[1]	Afzal M A F, Hachmann J. Handbook on Big Data and Machine Learning in the Physical Sciences. Singapore: World Scientific, 2020. 1. Cited in this article [1]

[2]	Xu D G, Zhang Q, Huo X Y, Wang Y T, Yang M L. Mater. Genome Eng. Adv., 2023, 1: e11. Cited in this article [1]

[3]	Isayev O, Fourches D, Muratov E, Oses C, Rasch K, Tropscha A, Curtarolo S. Bull. Am. Phys. Soc., 2014, 39799817. Cited in this article [1]

[4]	Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C. NPJ Comput. Mater., 2017, 3: 54. Cited in this article [1]

[5]	Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. In Neural Information Processing Systems. Nevada: NeurIPS, 2013. 16447573. Cited in this article [2]

[6]	Mikolov T, Chen K, Corrado G, Dean J. Efficient Estimation of Word Representations in Vector Space. [2013-09-07]. https://doi.org/10.48550/arXiv.1301.3781. Cited in this article [3]

[7]	Yaghoobi M, Alaei M. Comput. Mater. Sci., 2022, 207: 111284. Cited in this article [1]

[8]	Karim A, Singh J, Mishra A, Dehzangi A, Newto, M A H, Sattar A. Lecture Notes in Computer Science. Eds.: Ohara K, Bai Q. Cham: Springer, 2019. 11669: 142. Cited in this article [3]

[9]	Karim A, Riahi V, Mishra A, Hakim Newton M A, Dehzangi A, Balle T, Sattar A. ACS Omega, 2021, 6(18): 12306. Cited in this article [3]

[10]	Manolache A, Tantaru D, Niepert M. MolMix: A Simple Yet Effective Baseline for Multimodal Molecular Representation Learning. [2024-10-24]. https://doi.org/10.48550/arXiv.2410.07981. Cited in this article [2]

[11]	Devata S, Sridharan B, Mehta S, Pathak Y, Laghuvarapu S, Varma G, Priyakumar U D. Digit. Discov., 2024, 3(4): 818. Cited in this article [2]

[12]	Huang E, Yang J S, Liao K Y K, Tseng W C W, Lee C K, Gill M, Compas C B, See S, Tsai F J. Sci. Rep., 2024, 271087139. Cited in this article [2]

[13]	Bengio Y, Ducharme R, Vincent P. In Neural Information Processing Systems. Colorado: NeurIPS, 2000, 13. Cited in this article [1]

[14]	Pennington J, Socher R, Manning C. 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Stroudsburg: ACL, 2014. 1532. Cited in this article [2]

[15]	Bojanowski P, Grave E, Joulin A, Mikolov T. Trans. Assoc. Comput. Linguist., 2017, 5: 135. Cited in this article [2]

[16]	Deerwester S, Dumais S T, Furnas G W, Landauer T K, Harshman R. J. Am. Soc. Inf. Sci., 1990, 41(6): 391 Cited in this article [1]

[17]	Blei D M, Ng A Y, Jordan M I. J. Mach. Learn. Res. 2003, 3(1): 993. Cited in this article [1]

[18]	Peters M E, Neumann M, Iyyer M, Gardner M, Clark C, Lee K, Zettlemoyer L. Deep Contextualized Word Representations. [2018-02-15]. https://doi.org/10.48550/arXiv.1802.05365. Cited in this article [1]

[19]	Devlin J, Chang M W, Lee K, Toutanova K. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Minnesota: ACL, 2019. 4171. Cited in this article [1]

[20]	Belkin M, Niyogi P. Neural Comput., 2003, 15(6): 1373. Cited in this article [1]

[21]	Ezzat A, Wu M, Li X L, Kwoh C. Methods, 2017, 129: 81. Cited in this article [1]

[22]	Perozzi B, Al-Rfou R, Skiena S. 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2014. 701. Cited in this article [1]

[23]	Grover A, Leskovec J. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. California: ACM, 2016, 855. Cited in this article [1]

[24]	Hamilton W, Ying Z, Leskovec J. Neural Information Processing Systems. California: NeurIPS, 2017. 30. Cited in this article [1]

[25]	Kipf T N, Welling M. Semi-Supervised Classification with Graph Convolutional Networks. [2017-02-22]. https://doi.org/10.48550/arXiv.1609.02907. Cited in this article [2]

[26]	Peng Y, Qi J. Multimedia Comput. Commun. Appl., 2019, 15(1): 1. Cited in this article [1]

[27]	Rasiwasia N, Costa Pereira J, Coviello E, Doyle G, Lanckriet G R G, Levy R, Vasconcelos N. 18th ACM International Conference on Multimedia. Firenze: ACM, 2010. 251. Cited in this article [1]

[28]	Guo W Z, Wang J W, Wang S P. IEEE Access, 2019, 7: 63373. Cited in this article [1]

