{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,2]],"date-time":"2025-08-02T16:18:29Z","timestamp":1754151509476,"version":"3.41.2"},"reference-count":41,"publisher":"American Chemical Society (ACS)","issue":"13","license":[{"start":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T00:00:00Z","timestamp":1750723200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/P020232\/1","EP\/S022848\/1","EP\/T022175\/1","EP\/X014088\/1"],"award-info":[{"award-number":["EP\/P020232\/1","EP\/S022848\/1","EP\/T022175\/1","EP\/X014088\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","award":["MR\/X023109\/1"],"award-info":[{"award-number":["MR\/X023109\/1"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Chem. Inf. Model."],"published-print":{"date-parts":[[2025,7,14]]},"DOI":"10.1021\/acs.jcim.5c00665","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T02:03:51Z","timestamp":1750817031000},"page":"6644-6654","source":"Crossref","is-referenced-by-count":0,"title":["Structural Bias in Three-Dimensional Autoregressive Generative Machine Learning of Organic Molecules"],"prefix":"10.1021","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6714-0337","authenticated-orcid":true,"given":"Zsuzsanna","family":"Koczor-Benda","sequence":"first","affiliation":[{"name":"Department of Chemistry","place":["Coventry, U.K."]},{"name":"University of Warwick","place":["Coventry, U.K."]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1864-4571","authenticated-orcid":true,"given":"Joe","family":"Gilkes","sequence":"additional","affiliation":[{"name":"Department of Chemistry","place":["Coventry, U.K."]},{"name":"University of Warwick","place":["Coventry, U.K."]},{"name":"Centre for Doctoral Training in Modelling of Heterogeneous Systems","place":["Coventry, U.K."]},{"name":"University of Warwick","place":["Coventry, U.K."]}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7866-7934","authenticated-orcid":true,"given":"Francesco","family":"Bartucca","sequence":"additional","affiliation":[{"name":"Department of Chemistry","place":["Coventry, U.K."]},{"name":"University of Warwick","place":["Coventry, U.K."]}]},{"given":"Abdulla","family":"Al-Fekaiki","sequence":"additional","affiliation":[{"name":"Department of Chemistry","place":["Coventry, U.K."]},{"name":"University of Warwick","place":["Coventry, U.K."]}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3004-785X","authenticated-orcid":true,"given":"Reinhard J.","family":"Maurer","sequence":"additional","affiliation":[{"name":"Department of Chemistry","place":["Coventry, U.K."]},{"name":"University of Warwick","place":["Coventry, U.K."]},{"name":"Department of Physics","place":["Coventry, U.K."]},{"name":"University of Warwick","place":["Coventry, U.K."]}]}],"member":"316","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"ref1\/cit1","doi-asserted-by":"publisher","DOI":"10.1039\/C9ME00039A"},{"key":"ref2\/cit2","unstructured":"Schwalbe-Koda, D.; G\u00f3mez-Bombarelli, R. Machine Learning Meets Quantum Physics; Sch\u00fctt, K. T.; Chmiela, S.; von Lilienfeld, O. A.; Tkatchenko, A.; Tsuda, K.; M\u00fcller, K.R., Eds. Springer International Publishing: Cham, 2020; pp 445\u2013467."},{"key":"ref3\/cit3","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.1608"},{"key":"ref4\/cit4","doi-asserted-by":"publisher","DOI":"10.1021\/jacs.2c13467"},{"key":"ref5\/cit5","unstructured":"Gebauer, N.; Gastegger, M.; Sch\u00fctt, K. Symmetry-Adapted Generation of 3d Point Sets for the Targeted Discovery of Molecules; Advances in Neural Information Processing Systems 32 (NeurIPS 2019); NIPS, 2019."},{"key":"ref6\/cit6","doi-asserted-by":"publisher","DOI":"10.1038\/s43588-022-00391-1"},{"key":"ref7\/cit7","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-28526-y"},{"key":"ref8\/cit8","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.2c00177"},{"key":"ref9\/cit9","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00572"},{"key":"ref10\/cit10","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.7b00690"},{"key":"ref11\/cit11","doi-asserted-by":"publisher","DOI":"10.1021\/acs.molpharmaceut.7b00346"},{"key":"ref12\/cit12","unstructured":"Tazhigulov, R. N.; Schiller, J.; Oppenheim, J.; Winston, M. Molecular Fingerprints for Robust and Efficient ML-Driven Molecular Generation. 2022. arXiv:2211.09086. arXiv.org e-Printarchive. https:\/\/doi.org\/10.48550\/arXiv.2211.09086."},{"key":"ref13\/cit13","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-025-00982-3"},{"key":"ref14\/cit14","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/aca1f7"},{"key":"ref15\/cit15","unstructured":"Hoogeboom, E.; Satorras, V. G.; Vignac, C.; Welling, M. Equivariant Diffusion for Molecule Generation in 3D; Proceedings of the 39th International Conference on Machine Learning; PMLR, 2022; pp 8867\u20138887."