{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T02:38:35Z","timestamp":1775011115406,"version":"3.50.1"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,2,1]],"date-time":"2025-02-01T00:00:00Z","timestamp":1738368000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1016\/j.saa.2024.125213","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T02:01:13Z","timestamp":1727143273000},"page":"125213","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"special_numbering":"C","title":["Data-driven machine learning models for predicting the maximum absorption and emission wavelengths of single benzene fluorophores"],"prefix":"10.1016","volume":"326","author":[{"given":"Yongshi","family":"Jin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaohe","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miao","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pingping","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1776-0025","authenticated-orcid":false,"given":"Weijie","family":"Chi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.saa.2024.125213_b0005","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1038\/s41592-021-01338-6","article-title":"Caveat fluorophore: an insiders\u2019 guide to small-molecule fluorescent labels","volume":"19","author":"Grimm","year":"2022","journal-title":"Nat. Methods"},{"key":"10.1016\/j.saa.2024.125213_b0010","doi-asserted-by":"crossref","first-page":"e202306061","DOI":"10.1002\/anie.202306061","article-title":"Spontaneously blinking rhodamine dyes for single-molecule localization microscopy","volume":"62","author":"Chi","year":"2023","journal-title":"Angew. Chem. Int. Ed."},{"key":"10.1016\/j.saa.2024.125213_b0015","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.aac.2023.02.004","article-title":"Shining light on plant health: detecting salt stress with a near-infrared fluorescent probe","volume":"2","author":"Liu","year":"2023","journal-title":"Adv. Agrochem"},{"key":"10.1016\/j.saa.2024.125213_b0020","doi-asserted-by":"crossref","first-page":"6777","DOI":"10.1021\/jacs.0c01473","article-title":"A general descriptor \u0394E enables the quantitative development of luminescent materials based on photoinduced electron transfer","volume":"142","author":"Chi","year":"2020","journal-title":"J. Am. Chem. Soc."},{"key":"10.1016\/j.saa.2024.125213_b0025","doi-asserted-by":"crossref","first-page":"e20230011","DOI":"10.1002\/smo.20230011","article-title":"Computational chemistry-assisted design of hydrazine-based fluorescent molecular rotor for viscosity sensors","volume":"1","author":"Dong","year":"2023","journal-title":"Smart Mol."},{"key":"10.1016\/j.saa.2024.125213_b0030","doi-asserted-by":"crossref","first-page":"e20220002","DOI":"10.1002\/smo.20220002","article-title":"Engineering an asymmetric rhodamine dye suitable for developing ratiometric fluorescent probe","volume":"1","author":"Yang","year":"2023","journal-title":"Smart Mol."},{"key":"10.1016\/j.saa.2024.125213_b0035","unstructured":"J. Huang, Y. Yao, L. Zhang, C. Yan, Z. Guo, \u201cCrossbreeding\u201d NIR-II flavchromene for PSMA-positive prostate cancer detection and image-guided surgery, Smart Mol., 10.1002\/smo.20240020."},{"key":"10.1016\/j.saa.2024.125213_b0040","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1039\/D0OB02387F","article-title":"Recent advances in single-benzene-based fluorophores: physicochemical properties and applications","volume":"19","author":"Kim","year":"2021","journal-title":"Org. Biomol. Chem."},{"key":"10.1016\/j.saa.2024.125213_b0045","unstructured":"Z. Bao, Y. Yang, X. Wu, Y. Ni, H. Zhou, Z. Zheng, B.Z. Tang, Single-benzene fluorophores: controlling electron density on amino of 2,5-diaminoterephthalates to manipulate optical\/electronic properties for efficient and diversified functions, Adv. Funct. Mater., 10.1002\/adfm.202403954."},{"key":"10.1016\/j.saa.2024.125213_b0050","article-title":"Single-benzene-based fluorophores (SBBFs) with green luminescence: improved sensitivity towards cysteine both in solution and solid state","volume":"36","author":"Fernandes","year":"2024","journal-title":"Mater. Today Chem."