[29]	Rupp M, Tkatchenko A, Müller K, von Lilienfeld O A. Phys. Rev. Lett., 2012, 108(5): 058301. Cited in this article [1]

[30]	Ward L, Liu R Q, Krishna A, Hegde V I, Agrawal A, Choudhary A, Wolverton C. Phys. Rev. B, 2017, 96(2): 024104. Cited in this article [2]

[31]	Liu K, Sun X, Jia L, Ma J, Xing H, Wu J, Gao H, Sun Y, Boulnois F, Fan J. Int. J. Mol. Sci., 2019, 20(14): 3389. Cited in this article [1]

[32]	Kearnes S, McCloskey K, Berndl M, Pande V, Riley P. J. Comput. Aided Mol. Des., 2016, 30(8): 595. Cited in this article [3]

[33]	Coley C W, Barzilay R, Green W, Jaakkola T, Jensen K. J. Chem. Inf. Model., 2017, 57(8): 1757. Cited in this article [2]

[34]	Yang K, Swanson K, Jin W G, Coley C, Eiden P, Gao H, Guzman-Perez A, Hopper T, Kelley B, Mathea M, Palmer A, Settels V, Jaakkola T, Jensen K, Barzilay R. J. Chem. Inf. Model., 2019, 59(8): 3370. Cited in this article [1]

[35]	Xie T, Grossman J. Phys. Rev. Lett., 2018, 120(14): 145301. Cited in this article [1]

[36]	Ramakrishnan R, Dral P O, Rupp M, von Lilienfeld O A, Sci. Data, 2014, 1: 140022. Cited in this article [1]

[37]	Chen C, Ye W K, Zuo Y X, Zheng C, Ong S. Chem. Mater., 2019, 31(9): 3564. Cited in this article [3]

[38]	Venugopal V, Olivetti E. Sci. Data, 2024, 11(1): 217. Cited in this article [1]

[39]	Tshitoyan V, Dagdelen J, Weston L, Dunn A, Rong Z Q, Kononova O, Persson K A, Ceder G, Jain A. Nature, 2019, 571(7763): 95 Cited in this article [2]

[40]	Consonni V, Ballabio D, Todeschini R. Cheminformatics, QSAR and Machine Learning Applications for Novel Drug Development. New York: Academic Press, 2023. 303. Cited in this article [1]

[41]	Consonni V, Todeschini R. Statistical Modelling of Molecular Descriptors in QSAR/QSPR. Dehmer M, Varmuza K, Bonchev D (Eds.). New Jersey: Wiley, 2012, 111. Cited in this article [1]

[42]	Amar Y, Schweidtmann A M, Deutsch P, Cao L W, Lapkin A. Chem. Sci., 2019, 10(27): 6697. Cited in this article [1]

[43]	Durant J L, Leland B A, Henry D R, Nourse J G. J. Chem. Inf. Comput. Sci., 2002, 42(6): 1273. Cited in this article [1]

[44]	Rogers D, Hahn M. J. Chem. Inf. Model., 2010, 50(5): 742. Cited in this article [2]

[45]	Vidal D, Thormann M, Pons M. J. Chem. Inf. Model., 2005, 45(2): 386. Cited in this article [1]

[46]	Schwartz J, Awale M, Reymond J. J. Chem. Inf. Model., 2013, 53(8): 1979. Cited in this article [1]

[47]	Bender A, Jenkins J L, Glick M, Deng Z, Nettles J H, Davies J W. J. Chem. Inf. Model., 2006, 46(6): 2445. Cited in this article [1]

[48]	Nidhi, Glick M, Davies J, Jenkins J. J. Chem. Inf. Model., 2006, 46(3): 1124. Cited in this article [1]

[49]	Laufkötter O, Sturm N, Bajorath J, Chen H M, Engkvist O. J. Cheminform, 2019, 11(1): 54. Cited in this article [1]

[50]	David L, Thakkar A, Mercado R, Engkvist O. J. Cheminform, 2020, 12(1): 56. Cited in this article [1]

[51]	Yu L, Sun L L, Du B W, Lv W F. Adv. Neural Inform. Process. Syst., 2023, 36: 67686. Cited in this article [1]

[52]	Yuan H N, Sun Q Y, Fu X C, Zhang Z W, Ji C, Peng H, Li J X. Neural Information Processing Systems. New York: Curran Associates, 2024, 36. Cited in this article [1]

[53]	Faber F A, Hutchison L, Huang B, Gilmer J, Schoenholz S S, Dahl G E, Vinyals O, Kearnes S, Riley P F, von Lilienfeld O A. J. Chem. Theory Comput., 2017, 13(11): 5255. Cited in this article [1]

[54]	Choudhary K, Garrity K, Ghimire N, Anand N, Tavazza F. Phys. Rev. B, 2021, 103(15): 155131. Cited in this article [1]

