{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T07:55:54Z","timestamp":1786089354983,"version":"3.56.0"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T00:00:00Z","timestamp":1683158400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T00:00:00Z","timestamp":1683158400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Nat Mach Intell"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Deep learning models can accurately predict molecular properties and help making the search for potential drug candidates faster and more efficient. Many existing methods are purely data driven, focusing on exploiting the intrinsic topology and construction rules of molecules without any chemical prior information. The high data dependency makes them difficult to generalize to a wider chemical space and leads to a lack of interpretability of predictions. Here, to address this issue, we introduce a chemical element-oriented knowledge graph to summarize the basic knowledge of elements and their closely related functional groups. We further propose a method for knowledge graph-enhanced molecular contrastive learning with functional prompt (KANO), exploiting external fundamental domain knowledge in both pre-training and fine-tuning. Specifically, with element-oriented knowledge graph as a prior, we first design an element-guided graph augmentation in contrastive-based pre-training to explore microscopic atomic associations without violating molecular semantics. Then, we learn functional prompts in fine-tuning to evoke the downstream task-related knowledge acquired by the pre-trained model. Extensive experiments show that KANO outperforms state-of-the-art baselines on 14 molecular property prediction datasets and provides chemically sound explanations for its predictions. This work contributes to more efficient drug design by offering a high-quality knowledge prior, interpretable molecular representation and superior prediction performance.<\/jats:p>","DOI":"10.1038\/s42256-023-00654-0","type":"journal-article","created":{"date-parts":[[2023,5,4]],"date-time":"2023-05-04T16:02:48Z","timestamp":1683216168000},"page":"542-553","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":203,"title":["Knowledge graph-enhanced molecular contrastive learning with functional prompt"],"prefix":"10.1038","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9538-848X","authenticated-orcid":false,"given":"Yin","family":"Fang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1636-5269","authenticated-orcid":false,"given":"Qiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1970-0678","authenticated-orcid":false,"given":"Ningyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9991-6892","authenticated-orcid":false,"given":"Zhuo","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0253-1476","authenticated-orcid":false,"given":"Xiang","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1928-3878","authenticated-orcid":false,"given":"Xin","family":"Shao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6336-3007","authenticated-orcid":false,"given":"Xiaohui","family":"Fan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5496-7442","authenticated-orcid":false,"given":"Huajun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,4]]},"reference":[{"key":"654_CR1","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1038\/nbt.2786","volume":"32","author":"M Hay","year":"2014","unstructured":"Hay, M., Thomas, D. W., Craighead, J. L., Economides, C. & Rosenthal, J. Clinical development success rates for investigational drugs. Nat. Biotechnol. 32, 40\u201351 (2014).","journal-title":"Nat. Biotechnol."},{"key":"654_CR2","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1038\/d41573-019-00074-z","volume":"18","author":"H Dowden","year":"2019","unstructured":"Dowden, H. & Munro, J. Trends in clinical success rates and therapeutic focus. Nat. Rev. Drug Discov. 