},{"key":"10.1016\/j.saa.2024.125213_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.dyepig.2023.111811","article-title":"Amino-SBBF (single benzene-based fluorophore) library: its synthesis, photophysical property, and cellular imaging application","volume":"221","author":"Jin","year":"2024","journal-title":"Dyes Pigments"},{"key":"10.1016\/j.saa.2024.125213_b0060","article-title":"Precision molecular engineering of miniaturized near-infrared fluorophores","author":"Huang","year":"2024","journal-title":"Chem. Rxiv"},{"key":"10.1016\/j.saa.2024.125213_b0065","doi-asserted-by":"crossref","first-page":"11462","DOI":"10.1039\/C9CC06260B","article-title":"A general strategy for development of a single benzene fluorophore with full-color-tunable, environmentally insensitive, and two-photon solid-state emission","volume":"55","author":"Xiang","year":"2019","journal-title":"Chem. Commun."},{"key":"10.1016\/j.saa.2024.125213_b0070","doi-asserted-by":"crossref","first-page":"5409","DOI":"10.1038\/s41467-021-25677-2","article-title":"Relief of excited-state antiaromaticity enables the smallest red emitter","volume":"12","author":"Kim","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.saa.2024.125213_b0075","doi-asserted-by":"crossref","first-page":"e202302107","DOI":"10.1002\/anie.202302107","article-title":"Single-benzene dual-emitters harness excited-state antiaromaticity for white light generation and fluorescence imaging","volume":"62","author":"Kim","year":"2023","journal-title":"Angew. Chem. Int. Ed."},{"key":"10.1016\/j.saa.2024.125213_b0080","doi-asserted-by":"crossref","first-page":"16584","DOI":"10.1021\/jp404170w","article-title":"Molecular design of UV\u2013vis absorption and emission properties in organic fluorophores: toward larger bathochromic shifts, enhanced molar extinction coefficients, and greater stokes shifts","volume":"117","author":"Liu","year":"2013","journal-title":"J. Phys. Chem. C"},{"key":"10.1016\/j.saa.2024.125213_b0085","doi-asserted-by":"crossref","first-page":"4505","DOI":"10.1021\/acs.jcim.3c00643","article-title":"Open-source machine learning in computational chemistry","volume":"63","author":"Hagg","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.saa.2024.125213_b0090","doi-asserted-by":"crossref","DOI":"10.1016\/j.dyepig.2023.111670","article-title":"Data-driven machine learning models for quick prediction of the Stokes shift of organic fluorescent materials","volume":"220","author":"Zhao","year":"2023","journal-title":"Dyes Pigments"},{"key":"10.1016\/j.saa.2024.125213_b0095","doi-asserted-by":"crossref","first-page":"eabj3906","DOI":"10.1126\/sciadv.abj3906","article-title":"De novo creation of a naked eye\u2013detectable fluorescent molecule based on quantum chemical computation and machine learning","volume":"8","author":"Sumita","year":"2022","journal-title":"Sci. Adv."},{"key":"10.1016\/j.saa.2024.125213_b0100","doi-asserted-by":"crossref","first-page":"7955","DOI":"10.1039\/C8CC02850H","article-title":"Prediction and understanding of AIE effect by quantum mechanics-aided machine-learning algorithm","volume":"54","author":"Qiu","year":"2018","journal-title":"Chem. Commun."},{"key":"10.1016\/j.saa.2024.125213_b0105","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1039\/D1SC05677H","article-title":"Multi-fidelity prediction of molecular optical peaks with deep learning","volume":"13","author":"Greenman","year":"2022","journal-title":"Chem. Sci."},{"key":"10.1016\/j.saa.2024.125213_b0110","doi-asserted-by":"crossref","first-page":"1462","DOI":"10.1021\/acs.jcim.2c01662","article-title":"Machine learning models for predicting molecular UV\u2013Vis spectra with quantum mechanical properties","volume":"63","author":"McNaughton","year":"2023","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.saa.2024.125213_b0115","doi-asserted-by":"crossref","first-page":"6378","DOI":"10.1021\/jp810292n","article-title":"Universal solvation model based on solute electron density and on a continuum model of the solvent defined by the bulk dielectric constant and atomic surface tensions","volume":"113","author":"Marenich","year":"2009","journal-title":"J. Phys. Chem. B"},{"key":"10.1016\/j.saa.2024.125213_b0120","unstructured":"M.e. Frisch, G. Trucks, H. Schlegel, G. Scuseria, M. Robb, J. Cheeseman, G. Scalmani, V. Barone, G. Petersson, H. Nakatsuji, Gaussian 16, revision C. 01, in, Gaussian, Inc., Wallingford CT, 2016."},{"key":"10.1016\/j.saa.2024.125213_b0125","doi-asserted-by":"crossref","first-page":"580","DOI":"10.1002\/jcc.22885","article-title":"Multiwfn: A multifunctional wavefunction analyzer","volume":"33","author":"Lu","year":"2012","journal-title":"J. Comput. Chem."