[55]	Choudhary K, Garrity K F, Tavazza F. J. Phys. Condens. Matter, 2020, 32(47): 475501. Cited in this article [1]

[56]	Liu C H, Tao Y Z, Hsu D, Du Q, Billinge S. Acta Crystallogr. A: Found. Adv., 2019, 75(4): 633. Cited in this article [1]

[57]	Xu K, Hu W, Leskovec J, Jegelka S. How Powerful Are Graph Neural Networks? [2018-10-01]. https://doi.org/10.48550/arXiv.1810.00826. Cited in this article [1]

[58]

Battaglia P, Hamrick J B, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, Tacchetti A, Raposo D, Santoro A, Faulkner R, Gülçehre Ç, Song H F, Ballard A J, Gilmer J, Dahl G E, Vaswani A, Allen K R, Nash C, Langston V, Dyer C, Heess N, Wierstra D, Kohli P, Botvinick M, Vinyals O, Li Y J, Pascanu R. Relational Inductive Biases, Deep Learning, and Graph Networks. [2018-06-04]. https://doi.org/10.48550/arXiv.1806.01261. Cited in this article [1]

[59]	Gasteiger J, Giri S, Margraf J T, Günnemann S. Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules. [2022-05-05]. https://doi.org/10.48550/arXiv.2011.14115. Cited in this article [2]

[60]	Flam-Shepherd D, Wu T C, Friederich P, Aspuru-Guzik A. Mach. Learn. Sci. Technol., 2021, 2(4): 045009. Cited in this article [1]

[61]	Gasteiger J, Becker F, Günnemann S. Neural Information Processing Systems. New York: Curran Associates, 2021. 34: 6790. Cited in this article [1]

[62]	Schütt K T, Sauceda H E, Kindermans P J, Tkatchenko A, Müller K R. J. Chem. Phys., 2018, 148(24): 241722. Cited in this article [2]

[63]	Unke O T, Meuwly M. J. Chem. Theory Comput., 2019, 15(6): 3678. Cited in this article [1]

[64]	Gasteiger J, Groß J, Günnemann S. Directional Message Passing for Molecular Graphs. [2022-04-05]. https://doi.org/10.48550/arXiv.2003.03123. Cited in this article [1]

[65]	Chen Z H, You Z H, Guo Z H, Yi H C, Luo G X, Wang Y B. Front. Bioeng. Biotechnol., 2020, 8: 338. Cited in this article [1]

[66]	Jo J, Baek J, Lee S, Kim D, Kang M, Hwang S J. Neural Information Processing Systems. New York: Curran Associates, 2021. 34: 7534. Cited in this article [1]

[67]	Gilmer J, Schoenholz S S, Riley P F, Vinyals O, Dahl G E. International Conference on Machine Learning. Sydney: PMLR, 2017. 1263. Cited in this article [1]

[68]	Dwivedi V P, Joshi C K, Luu A T, Laurent T, Bengio Y, Bresson X. J. Mach. Learn. Res., 2023, 24(43): 1. Cited in this article [1]

[69]	Choudhary K, DeCost B L. NPJ Comput. Mater., 2021, 7(1): 185. Cited in this article [1]

[70]	Liao Y L, Smidt T, Shuaibi M, Da A. Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields. [2024-12-19]. https://doi.org/10.48550/arXiv.2403.09549. Cited in this article [1]

[71]	Barroso-Luque L, Shuaibi M, Fu X, Wood B M, Dzamba M, Gao M, Rizvi A, Zitnick C L, Ulissi Z W. Open Materials 2024 (OMat24) Inorganic Materials Dataset and Models. [2024-10-16]. https://arxiv.org/abs/2410.127711 https://arxiv.org/abs/2410.127711 Cited in this article [1]

[72]	Jaeger S, Fulle S, Turk S. J. Chem. Inf. Model., 2018, 58(1): 27. Cited in this article [2]

[73]	Mann V, Brito K, Gani R, Venkatasubramanian V. Fluid Phase Equilib., 2022, 561: 113531. Cited in this article [2]

[74]	Bechhofer S, Harmelen F V, Hendler J, Horrocks I, McGuinness D L, Patel-Schneider P, Stein L. OWL Web Ontology Language Reference. [2024-02-10]. http://www.w3.org/TR/2004/rec-owl-ref-20040210/ http://www.w3.org/TR/2004/rec-owl-ref-20040210/ Cited in this article [1]

[75]	Chen J Y, Hu P, Jimenez-Ruiz E, Holter O M, Antonyrajah D, Horrocks I. Mach. Learn., 2021, 1813. Cited in this article [1]

[76]	Goh G B, Hodas N O, Siegel C, Vishnu A. SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties. [2018-03-18]. https://arxiv.org/abs/1712.02034 https://arxiv.org/abs/1712.02034 Cited in this article [1]

