{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T16:04:25Z","timestamp":1768838665317,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"JST through the Establishment of University Fellowships Towards the Creation of Science Technology Innovation","award":["JPMJFS 2132"],"award-info":[{"award-number":["JPMJFS 2132"]}]},{"name":"JSPS  KAKENHI","award":["JP21K19796"],"award-info":[{"award-number":["JP21K19796"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>The relationships between molecular structures and their properties are subtle and complex, and the properties of odor are no exception. Molecules with similar structures, such as a molecule and its optical isomer, may have completely different odors, whereas molecules with completely distinct structures may have similar odors. Many works have attempted to explain the molecular structure-odor relationship from chemical and data-driven perspectives. The Transformer model is widely used in natural language processing and computer vision, and the attention mechanism included in the Transformer model can identify relationships between inputs and outputs. In this paper, we describe the construction of a Transformer model for predicting molecular properties and interpreting the prediction results. The SMILES data of 100,000 molecules are collected and used to predict the existence of molecular substructures, and our proposed model achieves an F1 value of 0.98. The attention matrix is visualized to investigate the substructure annotation performance of the attention mechanism, and we find that certain atoms in the target substructures are accurately annotated. Finally, we collect 4462 molecules and their odor descriptors and use the proposed model to infer 98 odor descriptors, obtaining an average F1 value of 0.33. For the 19 odor descriptors that achieved F1 values greater than 0.45, we also attempt to summarize the relationship between the molecular substructures and odor quality\u00a0through the attention matrix.<\/jats:p>","DOI":"10.1186\/s13321-022-00671-y","type":"journal-article","created":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T05:03:03Z","timestamp":1672290183000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Investigation of the structure-odor relationship using a Transformer model"],"prefix":"10.1186","volume":"14","author":[{"given":"Xiaofan","family":"Zheng","sequence":"first","affiliation":[]},{"given":"Yoichi","family":"Tomiura","sequence":"additional","affiliation":[]},{"given":"Kenshi","family":"Hayashi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,29]]},"reference":[{"key":"671_CR1","doi-asserted-by":"publisher","DOI":"10.3390\/ijms20123018","author":"M Genva","year":"2019","unstructured":"Genva M, Kemene T, Deleu M, Lins L, Fauconnier M-L (2019) Is it possible to predict the odor of a molecule on the basis of its structure? Int J Mol Sci. https:\/\/doi.org\/10.3390\/ijms20123018.\u00a0Accessed on Dec 20 2022","journal-title":"Int J Mol Sci"},{"key":"671_CR2","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms13890","author":"K Sch\u00fctt","year":"2017","unstructured":"Sch\u00fctt K, Arbabzadah F, Chmiela S, M\u00fcller K-R, Tkatchenko A (2017) Quantum-chemical insights from deep tensor neural networks. Nat Commun. https:\/\/doi.org\/10.1038\/ncomms13890.\u00a0Accessed on Dec 20 2022","journal-title":"Nat Commun"},{"key":"671_CR3","unstructured":"Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: International Conference on Machine Learning, PMLR, pp 1263\u20131272"},{"issue":"2","key":"671_CR4","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1021\/acs.jcim.8b00803","volume":"59","author":"S Zheng","year":"2019","unstructured":"Zheng S, Yan X, Yang Y, Xu J (2019) Identifying structure-property relationships through smiles syntax analysis with self-attention mechanism. J Chem Inf Model 59(2):914\u2013923","journal-title":"J Chem Inf Model"},{"issue":"2","key":"671_CR5","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1039\/C7SC02664A","volume":"9","author":"Z Wu","year":"2018","unstructured":"Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, Pappu AS, Leswing K, Pande V (2018) Moleculenet: a benchmark for molecular machine learning. Chem Sci 9(2):513\u2013530","journal-title":"Chem Sci"},{"key":"671_CR6","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.aiopen.2021.01.001","volume":"1","author":"J Zhou","year":"2020","unstructured":"Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: a