},{"key":"ref16\/cit16","unstructured":"Xu, M.; Powers, A. S.; Dror, R. O.; Ermon, S.; Leskovec, J. Geometric Latent Diffusion Models for 3D Molecule Generation; Proceedings of the 40th International Conference on Machine Learning; ACM, 2023; pp 38592\u201338610."},{"key":"ref17\/cit17","unstructured":"Hua, C.; Luan, S.; Xu, M.; Ying, Z.; Fu, J.; Ermon, S.; Precup, D. MUDiff: Unified Diffusion for Complete Molecule Generation; Proceedings of the Second Learning on Graphs Conference; PMLR, 2024; pp 33:1\u201333:26."},{"key":"ref18\/cit18","unstructured":"Cornet, F.; Bartosh, G.; Schmidt, M. N.; Naesseth, C. A. Equivariant Neural Diffusion for Molecule Generation; Advances in Neural Information Processing Systems 37 (NeurIPS 2024); NIPS, 2024; pp 49429\u201349460."},{"key":"ref19\/cit19","unstructured":"Jo, J.; Lee, S.; Hwang, S. J. Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations; Proceedings of the 39th International Conference on Machine Learning; PMLR, 2022; pp 10362\u201310383."},{"key":"ref20\/cit20","unstructured":"Sch\u00fctt, K. T.; Kindermans, P. J.; Sauceda, H. E.; Chmiela, S.; Tkatchenko, A.; M\u00fcller, K. R. SchNet: A Continuous-Filter Convolutional Neural Network for Modeling Quantum Interactions; Advances in Neural Information Processing Systems 30 (NIPS 2017); NIPS, 2017; pp 992\u20131002."},{"key":"ref21\/cit21","doi-asserted-by":"publisher","DOI":"10.1063\/1.5019779"},{"key":"ref22\/cit22","unstructured":"Koczor-Benda, Z.; Chaudhuri, S.; Gilkes, J.; Bartucca, F.; Li, L.; Maurer, R. J. Generative design of functional organic molecules for terahertz radiation detection. 2025. arXiv:2503.14748. arXiv.org e-Printarchive. https:\/\/doi.org\/10.48550\/arXiv.2503.14748."},{"key":"ref23\/cit23","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpcb.1c06437"},{"key":"ref24\/cit24","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2014.22"},{"key":"ref25\/cit25","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-020-0385-y"},{"key":"ref26\/cit26","doi-asserted-by":"publisher","DOI":"10.1039\/D1SC01542G"},{"key":"ref27\/cit27","doi-asserted-by":"publisher","DOI":"10.1126\/science.abk2593"},{"key":"ref28\/cit28","doi-asserted-by":"publisher","DOI":"10.1126\/science.abk3106"},{"key":"ref29\/cit29","doi-asserted-by":"publisher","DOI":"10.1021\/ci300415d"},{"key":"ref30\/cit30","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevX.11.041035"},{"key":"ref31\/cit31","unstructured":"Gebauer, N.; Sch\u00fctt, K. T. Conditional G-SchNet Extension for SchNetPack 2.0 - A Generative Neural Network for 3d Molecules; GitHub. https:\/\/github.com\/atomistic-machine-learning\/schnetpack-gschnet."},{"key":"ref32\/cit32","doi-asserted-by":"publisher","DOI":"10.1021\/ci00057a005"},{"key":"ref33\/cit33","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-3-33"},{"key":"ref34\/cit34","unstructured":"Landrum, G. RDKit: Open-source cheminformatics; RDKit. http:\/\/www.rdkit.org\/."},{"key":"ref35\/cit35","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevB.87.184115"},{"key":"ref36\/cit36","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpc.2019.106949"},{"key":"ref37\/cit37","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009783824328"},{"key":"ref38\/cit38","doi-asserted-by":"publisher","DOI":"10.1145\/3068335"},{"key":"ref39\/cit39","first-page":"2825","volume":"23","author":"Pedregosa F.","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref40\/cit40","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpcc.3c06648"},{"key":"ref41\/cit41","unstructured":"Sch\u00fctt, K. T.; Unke, O. T.; Gastegger, M. Equivariant message passing for the prediction of tensorial properties and molecular spectra. 2021.arXiv:2102.03150. arXiv.org e-Printarchive. https:\/\/doi.org\/10.48550\/arXiv.2102.03150."}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.5c00665","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.5c00665","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,19]],"date-time":"2025-07-19T07:26:53Z","timestamp":1752910013000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.jcim.5c00665"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,25]]},"references-count":41,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2025,7,14]]}},"alternative-id":["10.1021\/acs.jcim.5c00665"],"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.5c00665","relation":{},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"type":"print","value":"1549-9596"},{"type":"electronic","value":"1549-960X"}],"subject":[],"published":{"date-parts":[[2025,6,25]]}}}