[77]	Jeon W, Kim D. Bioinformatics, 2019, 35(23): 4979. Cited in this article [1]

[78]	Zang X, Zhao X B, Tang B Z. Commun. Chem., 2023, 6(1): 34. Cited in this article [2]

[79]	Wang S, Guo Y Z, Wang Y H, Sun H M, Huang J Z. 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics. New York: ACM, 2019. 429. Cited in this article [2]

[80]	Guo Z J, Zhang Y, Lu W. Attention Guided Graph Convolutional Networks for Relation Extraction. [2019-08-02]. https://www.aclweb.org/anthology/P19-1024/ https://www.aclweb.org/anthology/P19-1024/ Cited in this article [1]

[81]	Qin L, Dong G C, Peng J. 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Seoul: IEEE, 2020. 708. Cited in this article [1]

[82]	Li J, Jiang X. Wirel. Commun. Mob. Computi., 2021, 2021(1): 7181815. Cited in this article [2]

[83]	Liu Y W, Zhang R S, Li T F, Jiang J, Ma J, Wang P. J. Mol. Graph. Model., 2023, 118: 108344. Cited in this article [2]

[84]	Tang Q, Nie F L, Zhao Q, Chen W. Brief. Bioinform., 2022, 23(5): bbac357. Cited in this article [2]

[85]	Chithrananda S, Grand G, Ramsundar B. ChemBERTa: Large-Scale Self-Supervised Pretrainfab shing for Molecular Property Prediction. [2020-10-19]. https://arxiv.org/abs/2010.09885 https://arxiv.org/abs/2010.09885 Cited in this article [2]

[86]	Ghaayathri Devi K, Bedadhala R S, Sachin Kumar S, Soman K P, Bodapatiq J D. 2024 2024 4th International Conference on Intelligent Technologies (CONIT). Bangalore: IEEE, 2024. 1. Cited in this article [1]

[87]	Ross J, Belgodere B M, Chenthamarakshan V, Padhi I, Mroueh Y, Das P. Nat. Mach. Intell., 2022, 4(12): 1256. Cited in this article [2]

[88]	Wu F, Radev D, Li S Z. Proc. AAAI Conf. Artif. Intell., 2023, 37(4): 5312. Cited in this article [2]

[89]	Wan F P, Zeng J Y. D. Deep Learning with Feature Embedding for Compound-Protein Interaction Prediction. [2016-11-07]. https://www.biorxiv.org/content/10.1101/086033v1 https://www.biorxiv.org/content/10.1101/086033v1 Cited in this article [2]

[90]	Olivecrona M, Blaschke T, Engkvist O, Chen H M. J. Cheminf., 2017, 9: 48. Cited in this article [1]

[91]	Schneider N, Fechner N, Landrum G, Stiefl N. J. Chem. Inf. Model., 2017, 57(8): 1816. Cited in this article [1]

[92]	Mann V, Venkatasubramanian V. AIChE J., 2021, 67(3): e17190. Cited in this article [1]

[93]	Hinton G E, Salakhutdinov R R. Science, 2006, 313(5786): 504. Cited in this article [1]

[94]	Gómez-Bombarelli R, Wei J N, Duvenaud D, Hernández-Lobato J M, Sánchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel T D, Adams R P, Aspuru-Guzik A. ACS Cent. Sci., 2018, 4(2): 268. Cited in this article [2]

[95]	Winter R, Montanari F, Noé F, Clevert D A. Chem. sci., 2019, 10(6): 1692. Cited in this article [1]

[96]	Popova M, Isayev O, Tropsha A. Sci. Adv., 2018, 4(7): eaap7885. Cited in this article [1]

[97]	Segler M H S, Kogej T, Tyrchan C, Waller M. ACS Cent. Sci., 2018, 4(1): 120. Cited in this article [1]

[98]	Xu Z, Wang S, Zhu F Y, Huang J Z. 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics. Boston: ACM, 2017. 285. Cited in this article [2]

[99]	Hou Y Y, Wang S Y, Bai B, Chan H C S, Yuan S G. Molecules, 2022, 27(5): 1668. Cited in this article [1]

[100]

Lv Q J, Chen G X, Zhao L, Zhong W H, Chen C Y. Brief. Bioinform., 2021, 22(6): bbab317. Cited in this article [1]

[101]

Bagal V, Aggarwal R, Vinod P K, Priyakumar U D. J. Chem. Inf. Model., 2022, 62(9): 2064. Cited in this article [1]

[102]

Radford A, Narasimhan K, Salimans T, Sutskever I. Improving Language Understanding by Generative Pre-Training. [2018-06-11]. https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf Cited in this article [1]

[103]

Jin W G, Barzilay R, Jaakkola T. International Conference on Machine Learning. Sweden: PMLR, 2018. 2323. Cited in this article [1]

[104]

