{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T17:42:20Z","timestamp":1774892540514,"version":"3.50.1"},"reference-count":68,"publisher":"American Chemical Society (ACS)","issue":"22","license":[{"start":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T00:00:00Z","timestamp":1730764800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["21933010"],"award-info":[{"award-number":["21933010"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22250710136"],"award-info":[{"award-number":["22250710136"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["22333006"],"award-info":[{"award-number":["22333006"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Chem. Inf. Model."],"published-print":{"date-parts":[[2024,11,25]]},"DOI":"10.1021\/acs.jcim.4c01186","type":"journal-article","created":{"date-parts":[[2024,11,5]],"date-time":"2024-11-05T09:12:30Z","timestamp":1730797950000},"page":"8440-8452","source":"Crossref","is-referenced-by-count":14,"title":["ChemXTree: A Feature-Enhanced Graph Neural Network-Neural Decision Tree Framework for ADMET Prediction"],"prefix":"10.1021","volume":"64","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3325-5427","authenticated-orcid":true,"given":"Yuzhi","family":"Xu","sequence":"first","affiliation":[{"name":"Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning and NYU-ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200062, China"},{"name":"Department of Chemistry, New York University, New York, New York 10003, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinxin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States"},{"name":"Department of Materials Science and Engineering, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Xia","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning and NYU-ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200062, China"},{"name":"Department of Chemistry, New York University, New York, New York 10003, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7370-2797","authenticated-orcid":true,"given":"Jiankai","family":"Ge","sequence":"additional","affiliation":[{"name":"Chemical and Biomolecular Engineering, University of Illinois at Urbana\u2212Champaign, Urbana, Illinois 61801, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2250-8548","authenticated-orcid":true,"given":"Cheng-Wei","family":"Ju","sequence":"additional","affiliation":[{"name":"Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois 60615, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2133-1768","authenticated-orcid":true,"given":"Haiping","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen 518055, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4612-1863","authenticated-orcid":true,"given":"John Z.H.","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning and NYU-ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200062, China"},{"name":"Department of Chemistry, New York University, New York, New York 10003, United States"},{"name":"Faculty of Synthetic Biology, Shenzhen Institute of Advanced Technology, Shenzhen 518055, China"},{"name":"Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, 200062 Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"316","published-online":{"date-parts":[[2024,11,5]]},"reference":[{"key":"ref1\/cit1","doi-asserted-by":"publisher","DOI":"10.1016\/j.addr.2015.01.009"},{"key":"ref2\/cit2","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.1429"},{"key":"ref3\/cit3","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.2c00256"},{"key":"ref4\/cit4","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c00075"},{"key":"ref5\/cit5","doi-asserted-by":"publisher","DOI":"10.1021\/ci300367a"},{"key":"ref6\/cit6","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.2c00260"},{"key":"ref7\/cit7","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.2c01393"},{"key":"ref8\/cit8","doi-asserted-by":"publisher","DOI":"10.1016\/j.memsci.2022.120268"},{"key":"ref9\/cit9","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-020-0414-z"},{"key":"ref10\/cit10","doi-asserted-by":"publisher","DOI":"10.3389\/fchem.2019.00895"},{"key":"ref11\/cit11","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00557-6"},{"key":"ref12\/cit12","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btac545"},{"key":"ref13\/cit13","doi-asserted-by":"publisher","DOI":"10.1016