{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:12:37Z","timestamp":1777983157948,"version":"3.51.4"},"reference-count":60,"publisher":"American Chemical Society (ACS)","issue":"5","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100019180","name":"HORIZON EUROPE European Research Council","doi-asserted-by":"publisher","award":["101162908"],"award-info":[{"award-number":["101162908"]}],"id":[{"id":"10.13039\/100019180","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Chem. Inf. Model."],"published-print":{"date-parts":[[2026,3,9]]},"DOI":"10.1021\/acs.jcim.5c02645","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T04:00:58Z","timestamp":1771992058000},"page":"2434-2442","source":"Crossref","is-referenced-by-count":0,"title":["ChemTorch: A Deep Learning Framework for Benchmarking and Developing Chemical Reaction Property Prediction Models"],"prefix":"10.1021","volume":"66","author":[{"given":"Jasper","family":"De Landsheere","sequence":"first","affiliation":[{"name":"Institute of Materials Chemistry","place":["Vienna, Austria"]},{"name":"TU Wien","place":["Vienna, Austria"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anton","family":"Zamyatin","sequence":"additional","affiliation":[{"name":"Institute of Materials Chemistry","place":["Vienna, Austria"]},{"name":"TU Wien","place":["Vienna, Austria"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johannes","family":"Karwounopoulos","sequence":"additional","affiliation":[{"name":"Institute of Materials Chemistry","place":["Vienna, Austria"]},{"name":"TU Wien","place":["Vienna, Austria"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8404-6596","authenticated-orcid":true,"given":"Esther","family":"Heid","sequence":"additional","affiliation":[{"name":"Institute of Materials Chemistry","place":["Vienna, Austria"]},{"name":"TU Wien","place":["Vienna, Austria"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"316","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"key":"ref1\/cit1","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-023-00685-0"},{"key":"ref2\/cit2","doi-asserted-by":"publisher","DOI":"10.1126\/science.aar5169"},{"key":"ref3\/cit3","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-023-02043-z"},{"key":"ref4\/cit4","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01529-6"},{"key":"ref5\/cit5","doi-asserted-by":"publisher","DOI":"10.1063\/5.0059742"},{"key":"ref6\/cit6","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ac8f1a"},{"key":"ref7\/cit7","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrev.1c00021"},{"key":"ref8\/cit8","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.7b00577"},{"key":"ref9\/cit9","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpca.3c07240"},{"key":"ref10\/cit10","doi-asserted-by":"publisher","DOI":"10.1039\/D5DD00240K"},{"key":"ref11\/cit11","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpca.2c02614"},{"key":"ref12\/cit12","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpclett.0c00500"},{"key":"ref13\/cit13","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.4c00104"},{"key":"ref14\/cit14","doi-asserted-by":"publisher","DOI":"10.1039\/D1SC02087K"},{"key":"ref15\/cit15","doi-asserted-by":"publisher","DOI":"10.1039\/D1DD00006C"},{"key":"ref16\/cit16","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/abc81d"},{"key":"ref17\/cit17","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-025-01098-4"},{"key":"ref18\/cit18","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-023-00732-w"},{"key":"ref19\/cit19","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00284-w"},{"key":"ref20\/cit20","doi-asserted-by":"publisher","DOI":"10.1039\/D5DD00283D"},{"key":"ref21\/cit21","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-44629-6"},{"key":"ref22\/cit22","doi-asserted-by":"publisher","unstructured":"Anstine, D. M.; Zhao, Q.; Zubatiuk, R.; Zhang, S.; Singla, V.; Nikitin, F.; Savoie, B. M.; Isayev, O. AIMNet2-rxn: A Machine Learned Potential for Generalized Reaction Modeling on a Millions-of-Pathways Scale\n                      ChemRxiv\n                      2025 10.26434\/chemrxiv-2025-hpdmg. (accessed November 01, 2025).","DOI":"10.26434\/chemrxiv-2025-hpdmg"},{"key":"ref23\/cit23","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.9b00576"},{"key":"ref24\/cit24","doi-asserted-by":"publisher","DOI":"10.1016\/j.eng.2022.04.021"},{"key":"ref25\/cit25","doi-asserted-by":"publisher","DOI":"10.1038\/s43588-021-00101-3"},{"key":"ref26\/cit26","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.4c01787"},{"key":"ref27\/cit27","doi-asserted-by":"publisher","DOI":"10.1021\/acsomega.2c03812"},{"key":"ref28\/cit28","doi-asserted-by":"publisher","DOI":"10.1021\/ci5006614"},{"key":"ref29\/cit29","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c00975"},{"key":"ref30\/cit30","doi-asserted-by":"publisher","unstructured":"Gasteiger, J.; Giri, S.; Margraf, J. T.; G\u00fcnnemann, S. Fast and uncertainty-aware directional message passing for non-equilibrium molecules. 