Jin W G, Barzilay R, Jaakkola T. International Conference on Machine Learning. Vienna: PMLR, 2020. 4839. Cited in this article [1]

[105]

Gebauer N, Gastegger M, Schütt K T. Neural Information Processing Systems. Vancouver: PMLR, 2019. 32. Cited in this article [1]

[106]

Shi C, Xu M, Zhu Z, Zhang W, Zhang M, Tang J. GraphAF: A Flow-Based Autoregressive Model for Molecular Graph Generation. [2020-02-27]. https://doi.org/10.48550/arXiv.2001.09382. Cited in this article [2]

[107]

Zang C X, Wang F. 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. California: ACM, 2020. 617. Cited in this article [1]

[108]

Kingma D P, Dhariwal P. Neural Information Processing Systems. Montreal: Curran, 2018. 31. Cited in this article [1]

[109]

Peng X, Guan J, Liu Q, Ma J. MolDiff: Addressing the Atom-Bond Inconsistency Problem in 3D Molecule Diffusion Generation. [2023-05-11]. https://doi.org/10.48550/arXiv.2305.07508. Cited in this article [1]

[110]

Oestreich M, Merdivan E, Lee M, Schultze J L, Piraud M, Becker M. J. Cheminf., 2025, 17: 23. Cited in this article [1]

[111]

Krenn M, Häse F, Nigam A, Friederich P, Aspuru-Guzik A. Mach. Learn. Sci. Technol., 2020, 1(4): 045024. Cited in this article [1]

[112]

Eckmann P, Sun K, Zhao B, Feng M, Gilson M K, Yu R. International Conference on Machine Learning, ICML 2022. Vienna: PMLR, 2022. 162: 5777. Cited in this article [1]

[113]

Rombach R, Blattmann A, Lorenz D, Esser P, Ommer B. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). New Orleans: IEEE, 2022, 10674. Cited in this article [1]

[114]

Luo Y, Yan K, Ji S. International Conference on Machine Learning. PMLR, 2021. 7192. Cited in this article [1]

[115]

Imrie F, Bradley A R, van der Schaar M, Deane C M. J. Chem. Inf. Model., 2020, 60(4): 1983. Cited in this article [1]

[116]

Liu M, Yan K, Oztekin B, Ji S. GraphEBM: Molecular Graph Generation with Energy-Based Models. [2021-04-11]. https://doi.org/10.48550/arXiv.2102.00546. Cited in this article [1]

[117]

Welling M.; Teh Y. W. 28th International Conference on Machine Learning, ICML-11. Bellevue: Citeseer, 2011. 681. Cited in this article [1]

[118]

Xu M, Yu L, Song Y, Shi C, Ermon S, Tang J. GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation. [2022-05-06]. https://doi.org/10.48550/arXiv.2203.02923. Cited in this article [1]

[119]

Sohl-Dickstein J, Weiss E, Maheswaranathan N, Ganguli S. International Conference on Machine Learning. Lille: PMLR, 2015. 2256. Cited in this article [1]

[120]

Liu M, Luo Y, Uchino K, Maruhashi K, Ji S. Generating 3D Molecules for Target Protein Binding. [2022-05-30]. https://doi.org/10.48550/arXiv.2204.09410 Cited in this article [1]

[121]

Li Y. Roberta: A Robustly Optimized Bert Pretraining Approach. [2019-07-26]. https://arxiv.org/abs/1907.11692 https://arxiv.org/abs/1907.11692 Cited in this article [1]

[122]

Wu Z Q, Ramsundar B, Feinberg E N, Gomes J, Geniesse C, Pappu A S, Leswing K, Pande V. Chem. Sci., 2018, 9(2): 513. Cited in this article [2]

[123]

Su J L, Ahmed M, Lu Y, Pan S F, Bo W, Liu Y F. Neurocomputing, 2024, 568: 127063. Cited in this article [1]

[124]

Ishida S, Miyazaki T, Sugaya Y, Omachi S. Molecules., 2021, 26(11): 3125. Cited in this article [1]

[125]

Qiu J Z, Chen Q B, Dong Y X, Zhang J, Yang H X, Ding M, Wang K S, Tang J. 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. Virtual Event CA USA: ACM, 2020, 1150. Cited in this article [1]

[126]

Xu M, Wang H, Ni B, Guo H, Tang J. International Conference on Machine Learning. PMLR, 2021. 11548. Cited in this article [1]

[127]

Liu S, Wang H, Liu W, Lasenby J, Guo H, Tang J. Pre-Training Molecular Graph Representation with 3D Geometry.[2022-05-29]. https://doi.org/10.48550/arXiv.2110.07728. Cited in this article [2]

[128]

Veličković P, Fedus W, Hamilton W L, Liò P, Bengio Y, Hjelm R D. Deep Graph Infomax. [2018-12-21]. https://doi.org/10.48550/arXiv.1809.10341. Cited in this article [1]