18, 495\u2013496 (2019).","journal-title":"Nat. Rev. Drug Discov."},{"key":"654_CR3","doi-asserted-by":"publisher","first-page":"1100","DOI":"10.1093\/nar\/gkr777","volume":"40","author":"A Gaulton","year":"2012","unstructured":"Gaulton, A. et al. Chembl: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40, 1100\u20131107 (2012).","journal-title":"Nucleic Acids Res."},{"key":"654_CR4","doi-asserted-by":"publisher","first-page":"2324","DOI":"10.1021\/acs.jcim.5b00559","volume":"55","author":"T Sterling","year":"2015","unstructured":"Sterling, T. & Irwin, J. J. ZINC 15\u2014ligand discovery for everyone. J. Chem. Inf. Model. 55, 2324\u20132337 (2015).","journal-title":"J. Chem. Inf. Model."},{"key":"654_CR5","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1039\/C7SC02664A","volume":"9","author":"Z Wu","year":"2018","unstructured":"Wu, Z. et al. Moleculenet: a benchmark for molecular machine learning. Chem. Sci. 9, 513\u2013530 (2018).","journal-title":"Chem. Sci."},{"key":"654_CR6","doi-asserted-by":"publisher","first-page":"D1102","DOI":"10.1093\/nar\/gky1033","volume":"47","author":"S Kim","year":"2019","unstructured":"Kim, S. et al. Pubchem 2019 update: improved access to chemical data. Nucleic Acids Res. 47, D1102\u2013D1109 (2019).","journal-title":"Nucleic Acids Res."},{"key":"654_CR7","unstructured":"Hu, W. et al. Strategies for pre-training graph neural networks. In Proc. 8th International Conference on Learning Representations (OpenReview.net, 2020)."},{"key":"654_CR8","unstructured":"Rong, Y. et al. in Advances in Neural Information Processing Systems 33 (eds Larochelle, H. et al.) 12559\u201312571 (Curran Associates, 2020)."},{"key":"654_CR9","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1038\/s42256-021-00438-4","volume":"4","author":"X Fang","year":"2022","unstructured":"Fang, X. et al. Geometry-enhanced molecular representation learning for property prediction. Nat. Mach. Intell. 4, 127\u2013134 (2022).","journal-title":"Nat. Mach. Intell."},{"key":"654_CR10","unstructured":"Zhang, Z., Liu, Q., Wang, H., Lu, C. & Lee, C. in Advances in Neural Information Processing Systems 34 (eds Ranzato, M. et al.) 15870\u201315882 (Curran Associates, 2021)."},{"key":"654_CR11","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1038\/s42256-022-00447-x","volume":"4","author":"Y Wang","year":"2022","unstructured":"Wang, Y., Wang, J., Cao, Z. & Farimani, A. B. Molecular contrastive learning of representations via graph neural networks. Nat. Mach. Intell. 4, 279\u2013287 (2022).","journal-title":"Nat. Mach. Intell."},{"key":"654_CR12","unstructured":"You, Y. et al. in Advances in Neural Information Processing Systems 33 (eds Larochelle, H. et al.) 5812\u20135823 (Curran Associates, 2020)."},{"key":"654_CR13","first-page":"1","volume":"55","author":"P Liu","year":"2023","unstructured":"Liu, P. et al. Pre-train, prompt, and predict: a systematic survey of prompting methods in natural language processing. ACM Comput. Surv. 55, 1\u201335 (2023).","journal-title":"ACM Comput. Surv."},{"key":"654_CR14","unstructured":"Brown, T. et al. in Advances in Neural Information Processing Systems 33 (eds Larochelle, H. et al.) 1877\u20131901 (Curran Associates, 2020)."},{"key":"654_CR15","doi-asserted-by":"crossref","unstructured":"Sainz, O., de Lacalle, O. L., Labaka, G., Barrena, A. & Agirre, E. Label verbalization and entailment for effective zero and few-shot relation extraction. In Proc. 2021 Conference on Empirical Methods in Natural Language Processing (eds Moens, M.