{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T14:47:22Z","timestamp":1792075642664,"version":"4.4.0"},"reference-count":26,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T00:00:00Z","timestamp":1791331200000},"content-version":"vor","delay-in-days":6,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"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":["Chirality"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Optical rotation is highly sensitive to molecular conformation, making prediction expensive when conformational ensembles and electronic\u2010response calculations are required. We evaluate three\u2010dimensional convolutional neural networks trained on volumetric molecular descriptors for conformer\u2010level optical rotation. A chemically structured set of substituted phenylethanol and phenylethylamine derivatives yielded 82,790 valid conformer\u2010level sTD\u2010DFT reference records. Three descriptors were compared on a common grid: HF\/STO\u20103G electron density, B3LYP\/cc\u2010pVDZ electron density, and GFN2\u2010xTB charge fields, each combined with atom\u2010type channels. Performance was assessed using random conformer splits, structured chemical holdouts, and an independently generated CREST\/GFN2\u2010xTB conformer\u2010ensemble benchmark for the same 65 compounds (705 conformers). Conformer\u2010level predictions were Boltzmann\u2010weighted and compared with sTD\u2010DFT molecular rotations. The GFN2\u2010xTB model gave the best deployment performance (MAE 15.3\u2009deg\u2009dm\n                    <jats:sup>\u22121<\/jats:sup>\n                    (g\u2009mL\n                    <jats:sup>\u22121<\/jats:sup>\n                    )\n                    <jats:sup>\u22121<\/jats:sup>\n                    , RMSE 22.2,\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20090.768, Pearson\n                    <jats:italic>r<\/jats:italic>\n                    \u2009=\u20090.901, 93.8% sign accuracy). Compound\u2010specific GFN2\u2010xTB models did not outperform the full\u2010dataset model, indicating that shared chemical and conformational information improves transfer. These results show that inexpensive semiempirical charge fields can retain sufficient conformer\u2010resolved chiroptical information for rapid ensemble optical rotation prediction within this chemical series.\n                  <\/jats:p>","DOI":"10.1002\/chir.70156","type":"journal-article","created":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T04:37:43Z","timestamp":1791434263000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Conformer\u2010Resolved Optical Rotation Prediction From Three\u2010Dimensional Quantum and Semiempirical Molecular Fields Using 3D Convolutional Neural Networks"],"prefix":"10.1002","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2691-572X","authenticated-orcid":false,"given":"Marcelo A.","family":"Mu\u00f1oz","sequence":"first","affiliation":[{"name":"Instituto de Ciencias Qu\u00edmicas, Facultad de Ciencias Universidad Austral de Chile  Valdivia Chile"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,10,7]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1002\/9781118120392"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1201\/9781315374888"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1021\/jp993424s"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1021\/jp0105138"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1063\/5.0020543"},{"key":"e_1_2_7_7_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.accounts.0c00745"},{"key":"e_1_2_7_8_1","unstructured":"J.Gilmer S. S.Schoenholz P. F.Riley O.Vinyals andG. E.Dahl \u201cNeural Message Passing for Quantum Chemistry \u201d Proceedings of the 34th International Conference on Machine Learning PMLR Vol. 70 (2017) 1263\u20131272."},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00237"},{"key":"e_1_2_7_10_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.3c01250"},{"key":"e_1_2_7_11_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.5c02332"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.6b00740"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.saa.2019.117289"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00876"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.4c02374"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.8b01176"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1039\/C9CP06869D"},{"key":"e_1_2_7_18_1","doi-asserted-by":"publisher","DOI":"10.1063\/5.0197592"},{"key":"e_1_2_7_19_1","unstructured":"Grimme Lab \u201cstd2: Program for Computing Excited States and Response Functions via Simplified TD\u2010DFT Methods \u201d Grimme Lab GitHub repository grimme\u2010lab\/std2 (n.d.) accessed 2026\u201008\u201010 https:\/\/github.com\/grimme\u2010lab\/std2."},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1063\/5.0055522"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.38.3098"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.37.785"},{"key":"e_1_2_7_23_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.464913"},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.456153"},{"key":"e_1_2_7_25_1","doi-asserted-by":"publisher","DOI":"10.1063\/5.0006074"},{"key":"e_1_2_7_26_1","doi-asserted-by":"publisher","DOI":"10.1063\/1.1672392"},{"key":"e_1_2_7_27_1","unstructured":"A.Paszke S.Gross F.Massa et\u00a0al. \u201cPyTorch: An Imperative Style High\u2010Performance Deep Learning Library \u201d in Advances in Neural Information Processing Systems 32 (2019)."}],"container-title":["Chirality"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/chir.70156","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/chir.70156","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/chir.70156","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T14:25:57Z","timestamp":1792074357000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/chir.70156"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10]]},"references-count":26,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2026,10]]}},"alternative-id":["10.1002\/chir.70156"],"URL":"https:\/\/doi.org\/10.1002\/chir.70156","archive":["Portico"],"relation":{},"ISSN":["0899-0042","1520-636X"],"issn-type":[{"value":"0899-0042","type":"print"},{"value":"1520-636X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10]]},"assertion":[{"value":"2026-08-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-24","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-10-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70156"}}