[129]

You Y, Chen T, Sui Y, Chen T, Wang Z, Shen Y. In Neural Information Processing Systems. Curran Associates, 2020, 33, 5812. Cited in this article [1]

[130]

Zhang Z X, Liu Q, Wang H, Lu C Q, Lee C K. In Neural Information Processing Systems. Curran Associates, 2021, 34: 15870. Cited in this article [1]

[131]

Zhang S, Hu Z, Subramonian A, Sun Y. Motif-Driven Contrastive Learning of Graph Representations. [2021-03-15]. https://doi.org/10.48550/arXiv.2012.12533. Cited in this article [1]

[132]

Wang Y, Wang J, Cao Z, Barati Farimani A. Nat. Mach. Intell., 2022, 4(3): 279. Cited in this article [1]

[133]

Wang Y Y, Magar R, Liang C, Farimani A. J. Chem. Inf. Model., 2022, 62(11): 2713. Cited in this article [1]

[134]

Wang H, Li W, Jin X, Cho K, Ji H, Han J, Burke M D. Chemical-Reaction-Aware Molecule Representation Learning. [2021-10-12]. https://doi.org/10.48550/arXiv.2109.09888. Cited in this article [1]

[135]

Gong X, Liu Q, Han R, Guo Y K, Wang G Y. Neural Netw., 2025, 184: 107088. Cited in this article [1]

[136]

Gong X, Liu M T, Liu Q, Guo Y K, Wang G Y. Pattern Recognit., 2025, 163: 111463. Cited in this article [1]

[137]

Wang C H, Yang Y Q, Song J S, Nan X F. J. Chem. Inf. Model., 2024, 64(19): 7189. Cited in this article [1]

[138]

Guo Z C, Yu W H, Zhang C X, Jiang M, Chawla N V. 29th ACM International Conference on Information & Knowledge Management. Virtual Event Ireland: ACM, 2020. 435. Cited in this article [1]

[139]

Liu J P, Lei X J, Zhang Y C, Pan Y. Comput. Biol. Med., 2023, 153: 106524. Cited in this article [1]

[140]

Nguyen D M H, Lukashina N, Nguyen T, Le A T, Nguyen T, Ho N, Peters J, Sonntag D, Zaverkin V, Niepert M. Structure-Aware E(3)-Invariant Molecular Conformer Aggregation Networks. [2024-08-19]. https://doi.org/10.48550/arXiv.2402.01975. Cited in this article [1]

[141]

Vayer T, Courty N, Tavenard R, Chapel L, Flamary R. In International Conference on Machine Learning. California: PMLR, 2019, 6275. Cited in this article [1]

[142]

Zhu Y, Hwang J, Adams K, Liu Z, Nan B, Stenfors B, Du Y, Chauhan J, Wiest O, Isayev O, Coley C W, Sun Y, Wang W. Learning Over Molecular Conformer Ensembles: Datasets and Benchmarks.[2024-07-28]. https://doi.org/10.48550/arXiv.2310.00115. Cited in this article [1]

[143]

Wang X F, Li Z, Jiang M J, Wang S, Zhang S G, Wei Z Q. J. Chem. Inf. Model., 2019, 59(9): 3817. Cited in this article [1]

[144]

Cai H X, Zhang H M, Zhao D C, Wu J X, Wang L. Brief. Bioinform., 2022, 23(6): bbac408. Cited in this article [1]

[145]

Wang T Y, Sun J Q, Zhao Q. Comput. Biol. Med., 2023, 153: 106464. Cited in this article [1]

[146]

Zhang H H, Wu J T, Liu S C, Han S. Inf. Fusion, 2024, 103: 102092. Cited in this article [1]

[147]

Lu X, Xie L, Xu L, Mao R, Chang S, Xu X. Comput. Struct. Biotechnol. J., 2024, 23, 1666. Cited in this article [1]

[148]

Nan S H, Li Z M, Jin S M, Du W L, Shen W F. Ind. Eng. Chem. Res., 2025, 64(5): 3045. Cited in this article [1]

[149]

Yi W L, Zhang L, Xu Y L, Cheng X P, Chen T Z. Expert Syst. Appl., 2025, 260: 125403. Cited in this article [1]

[150]

Ryu J, Lee M Y, Lee J H, Lee B, Oh K. Bioinformatics, 2020, 36(10): 3049. Cited in this article [1]

[151]

Deng D G, Chen X W, Zhang R C, Lei Z R, Wang X J, Zhou F. J. Chem. Inf. Model., 2021, 61(6): 2697. Cited in this article [1]

[152]

Wu J Z, Su Y, Yang A, Ren J Z, Xiang Y. Comput. Biol. Med., 2023, 165: 107452. Cited in this article [1]

[153]