2020, arXiv:2011.14115v3. arXiv.org e-Print archive. https:\/\/doi.org\/10.48550\/arXiv.2011.14115 10.48550\/arXiv.2011.14115. (accessed November 01, 2025).","DOI":"10.48550\/arXiv.2011.14115"},{"key":"ref31\/cit31","doi-asserted-by":"publisher","DOI":"10.1039\/D4SC08572H"},{"key":"ref32\/cit32","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00237"},{"key":"ref33\/cit33","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.3c01250"},{"key":"ref34\/cit34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-86362-3_21"},{"key":"ref35\/cit35","unstructured":"Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser; Polosukhin, I. In\n                      Attention is All you Need\n                      , Advances in Neural Information Processing Systems 30; NeurIPS, 2017."},{"key":"ref36\/cit36","doi-asserted-by":"publisher","unstructured":"Xia, J.; Zhao, C.; Hu, B.; Gao, Z.; Tan, C.; Liu, Y.; Li, S.; Li, S. Z. In\n                      Mole-BERT: Rethinking Pre-training Graph Neural Networks for Molecules\n                      , The Eleventh International Conference on Learning Representations; ICLR, 2023 10.26434\/chemrxiv-2023-dngg4. (accessed November 01, 2025).","DOI":"10.26434\/chemrxiv-2023-dngg4"},{"key":"ref37\/cit37","doi-asserted-by":"crossref","unstructured":"Devlin, J.; Chang, M.W.; Lee, K.; Toutanova, K. In\n                      BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding\n                      , Proceedings of NAACL-HLT; Association for Computational Linguistics, 2019; pp 4171\u20134186.","DOI":"10.18653\/v1\/N19-1423"},{"key":"ref38\/cit38","doi-asserted-by":"crossref","unstructured":"Yang, Z.; Yang, D.; Dyer, C.; He, X.; Smola, A.; Hovy, E. In\n                      Hierarchical Attention Networks for Document Classification\n                      , Proceedings of NAACL-HLT; Association for Computational Linguistics, 2016; pp 1480\u20131489.","DOI":"10.18653\/v1\/N16-1174"},{"key":"ref39\/cit39","doi-asserted-by":"publisher","unstructured":"Corso, G.; St\u00e4rk, H.; Jing, B.; Barzilay, R.; Jaakkola, T. Diffdock: Diffusion steps, twists, and turns for molecular docking. 2022, arXiv:2210.01776v2. arXiv.org e-Print archive. https:\/\/doi.org\/10.48550\/arXiv.2210.01776 10.48550\/arXiv.2210.01776. (accessed November 01, 2025).","DOI":"10.48550\/arXiv.2210.01776"},{"key":"ref40\/cit40","doi-asserted-by":"publisher","unstructured":"Lipman, Y.; Chen, R. T.; Ben-Hamu, H.; Nickel, M.; Le, M. Flow matching for generative modeling. 2022, arXiv:2210.02747v2. arXiv.org e-Print archive. https:\/\/doi.org\/10.48550\/arXiv.2210.02747 10.48550\/arXiv.2210.02747. (accessed November 01, 2025).","DOI":"10.48550\/arXiv.2210.02747"},{"key":"ref41\/cit41","unstructured":"Aykent, S.; Xia, T. In\n                      Gotennet: Rethinking Efficient 3d Equivariant Graph Neural Networks\n                      , The Thirteenth International Conference on Learning Representations; ICLR, 2025."},{"key":"ref42\/cit42","doi-asserted-by":"publisher","unstructured":"Ferrer, M.; Deng, B.; Alfonso-Ramos, J.; Stuyver, T. Screening Diels\u2013Alder Reaction Space to Identify Candidate Reactions for Self-Healing Polymer Applications\n                      ChemRxiv\n                      2025 10.26434\/chemrxiv-2025-kv6n0. (accessed November 01, 2025).","DOI":"10.26434\/chemrxiv-2025-kv6n0"},{"key":"ref43\/cit43","unstructured":"Sch\u00fctt, K.; Kindermans, P.J.; Sauceda Felix, H. E.; Chmiela, S.; Tkatchenko, A.; M\u00fcller, K.R. In\n                      Schnet: A Continuous-filter Convolutional Neural Network for Modeling Quantum Interactions\n                      , Advances in Neural Information Processing Systems 30; NeurIPS, 2017."