review of methods and applications. AI Open 1:57\u201381","journal-title":"AI Open"},{"issue":"1","key":"671_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s41109-019-0195-3","volume":"5","author":"NM Kriege","year":"2020","unstructured":"Kriege NM, Johansson FD, Morris C (2020) A survey on graph kernels. Appl Netw Sci 5(1):1\u201342","journal-title":"Appl Netw Sci"},{"key":"671_CR8","unstructured":"Sch\u00fctt K, Unke O, Gastegger M (2021) Equivariant message passing for the prediction of tensorial properties and molecular spectra. In: International Conference on Machine Learning, PMLR, pp 9377\u20139388"},{"key":"671_CR9","unstructured":"Klicpera J, Gro\u00df J, G\u00fcnnemann S (2020) Directional message passing for molecular graphs. arXiv preprint arXiv:2003.03123. Accessed on Dec 20 2022"},{"issue":"1","key":"671_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-021-00519-x","volume":"13","author":"M Matveieva","year":"2021","unstructured":"Matveieva M, Polishchuk P (2021) Benchmarks for interpretation of QSAR models. J Cheminformatics 13(1):1\u201320","journal-title":"J Cheminformatics"},{"issue":"6327","key":"671_CR11","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1126\/science.aal2014","volume":"355","author":"A Keller","year":"2017","unstructured":"Keller A, Gerkin RC, Guan Y, Dhurandhar A, Turu G, Szalai B, Mainland JD, Ihara Y, Yu CW, Wolfinger R et al (2017) Predicting human olfactory perception from chemical features of odor molecules. Science 355(6327):820\u2013826","journal-title":"Science"},{"issue":"22","key":"671_CR12","doi-asserted-by":"publisher","first-page":"11999","DOI":"10.1021\/acs.analchem.7b02389","volume":"89","author":"L Shang","year":"2017","unstructured":"Shang L, Liu C, Tomiura Y, Hayashi K (2017) Machine-learning-based olfactometer: prediction of odor perception from physicochemical features of odorant molecules. Anal Chem 89(22):11999\u201312005","journal-title":"Anal Chem"},{"key":"671_CR13","unstructured":"Sanchez-Lengeling B, Wei JN, Lee BK, Gerkin RC, Aspuru-Guzik A, Wiltschko AB (2019) Machine learning for scent: learning generalizable perceptual representations of small molecules. arXiv preprint arXiv:1910.10685. Accessed on Dec 20 2022"},{"issue":"8","key":"671_CR14","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/s10822-016-9938-8","volume":"30","author":"S Kearnes","year":"2016","unstructured":"Kearnes S, McCloskey K, Berndl M, Pande V, Riley P (2016) Molecular graph convolutions: moving beyond fingerprints. J Comput Aided Mol Des 30(8):595\u2013608","journal-title":"J Comput Aided Mol Des"},{"issue":"1","key":"671_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-73978-1","volume":"10","author":"R Chacko","year":"2020","unstructured":"Chacko R, Jain D, Patwardhan M, Puri A, Karande S, Rai B (2020) Data based predictive models for odor perception. Sci Rep 10(1):1\u201313","journal-title":"Sci Rep"},{"issue":"1","key":"671_CR16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-021-99269-x","volume":"12","author":"T Debnath","year":"2022","unstructured":"Debnath T, Nakamoto T (2022) Predicting individual perceptual scent impression from imbalanced dataset using mass spectrum of odorant molecules. Sci Rep 12(1):1\u20139","journal-title":"Sci Rep"},{"key":"671_CR17","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European Conference on Computer Vision, Springer, pp 213\u2013229","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"671_CR18","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. Accessed on Dec 20 2022"},{"key":"671_CR19","doi-asserted-by":"publisher","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst. https:\/\/doi.org\/10.48550\/arXiv.1706.03762. Accessed on Dec 20 2022","DOI":"10.48550\/arXiv.1706.03762"},{"key":"671_CR20","unstructured":"Fan A, Lavril T, Grave E, Joulin A, Sukhbaatar S (2020) Addressing some limitations of transformers with feedback memory. arXiv preprint arXiv:2002.09402. Accessed on Dec 20 2022"},{"key":"671_CR21","doi-asserted-by":"crossref","unstructured":"Dai Z, Yang Z, Yang Y, Carbonell J, Le QV, Salakhutdinov R (2019) Transformer-xl: attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860. Accessed on Dec 20 2022","DOI":"10.18653\/v1\/P19-1285"},{"key":"671_CR22","doi-asserted-by":"crossref","unstructured":"Huang Z, Liang D, Xu P, Xiang B (2020) Improve transformer models with better relative position embeddings. arXiv preprint arXiv:2009.13658. Accessed on Dec 20 