Zheng Z X, Wang H, Tan Y Y, Liang C, Sun Y S. Expert Syst. Appl., 2023, 234: 121016. Cited in this article [1]

[154]

Stärk H, Beaini D, Corso G, Tossou P, Dallago C, Günnemann S, Liò P. International Conference on Machine Learning. Baltimore: PMLR, 2022. 20479. Cited in this article [1]

[155]

Chen M K, Gong X W, Pan S R, Wu J, Lin F, Du B, Hu W B. Neural Netw., 2025, 184: 107068. Cited in this article [1]

[156]

Chen R Z, Li C Y, Wang L Y, Liu M Q, Chen S G, Yang J H, Zeng X X. Inf. Fusion, 2025, 115: 102784. Cited in this article [2]

[157]

Xiang H X, Jin S T, Xia J, Zhou M, Wang J M, Zeng L, Zeng X X. Thirty-Third International Joint Conference on Artificial Intelligence. Jeju: AAAI Press, IJCAI Organization, 2024. 6107. Cited in this article [1]

[158]

Ma M, Lei X J. Comput. Biol. Med., 2024, 169: 107911. Cited in this article [1]

[159]

Yin R, Liu R Y, Hao X S, Zhou X R, Liu Y, Ma C, Wang W P. IEEE Trans. Image Process., 2024, 34: 3225. Cited in this article [1]

[160]

Chen Z Y, Xie F K, Wan M, Yuan Y, Liu M, Wang Z G, Meng S, Wang Y G. Chin. Phys. B., 2023, 32(11): 118104. Cited in this article [1]

[161]

Grisoni F. Curr. Opin. Struct. Biol., 2023, 79: 102527. Cited in this article [1]

[162]

Luo Y, Zhang J, Fan S, Yang K, Wu Y, Qiao M, Nie Z. BioMedGPT: Open Multimodal Generative Pre-Trained Transformer for BioMedicine. [2023-08-21]. https://doi.org/10.48550/arXiv.2308.09442. Cited in this article [1]

[163]

Xie T, Wan Y, Liu Y, Zeng Y, Wang S, Zhang W, Grazian C, Kit C, Ouyang W, Zhou D, Hoex B. DARWIN 1.5: Large Language Models as Materials Science Adapted Learners. [2025-05-21]. https://doi.org/10.48550/arXiv.2412.11970. Cited in this article [1]

[164]

Liu X, Wang Y, Yang T, Liu X, Wen X D. AlchemBERT: Exploring Lightweight Language Models for Materials Informatics. [2025-02-13]. https://www.cambridge.org/engage/chemrxiv/article-details/6781a6b481d2151a02a3212e https://www.cambridge.org/engage/chemrxiv/article-details/6781a6b481d2151a02a3212e Cited in this article [1]

[165]

Zeng Z N, Yao Y, Liu Z Y, Sun M S. Nat. Commun., 2022, 13(1): 862. Cited in this article [1]

[166]

Su B, Du D, Yang Z, Zhou Y, Li J, Rao A, Sun H, Lu Z, Wen J R. A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language. [2022-09-12]. https://doi.org/10.48550/arXiv.2209.05481. Cited in this article [1]

[167]

Liu S, Nie W, Wang C, Lu J, Qiao Z, Liu L, Tang J, Xiao C, Anandkumar A. Nat. Mach. Intel., 2023, 5(12): 1447. Cited in this article [1]

[168]

Hua Y, Feng Z H, Song X N, Wu X J, Kittler J. Pattern Recognit., 2025, 157: 110887. Cited in this article [1]

[169]

Polat C, Kurban H, Serpedin E, Kurban M. Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding.[2025-05-17]. https://doi.org/10.48550/arXiv.2505.12137. Cited in this article [1]

[170]

Kim S, Chen J, Cheng T J, Gindulyte A, He J, He S Q, Li Q L, Shoemaker B A, Thiessen P A, Yu B, Zaslavsky L, Zhang J, Bolton E E. Nucleic Acids Res., 2025, 53(D1): D1516. Cited in this article [1]

[171]

Arevalo J, Solorio T, Montes-y-Gómez M, González F A. Neural Comput. Appl., 2020, 32(14): 10209. Cited in this article [1]

[172]

Tang X R, Tran A, Tan J, Gerstein M B. Bioinformatics, 2024, 40(Supplement_1): i357. Cited in this article [1]

[173]

Kang C L, Liu X Y, Guo F. The Thirteenth International Conference on Learning Representations. Singapore: ICLR, 2025. Cited in this article [1]

[174]

Chen B, Li C, Dai H, Song L. International Conference on Machine Learning. PMLR, 2020, 1608. Cited in this article [1]

[175]

Liu G, Sun M, Matusik W, Jiang M, Chen J. Multimodal Large Language Models for Inverse Molecular Design with Retrosynthetic Planning. [2024-10-08]. https://arxiv.org/abs/2410.04223 https://arxiv.org/abs/2410.04223 Cited in this article [1]