},{"key":"ref44\/cit44","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-29939-5"},{"key":"ref45\/cit45","doi-asserted-by":"publisher","DOI":"10.1109\/2.161279"},{"key":"ref46\/cit46","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/aba822"},{"key":"ref47\/cit47","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-023-01977-8"},{"key":"ref48\/cit48","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-023-02043-z"},{"key":"ref49\/cit49","doi-asserted-by":"publisher","unstructured":"Gasteiger, J.; Gro\u00df, J.; G\u00fcnnemann, S. Directional message passing for molecular graphs. 2020, arXiv:2003.03123v2. arXiv.org e-Print archive. https:\/\/doi.org\/10.48550\/arXiv.2003.03123 10.48550\/arXiv.2003.03123. (accessed November 01, 2025).","DOI":"10.48550\/arXiv.2003.03123"},{"key":"ref50\/cit50","doi-asserted-by":"crossref","unstructured":"Ramp\u00e1\u0161ek, L.; Galkin, M.; Dwivedi, V. P.; Luu, A. T.; Wolf, G.; Beaini, D. In\n                      Recipe for a General, Powerful, Scalable Graph Transformer\n                      , Advances in Neural Information Processing Systems 35; NeurIPS, 2022; pp 14501\u201314515.","DOI":"10.52202\/068431-1054"},{"key":"ref51\/cit51","doi-asserted-by":"publisher","unstructured":"Kipf, T. Semi-supervised classification with graph convolutional networks. 2016, arXiv:1609.02907v4. arXiv.org e-Print archive. https:\/\/doi.org\/10.48550\/arXiv.1609.02907 10.48550\/arXiv.1609.02907. (accessed November 01, 2025).","DOI":"10.48550\/arXiv.1609.02907"},{"key":"ref52\/cit52","doi-asserted-by":"publisher","unstructured":"Bresson, X.; Laurent, T. Residual gated graph convnets. 2017, arXiv:1711.07553v2. arXiv.org e-Print archive. https:\/\/doi.org\/10.48550\/arXiv.1711.07553 10.48550\/arXiv.1711.07553. (accessed November 01, 2025).","DOI":"10.48550\/arXiv.1711.07553"},{"key":"ref53\/cit53","doi-asserted-by":"publisher","unstructured":"Brody, S.; Alon, U.; Yahav, E. How attentive are graph attention networks. 2021, arXiv:2105.14491v3. arXiv.org e-Print archive. https:\/\/doi.org\/10.48550\/arXiv.2105.14491 10.48550\/arXiv.2105.14491. (accessed November 01, 2025).","DOI":"10.48550\/arXiv.2105.14491"},{"key":"ref54\/cit54","doi-asserted-by":"publisher","unstructured":"Xu, K.; Hu, W.; Leskovec, J.; Jegelka, S. How powerful are graph neural networks 2018, arXiv:1810.00826v3. arXiv.org e-Print archive. https:\/\/doi.org\/10.48550\/arXiv.1810.00826 10.48550\/arXiv.1810.00826. (accessed November 01, 2025).","DOI":"10.48550\/arXiv.1810.00826"},{"key":"ref55\/cit55","unstructured":"Corso, G.; Cavalleri, L.; Beaini, D.; Li\u00f2, P.; Veli\u010dkovi\u0107, P. In\n                      Principal Neighbourhood Aggregation For Graph Nets\n                      , Advances in Neural Information Processing Systems 33; NeurIPS Proceedings, 2020; pp 13260\u201313271."},{"key":"ref56\/cit56","doi-asserted-by":"publisher","unstructured":"Luo, Y.; Shi, L.; Wu, X.M. Can Classic GNNs Be Strong Baselines for Graph-level Tasks? Simple Architectures Meet Excellence. 2025, arXiv:2502.09263v3. arXiv.org e-Print archive. https:\/\/doi.org\/10.48550\/arXiv.2502.09263 10.48550\/arXiv.2502.09263. (accessed December 23, 2025).","DOI":"10.48550\/arXiv.2502.09263"},{"key":"ref57\/cit57","doi-asserted-by":"publisher","DOI":"10.1021\/jp953748q"},{"key":"ref58\/cit58","doi-asserted-by":"publisher","unstructured":"Mark, K.; Galustian, L.; Kovar, M. P.P.; Heid, E. Feynman\u2013Kac\u2013Flow: Inference Steering of Conditional Flow Matching to an Energy-Tilted Posterior. 2025, arXiv:2509.01543v1. arXiv.org e-Print archive. https:\/\/doi.org\/10.48550\/arXiv.2509.01543 10.48550\/arXiv.2509.01543. (accessed November 01, 2025).","DOI":"10.48550\/arXiv.2509.01543"},{"key":"ref59\/cit59","first-page":"1","volume":"18","author":"Li L.","year":"2018","journal-title":"J. Mach. Learn. Res."},{"key":"ref60\/cit60","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.5c00359"}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.5c02645","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.5c02645","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T07:41:29Z","timestamp":1773128489000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.jcim.5c02645"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,24]]},"references-count":60,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,3,9]]}},"alternative-id":["10.1021\/acs.jcim.5c02645"],"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.5c02645","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv-2025-9mggj","asserted-by":"object"},{"id-type":"doi","id":"10.26434\/chemrxiv-2025-9mggj-v2","asserted-by":"object"}]},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,24]]}}}