2022","DOI":"10.18653\/v1\/2020.findings-emnlp.298"},{"issue":"1","key":"671_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-00423-w","volume":"12","author":"P Karpov","year":"2020","unstructured":"Karpov P, Godin G, Tetko IV (2020) Transformer-CNN: swiss knife for QSAR modeling and interpretation. J Cheminformatics 12(1):1\u201312","journal-title":"J Cheminformatics"},{"key":"671_CR24","unstructured":"Maziarka \u0141, Danel T, Mucha S, Rataj K, Tabor J, Jastrzkebski S (2020) Molecule attention transformer. arXiv preprint arXiv:2002.08264. Accessed on Dec 20 2022"},{"key":"671_CR25","unstructured":"Maziarka \u0141, Danel T, Mucha S, Rataj K, Tabor J, Jastrz\u0119bski S (2019) Molecule-augmented attention transformer. In: Workshop on Graph Representation Learning, Neural Information Processing Systems"},{"key":"671_CR26","unstructured":"Maziarka \u0141, Majchrowski D, Danel T, Gai\u0144ski P, Tabor J, Podolak I, Morkisz P, Jastrz\u0119bski S (2021) Relative molecule self-attention transformer. arXiv preprint arXiv:2110.05841. Accessed on Dec 20 2022"},{"key":"671_CR27","unstructured":"Hutchinson MJ, Le\u00a0Lan C, Zaidi S, Dupont E, Teh YW, Kim H (2021) Lietransformer: Equivariant self-attention for lie groups. In: International Conference on Machine Learning, PMLR, pp 4533\u20134543"},{"key":"671_CR28","unstructured":"Th\u00f6lke P, De Fabritiis G (2022) Torchmd-net: equivariant transformers for neural network based molecular potentials. arXiv preprint arXiv:2202.02541. Accessed on Dec 20 2022"},{"key":"671_CR29","first-page":"22243","volume":"33","author":"T Chen","year":"2020","unstructured":"Chen T, Kornblith S, Swersky K, Norouzi M, Hinton GE (2020) Big self-supervised models are strong semi-supervised learners. Adv Neural Inf Process Syst 33:22243\u201322255","journal-title":"Adv Neural Inf Process Syst"},{"key":"671_CR30","unstructured":"Chen T, Kornblith S, Norouzi M, Hinton G (2020) A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, PMLR, pp 1597\u20131607."},{"key":"671_CR31","first-page":"18661","volume":"33","author":"P Khosla","year":"2020","unstructured":"Khosla P, Teterwak P, Wang C, Sarna A, Tian Y, Isola P, Maschinot A, Liu C, Krishnan D (2020) Supervised contrastive learning. Adv Neural Inf Process Syst 33:18661\u201318673","journal-title":"Adv Neural Inf Process Syst"},{"issue":"D1","key":"671_CR32","doi-asserted-by":"publisher","first-page":"930","DOI":"10.1093\/nar\/gky1075","volume":"47","author":"D Mendez","year":"2018","unstructured":"Mendez D, Gaulton A, Bento AP, Chambers J, De Veij M, F\u00e9lix E, Magari\u00f1os M, Mosquera J, Mutowo P, Nowotka M, Gordillo-Mara\u00f1\u00f3n M, Hunter F, Junco L, Mugumbate G, Rodriguez-Lopez M, Atkinson F, Bosc N, Radoux C, Segura-Cabrera A, Hersey A, Leach A (2018) ChEMBL: towards direct deposition of bioassay data. Nucleic Acids Res 47(D1):930\u2013940. https:\/\/doi.org\/10.1093\/nar\/gky1075.\u00a0Accessed on Dec 20 2022","journal-title":"Nucleic Acids Res"},{"key":"671_CR33","unstructured":"The good scents company information system. http:\/\/www.thegoodscentscompany.com\/. Accessed on Dec 20 2022"},{"key":"671_CR34","unstructured":"GitHub. https:\/\/github.com\/zhenghah\/0607. Accessed on Dec 20 2022"},{"issue":"1","key":"671_CR35","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12868-016-0287-2","volume":"17","author":"A Keller","year":"2016","unstructured":"Keller A, Vosshall LB (2016) Olfactory perception of chemically diverse molecules. BMC Neurosci 17(1):1\u201317","journal-title":"BMC Neurosci"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-022-00671-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-022-00671-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-022-00671-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,23]],"date-time":"2023-01-23T11:05:55Z","timestamp":1674471955000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-022-00671-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,29]]},"references-count":35,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["671"],"URL":"https:\/\/doi.org\/10.1186\/s13321-022-00671-y","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,29]]},"assertion":[{"value":"13 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 January 2023","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Update","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"We have corrected the link from 'Availability of data\nand materials' section","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"88"}}