[176]

Liu G, Xu J, Luo T, Jiang M. Graph Diffusion Transformers for Multi-Conditional Molecular Generation. [2024-10-03]. https://doi.org/10.48550/arXiv.2401.13858. Cited in this article [1]

[177]

Wiercioch M, Kirchmair J. Expert Syst. Appl., 2023, 213: 119055. Cited in this article [1]

[178]

He Z, Chen L, Lv H, Zhou R, Xu J, Chen Y, Hu J, Gao Y. Advanced Intelligent Computing Technology and Applications. Huang D S, Premaratne P, Jin B, Qu B, Jo K H, Hussain A (Eds.). Singapore: Springer, 2023. 14088: 700. Cited in this article [1]

[179]

Wu K D, Wei G. J. Chem. Inf. Model., 2018, 58(2): 520. Cited in this article [1]

[180]

Yap C W. J. Comput. Chem., 2011, 32(7):1466. Cited in this article [1]

[181]

Ding Y, Jiang X Q, Kim Y. Bioinformatics, 2022, 38(10): 2826. Cited in this article [1]

[182]

Schlichtkrull M, Kipf T N, Bloem P, Van Den Berg R, Titov I, Welling M. The Semantic Web. Gangemi A, Navigli R, Vidal M-E, Hitzler P, Troncy R, Hollink L, Tordai A, Alam M (Eds.). Cham: Springer, 2018, 10843: 593. Cited in this article [1]

[183]

Moriwaki H, Tian Y S, Kawashita N, Takagi T. J. Cheminf., 2018, 10: 4. Cited in this article [1]

[184]

Kumar R, Sharma A, Alexiou A, Bilgrami A L, Kamal M A, Ashraf G M. Front. Neurosci., 2022, 16: 858126. Cited in this article [1]

[185]

Zou Z, Zhang Y, Liang L, Wei M, Leng J, Jiang J, Luo Y, Hu W. Nat. Comput. Sci., 2023, 3(11): 957. Cited in this article [1]

[186]

Alberts M, Schilter O, Zipoli F, Hartrampf N, Laino T. Neural Information Processing Systems. Vancouver: Curran Associates, 2024, 37: 125780. Cited in this article [1]

[187]

Guo K, Nan B, Zhou Y, Guo T, Guo Z, Surve M, Liang Z, Chawla N, Wiest O, Zhang X. In Neural Information Processing Systems. Vancouver: Curran Associates, 2024, 37: 134721. Cited in this article [1]

[188]

Guo S B, Jiang J, Ren H, Wang S. J. Phys. Chem. Lett., 2023, 14(33): 7461. Cited in this article [1]

[189]

Yang G, Jiang S, Luo Y, Wang S, Jiang J. J. Phys. Chem. Lett. 2024, 15(34): 8766. Cited in this article [1]

[190]

EdwinChacko, RudraSondhi, Praveen A, Luska K L, Spectro: A Multi-Modal Approach for Molecule Elucidation Using IR and NMR Data. [2024-11-06]. https://www.cambridge.org/engage/chemrxiv/article-details/6724fb5b7be152b1d0ae66f8 https://www.cambridge.org/engage/chemrxiv/article-details/6724fb5b7be152b1d0ae66f8 Cited in this article [1]

[191]

BehnamGhader P, Adlakha V, Mosbach M, Bahdanau D, Chapados N, Reddy S. LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders. [2024-08-21]. https://doi.org/10.48550/arXiv.2404.05961 Cited in this article [1]

[192]

Mirza A, Jablonka K M. Elucidating Structures from Spectra Using Multimodal Embeddings and Discrete Optimization. [2024-11-22]. https://chemrxiv.org/engage/chemrxiv/article-details/673fbcab5a82cea2fa4c4a39 https://chemrxiv.org/engage/chemrxiv/article-details/673fbcab5a82cea2fa4c4a39 Cited in this article [1]

[193]

Rocabert-Oriols P, López N, Heras-Domingo J. Multi-Modal Contrastive Learning for Chemical Structure Elucidation with VibraCLIP. [2025-04-23]. https://www.cambridge.org/engage/chemrxiv/article-details/6807a71c50018ac7c5a0d0cb https://www.cambridge.org/engage/chemrxiv/article-details/6807a71c50018ac7c5a0d0cb Cited in this article [1]

[194]

Radford A, Kim J W, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J. International Conference on Machine Learning. PmLR, 2021, 8748. Cited in this article [1]

[195]

Wang L, Liu S, Rong Y, Zhao D, Liu Q, Wu S, Wang L. MolSpectra: Pre-Training 3D Molecular Representation with Multi-Modal Energy Spectra. [2025-02-22]. https://doi.org/10.48550/arXiv.2502.16284 Cited in this article [1]