-F. et al.) 1199\u20131212 (Association for Computational Linguistics, 2021).","DOI":"10.18653\/v1\/2021.emnlp-main.92"},{"key":"654_CR16","doi-asserted-by":"publisher","first-page":"13099","DOI":"10.1609\/aaai.v36i11.21686","volume":"36","author":"H Ye","year":"2022","unstructured":"Ye, H. et al. Learning to ask for ata-efficient event argument extraction (student abstract). Proc. AAAI Conference on Artificial Intelligence 36, 13099\u201313100 (2022).","journal-title":"Proc. AAAI Conference on Artificial Intelligence"},{"key":"654_CR17","unstructured":"Tsimpoukelli, M. et al. in Advances in Neural Information Processing Systems 34 (eds Ranzato, M. et al.) 200\u2013212 (Curran Associates, 2021)."},{"key":"654_CR18","doi-asserted-by":"publisher","first-page":"8408","DOI":"10.1021\/acs.jmedchem.0c00754","volume":"63","author":"P Ertl","year":"2020","unstructured":"Ertl, P., Altmann, E. & McKenna, J. M. The most common functional groups in bioactive molecules and how their popularity has evolved over time. J. Med. Chem. 63, 8408\u20138418 (2020).","journal-title":"J. Med. Chem."},{"key":"654_CR19","doi-asserted-by":"publisher","first-page":"3896","DOI":"10.1093\/bioinformatics\/btab627","volume":"37","author":"M Delmas","year":"2021","unstructured":"Delmas, M. et al. Building a knowledge graph from public databases and scientific literature to extract associations between chemicals and diseases. Bioinformatics 37, 3896\u20133904 (2021).","journal-title":"Bioinformatics"},{"key":"654_CR20","doi-asserted-by":"crossref","unstructured":"Lin, X., Quan, Z., Wang, Z., Ma, T. & Zeng, X. KGNN: knowledge graph neural network for drug\u2013drug interaction prediction. In Proc. Twenty-Ninth International Joint Conference on Artificial Intelligence (ed. Bessiere, C) 2739\u20132745 (International Joint Conferences on Artificial Intelligence Organization, 2020).","DOI":"10.24963\/ijcai.2020\/380"},{"key":"654_CR21","doi-asserted-by":"publisher","first-page":"1813","DOI":"10.1007\/s10994-021-05997-6","volume":"110","author":"J Chen","year":"2021","unstructured":"Chen, J. et al. Owl2vec*: embedding of OWL ontologies. Mach. Learn. 110, 1813\u20131845 (2021).","journal-title":"Mach. Learn."},{"key":"654_CR22","doi-asserted-by":"crossref","unstructured":"Sun, M., Xing, J., Wang, H., Chen, B. & Zhou, J. MoCL: data-driven molecular fingerprint via knowledge-aware contrastive learning from molecular graph. In Proc. 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (eds Feida, Z. et al.) 3585\u20133594 (ACM, 2021).","DOI":"10.1145\/3447548.3467186"},{"key":"654_CR23","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1093\/nar\/gkv1075","volume":"44","author":"M Kuhn","year":"2016","unstructured":"Kuhn, M., Letunic, I., Jensen, L. J. & Bork, P. The SIDER database of drugs and side effects. Nucleic Acids Res. 44, 1075\u20131079 (2016).","journal-title":"Nucleic Acids Res."},{"key":"654_CR24","doi-asserted-by":"crossref","unstructured":"Riesen, K. & Bunke, H. IAM graph database repository for graph based pattern recognition and machine learning. In Structural, Syntactic, and Statistical Pattern Recognition. SSPR \/SPR 2008. Lecture Notes in Computer Science, Vol. 5342 (eds Lobo, N. V. et al.) 287\u2013297 (Springer, 2008).","DOI":"10.1007\/978-3-540-89689-0_33"},{"key":"654_CR25","unstructured":"Wang, T. & Isola, P. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In Proc. 37th International Conference on Machine Learning (eds Daum\u00e9, H. III & Sing, A.) 9929\u20139939 (PMLR, 2020)."},{"key":"654_CR26","doi-asserted-by":"crossref","unstructured":"Song, Y. et al. Communicative representation learning on attributed molecular graphs. In Proc. Twenty-Ninth International Joint Conference on Artificial Intelligence (ed Bessiere, C.) 2831\u20132838 (International Joint Conferences on Artificial Intelligence Organization, 2020).","DOI":"10.24963\/ijcai.2020\/392"},{"key":"654_CR27","first-page":"3221","volume":"15","author":"L van der Maaten","year":"2014","unstructured":"van der Maaten, L. Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221\u20133245 (2014).","journal-title":"J. Mach. Learn. Res."},{"key":"654_CR28","doi-asserted-by":"publisher","first-page":"2887","DOI":"10.1021\/jm9602928","volume":"39","author":"GW Bemis","year":"1996","unstructured":"Bemis, G. W. & Murcko, M. A. The properties of known drugs. 1. Molecular frameworks. J. Med. Chem. 39, 2887\u20132893 (1996).","journal-title":"J. Med. Chem."},{"key":"654_CR29","doi-asserted-by":"publisher","first-page":"2916","DOI":"10.1214\/10-AOS799","volume":"38","author":"ZI Botev","year":"2010","unstructured":"Botev, Z. I., Grotowski, J. F. & Kroese, D. P. Kernel density estimation via diffusion. Ann. Stat. 38, 2916\u20132957 (2010).","journal-title":"Ann. Stat."},{"key":"654_CR30","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1038\/460208a","volume":"460","author":"T Hartung","year":"2009","unstructured":"Hartung, T. Toxicology for the twenty-first century. Nature 460, 208\u2013212 (2009).","journal-title":"Nature"},{"key":"654_CR31","first-page":"49","volume":"23","author":"RB Fitzpatrick","year":"2004","unstructured":"Fitzpatrick, R. B. Haz-map: information on hazardous chemicals and occupational diseases. Med. Ref. Serv. Q. 23, 49\u201356 (2004).","journal-title":"Med. Ref. Serv. Q."},{"key":"654_CR32","first-page":"618","volume":"44","author":"N Puvaneswari","year":"2006","unstructured":"Puvaneswari, N., Muthukrishnan, J. & Gunasekaran, P. Toxicity assessment and microbial degradation of az dyes. Indian J. Exp. Biol. 44, 618\u2013626 (2006).","journal-title":"Indian J. Exp. Biol."},{"key":"654_CR33","doi-asserted-by":"publisher","first-page":"1936","DOI":"10.1021\/acs.jcim.6b00290","volume":"56","author":"G Subramanian","year":"2016","unstructured":"Subramanian, G., Ramsundar, B., Pande, V. & Denny, R. A. Computational modeling of \u03b2-secretase 1 (BACE-1) inhibitors using ligand based approaches. J. Chem. Inf. Model. 56, 1936\u20131949 (2016).","journal-title":"J. Chem. Inf. Model."},{"key":"654_CR34","doi-asserted-by":"publisher","first-page":"110","DOI":"10.3389\/fmolb.2022.834453","volume":"9","author":"LG Mureddu","year":"2022","unstructured":"Mureddu, L. G. & Vuister, G. W. Fragment-based drug discovery by NMR. Where are the successes and where can it be improved? Front. Mol. Biosci. 9, 110 (2022).","journal-title":"Front. Mol. Biosci."},{"key":"654_CR35","doi-asserted-by":"publisher","first-page":"e0269129","DOI":"10.1371\/journal.pone.0269129","volume":"17","author":"ID Garc\u00eda Mar\u00edn","year":"2022","unstructured":"Garc\u00eda Mar\u00edn, I. D. et al. New compounds from heterocyclic amines scaffold with multitarget inhibitory activity on a\u03b2 aggregation, ache, and bace1 in the alzheimer disease. PLoS ONE 17, e0269129 (2022).","journal-title":"PLoS ONE"},{"key":"654_CR36","doi-asserted-by":"publisher","first-page":"711","DOI":"10.1007\/s10822-014-9747-x","volume":"28","author":"DL Mobley","year":"2014","unstructured":"Mobley, D. L. & Guthrie, J. P. Freesolv: a database of experimental and calculated hydration free energies, with input files. J. Comput. Aided Mol. Design 28, 711\u2013720 (2014).","journal-title":"J. Comput. Aided Mol. Design"},{"key":"654_CR37","doi-asserted-by":"publisher","first-page":"8732","DOI":"10.1021\/ja902302h","volume":"131","author":"LC Blum","year":"2009","unstructured":"Blum, L. C. & Reymond, J.-L. 970 million druglike small molecules for virtual screening in the chemical universe database gdb-13. J. Am. Chem. Soc. 131, 8732\u20138733 (2009).","journal-title":"J. Am. Chem. Soc."