\/j.net.2020.04.008"},{"key":"ref14\/cit14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-64185-0_28"},{"key":"ref15\/cit15","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad306"},{"key":"ref16\/cit16","doi-asserted-by":"publisher","DOI":"10.1021\/acs.accounts.0c00699"},{"key":"ref17\/cit17","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbad305"},{"key":"ref18\/cit18","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac408"},{"key":"ref19\/cit19","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00447-x"},{"key":"ref20\/cit20","doi-asserted-by":"publisher","DOI":"10.3390\/ijms24087139"},{"key":"ref21\/cit21","doi-asserted-by":"publisher","DOI":"10.3390\/ijms232113347"},{"key":"ref22\/cit22","doi-asserted-by":"publisher","DOI":"10.1002\/open.202300051"},{"key":"ref23\/cit23","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-016-9938-8"},{"key":"ref24\/cit24","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106491"},{"key":"ref25\/cit25","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-020-00479-8"},{"key":"ref26\/cit26","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106379"},{"key":"ref27\/cit27","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrev.3c00189"},{"key":"ref28\/cit28","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b01184"},{"key":"ref29\/cit29","doi-asserted-by":"publisher","unstructured":"Bashir, S. B.; Farag, M. M.; Hamid, A. K.; Adam, A. A.; Abo-Khalil, A. G.; Bansal, R. A Novel Hybrid CNN-XGBoost Model for Photovoltaic System Power Forecasting.  2024 6th International Youth Conference on Radio Electronics,Electrical and Power Engineering (REEPE), Cairo, Egypt, Feb 29\u2013Mar 02, 2024; pp 1\u20136, 10.1109\/REEPE60449.2024.10479878.","DOI":"10.1109\/REEPE60449.2024.10479878"},{"key":"ref30\/cit30","unstructured":"Shi, S.; Qiao, K.; Yang, J.; Song, B.; Chen, J.; Yan, B. RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection.  \tarXiv:2304.08239, 2023."},{"key":"ref31\/cit31","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c01489"},{"key":"ref32\/cit32","doi-asserted-by":"publisher","unstructured":"Chen, T.; Guestrin, C. Xgboost: A scalable tree boosting system.  KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016; Association for Computing Machinery: New York, NY, 785\u2013794, 10.1145\/2939672.2939785.","DOI":"10.1145\/2939672.2939785"},{"key":"ref33\/cit33","unstructured":"Liu, S.; Demirel, M. F.; Liang, Y. N-gram graph: Simple unsupervised representation for graphs, with applications to molecules.  Advances in Neural Information Processing Systems; Advances in Neural Information Processing Systems 32 (NeurIPS 2019), 2019; Vol. 32."},{"key":"ref34\/cit34","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmech.2022.114611"},{"key":"ref35\/cit35","doi-asserted-by":"publisher","DOI":"10.1016\/j.mtcomm.2023.107577"},{"key":"ref36\/cit36","unstructured":"Frosst, N.; Hinton, G. Distilling a neural network into a soft decision tree arXiv:1711.09784, 2017."},{"key":"ref37\/cit37","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3070575"},{"key":"ref38\/cit38","first-page":"1855","volume":"108","author":"Silva A.","year":"2020","journal-title":"Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics"},{"key":"ref39\/cit39","doi-asserted-by":"crossref","first-page":"340558","DOI":"10.1016\/j.aca.2022.340558","volume":"1244","year":"2023","journal-title":"Anal. Chim. Acta"},{"key":"ref40\/cit40","doi-asserted-by":"publisher","DOI":"10.1039\/C7SC02664A"},{"key":"ref41\/cit41","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00237"},{"key":"ref42\/cit42","unstructured":"Cho, K.; Van Merri\u00ebnboer, B.; Bahdanau, D.; Bengio, Y.  Analyzing learned molecular representations for property prediction. arXiv preprint arXiv:1409.1259, 2014."},{"key":"ref43\/cit43","unstructured":"Chung, J.; Gulcehre, C.; Cho, K.; Bengio, Y. On the properties of neural machine translation: Encoder-decoder approaches.  arXiv preprint arXiv:1412.3555, 2014."},{"key":"ref44\/cit44","unstructured":"Manu\nJoseph, H. R. GANDALF: Gated Adaptive Network for Deep Automated Learning Learning of Features.  arXiv preprint arXiv:2207.08548, 2023."},{"key":"ref45\/cit45","unstructured":"Hu, W.; Liu, B.; Gomes, J.; Zitnik, M.; Liang, P.; Pande, V.; Leskovec, J. Strategies for pre-training graph neural networks.  