},{"key":"10.1016\/j.saa.2024.125213_b0130","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1021\/acs.jcim.1c01449","article-title":"Prediction of maximum absorption wavelength using deep neural networks","volume":"62","author":"Shao","year":"2022","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.saa.2024.125213_b0135","doi-asserted-by":"crossref","first-page":"1044","DOI":"10.1021\/acs.jcim.8b00672","article-title":"DeepChemStable: chemical stability prediction with an attention-based graph convolution network","volume":"59","author":"Li","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.saa.2024.125213_b0140","doi-asserted-by":"crossref","first-page":"4401","DOI":"10.1021\/acs.jpclett.9b01428","article-title":"Convolutional neural network of atomic surface structures to predict binding energies for high-throughput screening of catalysts","volume":"10","author":"Back","year":"2019","journal-title":"J. Phys. Chem. Lett."},{"key":"10.1016\/j.saa.2024.125213_b0145","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy","volume":"27","author":"Hanchuan","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.saa.2024.125213_b0150","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1038\/s41578-022-00490-5","article-title":"Machine learning for a sustainable energy future","volume":"8","author":"Yao","year":"2023","journal-title":"Nat. Rev. Mater."},{"key":"10.1016\/j.saa.2024.125213_b0155","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1038\/s41580-021-00407-0","article-title":"A guide to machine learning for biologists","volume":"23","author":"Greener","year":"2022","journal-title":"Nat. Rev. Mol. Cell Biol."},{"key":"10.1016\/j.saa.2024.125213_b0160","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"Alzubaidi","year":"2021","journal-title":"J. Big Data"},{"key":"10.1016\/j.saa.2024.125213_b0165","first-page":"1169","article-title":"Multi-label graph convolutional network representation learning","volume":"8","author":"Shi","year":"2022","journal-title":"IEEE TBD."},{"key":"10.1016\/j.saa.2024.125213_b0170","doi-asserted-by":"crossref","first-page":"8749","DOI":"10.1021\/acs.jmedchem.9b00959","article-title":"Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism","volume":"63","author":"Xiong","year":"2020","journal-title":"J. Med. Chem."},{"key":"10.1016\/j.saa.2024.125213_b0175","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1021\/acs.jcim.0c01203","article-title":"Machine learning enables highly accurate predictions of photophysical properties of organic fluorescent materials: emission wavelengths and quantum yields","volume":"61","author":"Ju","year":"2021","journal-title":"J. Chem. Inf. Model."},{"key":"10.1016\/j.saa.2024.125213_b0180","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1016\/j.cclet.2021.02.003","article-title":"Quantitative assessment of rhodamine spectra","volume":"32","author":"Zhou","year":"2021","journal-title":"Chin. Chem. Lett."},{"key":"10.1016\/j.saa.2024.125213_b0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.dyepig.2022.110647","article-title":"Predicting the maximum absorption wavelength of azo dyes using an interpretable machine learning strategy","volume":"206","author":"Mai","year":"2022","journal-title":"Dyes Pigments"}],"container-title":["Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1386142524013799?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1386142524013799?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T16:10:42Z","timestamp":1732378242000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1386142524013799"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2]]},"references-count":37,"alternative-id":["S1386142524013799"],"URL":"https:\/\/doi.org\/10.1016\/j.saa.2024.125213","relation":{},"ISSN":["1386-1425"],"issn-type":[{"value":"1386-1425","type":"print"}],"subject":[],"published":{"date-parts":[[2025,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Data-driven machine learning models for predicting the maximum absorption and emission wavelengths of single benzene fluorophores","name":"articletitle","label":"Article Title"},{"value":"Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.saa.2024.125213","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"125213"}}