{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T16:03:45Z","timestamp":1790697825073,"version":"4.1.0"},"reference-count":39,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Molecular Informatics"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:p>\n                    Predicting UV\u2013vis spectra from molecular structure is an important but relatively underexplored task. In this study, we developed a deep learning approach based on graph neural networks (GNNs) to directly predict UV\u2013vis spectra from SMILES representations. Several GNN architectures were evaluated, among which Attentive Fingerprint achieved the best performance with an\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    of 0.8630, mean absolute error of 0.0389, root mean squared error of 0.0740, cosine similarity of 0.9589, Pearson correlation coefficient of 0.9521, and distance metric of 1.5758 on the test set. Further analysis showed that the model might be able to capture meaningful structure\u2013property relationship across diverse chemical classes. The model was also evaluated on external datasets, where it maintained good predictive performance, indicating its generalizability. These results demonstrate that attention\u2010based GNNs provide an effective approach for UV\u2013vis spectrum prediction and may offer useful insights into the molecular regions contributing to model predictions, with potential applications in molecular analysis, high\u2010throughput screening, and compound design. To facilitate practical use, we deployed the model as an interactive web application (\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/spectra-prediction.streamlit.app\/\">https:\/\/spectra\u2010prediction.streamlit.app\/<\/jats:ext-link>\n                    ), which enables direct prediction of UV\u2013vis spectra from SMILES inputs.\n                  <\/jats:p>","DOI":"10.1002\/minf.70056","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T15:11:01Z","timestamp":1790694661000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data\u2010Driven Prediction of Ultraviolet\u2013Visible Spectra From Molecular Structure Using Attention\u2010Based Graph Neural Networks"],"prefix":"10.1002","volume":"45","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1915-0565","authenticated-orcid":false,"given":"Nguyen Van","family":"Phuong","sequence":"first","affiliation":[{"name":"Department of Pharmacognosy Hanoi University of Pharmacy  Hanoi Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5253-363X","authenticated-orcid":false,"given":"Ngo Huy","family":"Trung","sequence":"additional","affiliation":[{"name":"Department of Pharmacognosy Hanoi University of Pharmacy  Hanoi Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6542-5990","authenticated-orcid":false,"given":"Nguyen Trung","family":"Khoa","sequence":"additional","affiliation":[{"name":"Phenikaa Pharmaceuticals Joint Stock Company  Hanoi Vietnam"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"e_1_2_8_2_1","first-page":"337","volume-title":"Characterization I","author":"F\u00f6rster H.","year":"2004"},{"key":"e_1_2_8_3_1","doi-asserted-by":"crossref","first-page":"2512","DOI":"10.1002\/cjce.23344","article-title":"Experimental Methods in Chemical Engineering: Ultraviolet Visible Spectroscopy\u2014UV\u2010Vis","volume":"96","author":"Rocha F. 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