arXiv preprint arXiv:1905.12265, 2019."},{"key":"ref46\/cit46","unstructured":"Rong, Y.; Bian, Y.; Xu, T.; Xie, W.; Wei, Y.; Huang, W.; Huang, J. Self-supervised graph transformer on large-scale molecular data.  Advances in Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada, 2020; Vol. 33, pp 12559\u201312571."},{"key":"ref47\/cit47","doi-asserted-by":"crossref","unstructured":"Zhou, G.; Gao, Z.; Ding, Q.; Zheng, H.; Xu, H.; Wei, Z.; Zhang, L.; Ke, G. Uni-Mol: a universal 3D molecular representation learning framework.  International Conference on Learning Representations, 2023.","DOI":"10.26434\/chemrxiv-2022-jjm0j-v4"},{"key":"ref48\/cit48","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.9b00959"},{"key":"ref49\/cit49","unstructured":"Liu, S.; Wang, H.; Liu, W.; Lasenby, J.; Guo, H.; Tang, J. Pre-training Molecular Graph Representation with 3D Geometry.  International Conference on Learning Representations, 2022."},{"key":"ref50\/cit50","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00438-4"},{"key":"ref51\/cit51","unstructured":"Huang, K.; Fu, T.; Gao, W.; Zhao, Y.; Roohani, Y.; Leskovec, J.; Coley, C. W.; Xiao, C.; Sun, J.; Zitnik, M. Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development.  arXiv:2102.09548, 2021."},{"key":"ref52\/cit52","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.3c01250"},{"key":"ref53\/cit53","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-642-24797-2_2","volume-title":"Supervised sequence labelling","author":"Graves A.","year":"2012"},{"key":"ref54\/cit54","doi-asserted-by":"publisher","DOI":"10.1021\/ci100050t"},{"key":"ref55\/cit55","first-page":"2579","volume":"9","author":"Van der Maaten L.","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref56\/cit56","doi-asserted-by":"publisher","DOI":"10.1021\/ci300400a"},{"key":"ref57\/cit57","doi-asserted-by":"publisher","DOI":"10.1002\/minf.201100069"},{"key":"ref58\/cit58","doi-asserted-by":"publisher","DOI":"10.1038\/nbt.1581"},{"key":"ref59\/cit59","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpba.2008.03.023"},{"key":"ref60\/cit60","unstructured":"Veli\u010dkovi\u0107, P.; Cucurull, G.; Casanova, A.; Romero, A.; Lio, P.; Bengio, Y. Graph attention networks.  arXiv:1710.10903, 2017."},{"key":"ref61\/cit61","unstructured":"Maziarka, \u0141.; Danel, T.; Mucha, S.; Rataj, K.; Jastrzebski, S. Molecule attention transformer.  arXiv:2002.08264, 2020."},{"key":"ref62\/cit62","unstructured":"Sun, F.Y.; Hoffmann, J.; Verma, V.; Tang, J. Infograph: Unsupervised and semisupervised graph-level representation learning via mutual information maximization.  arXiv:1908.01000, 2019."},{"key":"ref63\/cit63","unstructured":"Feurer, M.; Klein, A.; Eggensperger, K.; Springenberg, J.; Blum, M.; Hutter, F. Efficient and Robust Automated Machine Learning.  Advances in Neural Information Processing Systems 28 (NIPS 2015); Vol. 28."},{"key":"ref64\/cit64","unstructured":"Ying, C.; Cai, T.; Luo, S.; Zheng, S.; Ke, G.; He, D.; Shen, Y.; Liu, T.Y. Do transformers really perform badly for graph representation? Advances in Neural Information Processing Systems 34 (NeurIPS 2021), 2021; Vol. 34, pp 28877\u201328888."},{"key":"ref65\/cit65","unstructured":"Yang, Y.; Morillo, I. G.; Hospedales, T. M. Deep neural decision trees,  arXiv:1806.06988, 2018."},{"key":"ref66\/cit66","doi-asserted-by":"publisher","DOI":"10.1007\/BF00116037"},{"key":"ref67\/cit67","unstructured":"Badirli, S.; Liu, X.; Xing, Z.; Bhowmik, A.; Keerthi, S. Gradient Boosting Neural Networks: GrowNet.  \tarXiv:2002.07971, 2020."},{"key":"ref68\/cit68","unstructured":"Popov, S.; Morozov, S.; Babenko, A. Gradient Boosting Neural Networks: GrowNet.  International Conference on Learning Representations, 2020."}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.4c01186","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.4c01186","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,25]],"date-time":"2024-11-25T09:11:25Z","timestamp":1732525885000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.jcim.4c01186"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,5]]},"references-count":68,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2024,11,25]]}},"alternative-id":["10.1021\/acs.jcim.4c01186"],"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.4c01186","relation":{},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,5]]}}}