":"29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence","article-title":"Communicative representation learning on attributed molecular graphs","author":"Song","year":"2020"},{"issue":"11","key":"10.1016\/j.compbiolchem.2026.109310_b20","doi-asserted-by":"crossref","first-page":"2324","DOI":"10.1021\/acs.jcim.5b00559","article-title":"ZINC 15\u2013ligand discovery for everyone","volume":"55","author":"Sterling","year":"2015","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.compbiolchem.2026.109310_b21","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111268","article-title":"Multi-scale cross-attention transformer via graph embeddings for few-shot molecular property prediction","volume":"153","author":"Torres","year":"2024","journal-title":"Appl. Soft Comput."},{"issue":"3","key":"10.1016\/j.compbiolchem.2026.109310_b22","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1038\/s42256-022-00447-x","article-title":"Molecular contrastive learning of representations via graph neural networks","volume":"4","author":"Wang","year":"2022","journal-title":"Nat. Mach. Intell."},{"issue":"2","key":"10.1016\/j.compbiolchem.2026.109310_b23","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1039\/C7SC02664A","article-title":"MoleculeNet: A benchmark for molecular machine learning","volume":"9","author":"Wu","year":"2018","journal-title":"Chem. Sci."},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109310_b24","doi-asserted-by":"crossref","first-page":"4993","DOI":"10.1038\/s41467-024-49388-6","article-title":"t-SMILES: A fragment-based molecular representation framework for de novo ligand design","volume":"15","author":"Wu","year":"2024","journal-title":"Nat. Commun."},{"key":"10.1016\/j.compbiolchem.2026.109310_b25","series-title":"Mole-bert: Rethinking pre-training graph neural networks for molecules","author":"Xia","year":"2023"},{"key":"10.1016\/j.compbiolchem.2026.109310_b26","series-title":"How powerful are graph neural networks?","author":"Xu","year":"2018"},{"key":"10.1016\/j.compbiolchem.2026.109310_b27","series-title":"Are learned molecular representations ready for prime time?","author":"Yang","year":"2019"},{"issue":"10.1038","key":"10.1016\/j.compbiolchem.2026.109310_b28","article-title":"Hierarchical molecular graph self-supervised learning for property prediction. Communications","volume":"6","author":"Zang","year":"2023","journal-title":"Chemistry"},{"issue":"11","key":"10.1016\/j.compbiolchem.2026.109310_b29","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1038\/s42256-022-00557-6","article-title":"Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework","volume":"4","author":"Zeng","year":"2022","journal-title":"Nat. Mach. Intell."},{"issue":"4","key":"10.1016\/j.compbiolchem.2026.109310_b30","doi-asserted-by":"crossref","first-page":"bbae298","DOI":"10.1093\/bib\/bbae298","article-title":"MvMRL: A multi-view molecular representation learning method for molecular property prediction","volume":"25","author":"Zhang","year":"2024","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.compbiolchem.2026.109310_b31","first-page":"15870","article-title":"Motif-based graph self-supervised learning for molecular property prediction","volume":"34","author":"Zhang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"10.1016\/j.compbiolchem.2026.109310_b32","doi-asserted-by":"crossref","first-page":"bbag010","DOI":"10.1093\/bib\/bbag010","article-title":"Task-specific pre-training for molecular property prediction","volume":"27","author":"Zhang","year":"2026","journal-title":"Brief. Bioinform."},{"key":"10.1016\/j.compbiolchem.2026.109310_b33","first-page":"1","article-title":"Large language models for scientific discovery in molecular property prediction","author":"Zheng","year":"2025","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.compbiolchem.2026.109310_b34","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","article-title":"Graph neural networks: A review of methods and applications","volume":"1","author":"Zhou","year":"2020","journal-title":"AI Open"},{"key":"10.1016\/j.compbiolchem.2026.109310_b35","doi-asserted-by":"crossref","unstructured":"Zhu, J., Xia, Y., Wu, L., Xie, S., Zhou, W., Qin, T., Li, H., Liu, T.-Y., 2023. Dual-view molecular pre-training. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pp. 3615\u20133627.","DOI":"10.1145\/3580305.3599317"}],"container-title":["Computational Biology and Chemistry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1476927126004378?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1476927126004378?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T00:45:39Z","timestamp":1793148339000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1476927126004378"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":35,"alternative-id":["S1476927126004378"],"URL":"https:\/\/doi.org\/10.1016\/j.compbiolchem.2026.109310","relation":{},"ISSN":["1476-9271"],"issn-type":[{"value":"1476-9271","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"SMFP: A self-supervised multimodal molecular feature fusion method with bidirectional cross-attention for molecular property prediction","name":"articletitle","label":"Article Title"},{"value":"Computational Biology and Chemistry","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.compbiolchem.2026.109310","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109310"}}