},{"key":"654_CR38","doi-asserted-by":"crossref","unstructured":"Li, Y. & Yang, T. in Guide to Big Data Applications (ed. Srinivasan, S.) 83\u2013104 (Springer, 2018).","DOI":"10.1007\/978-3-319-53817-4_4"},{"key":"654_CR39","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S. & Dean, J. in Advances in Neural Information Processing Systems 26 (eds Burges, C. J. et al.) 3111\u20133119 (Curran Associates, 2013)."},{"key":"654_CR40","unstructured":"Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. E. A simple framework for contrastive learning of visual representations. In Proc. 37th International Conference on Machine Learning (eds Daum\u00e9, H. III & Singh, A.) 1597\u20131607 (PMLR, 2020)."},{"key":"654_CR41","first-page":"4","volume":"1","author":"G Landrum","year":"2013","unstructured":"Landrum, G. Rdkit documentation. Release 1, 4 (2013).","journal-title":"Release"},{"key":"654_CR42","unstructured":"Shang, C. et al. Edge attention-based multi-relational graph convolutional networks. Preprint at https:\/\/arxiv.org\/abs\/1802.04944 (2018)."},{"key":"654_CR43","unstructured":"Ba, L. J., Kiros, J. R. & Hinton, G. E. Layer normalization. Preprint at http:\/\/arxiv.org\/abs\/1607.06450 (2016)."},{"key":"654_CR44","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: encoder-decoder approaches. In Proc. SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation (eds Wu, D. et al.) 103\u2013111 (Association for Computational Linguistics, 2014).","DOI":"10.3115\/v1\/W14-4012"},{"key":"654_CR45","unstructured":"Ramsundar, B., Eastman, P., Walters, P. & Pande, V. Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More (O\u2019Reilly Media, 2019)."},{"key":"654_CR46","unstructured":"Liu, S., Demirel, M. F. & Liang, Y. in Advances in Neural Information Processing Systems 32 (eds Wallach, H. et al.) (Curran Associates, 2019)."},{"key":"654_CR47","unstructured":"Kipf, T. N. & Welling, M. Semi-supervised classification with graph convolutional networks. In Proc. 5th International Conference on Learning Representations (OpenReview.net, 2017)."},{"key":"654_CR48","unstructured":"Xu, K., Hu, W., Leskovec, J. & Jegelka, S. How powerful are graph neural networks? In Proc. 7th International Conference on Learning Representations (OpenReview.net, 2019)."},{"key":"654_CR49","unstructured":"Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. Neural message passing for quantum chemistry. In Proc. 34th International Conference on Machine Learning 70 (eds Doina, P. & Teh, Y. W.) 1263\u20131272 (PMLR, 2017)."},{"key":"654_CR50","doi-asserted-by":"crossref","unstructured":"Yang, K. et al. Are learned molecular representations ready for prime time? Preprint at http:\/\/arxiv.org\/abs\/1904.01561 (2019).","DOI":"10.26434\/chemrxiv.7940594.v1"},{"key":"654_CR51","unstructured":"Liu, S. et al. Pre-training molecular graph representation with 3d geometry. In Proc. Tenth International Conference on Learning Representations (OpenReview.net, 2022)."}],"container-title":["Nature Machine Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s42256-023-00654-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-023-00654-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s42256-023-00654-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T23:04:44Z","timestamp":1684796684000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s42256-023-00654-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,4]]},"references-count":51,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["654"],"URL":"https:\/\/doi.org\/10.1038\/s42256-023-00654-0","relation":{},"ISSN":["2522-5839"],"issn-type":[{"value":"2522-5839","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,4]]},"assertion":[{"value":"12 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 April 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 May 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}