{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T12:32:44Z","timestamp":1782736364955,"version":"3.54.5"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T00:00:00Z","timestamp":1728259200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T00:00:00Z","timestamp":1728259200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["FK 515800538"],"award-info":[{"award-number":["FK 515800538"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["P116\/2020"],"award-info":[{"award-number":["P116\/2020"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["FK 515800538"],"award-info":[{"award-number":["FK 515800538"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Leibniz Programme for Women Professors","award":["P116\/2020"],"award-info":[{"award-number":["P116\/2020"]}]},{"DOI":"10.13039\/501100005713","name":"Technische Universit\u00e4t M\u00fcnchen","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005713","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Bitter taste is an unpleasant taste modality that affects food consumption. Bitter peptides are generated during enzymatic processes that produce functional, bioactive protein hydrolysates or during the aging process of fermented products such as cheese, soybean protein, and wine. Understanding the underlying peptide sequences responsible for bitter taste can pave the way for more efficient identification of these peptides.\u00a0This paper presents BitterPep-GCN, a feature-agnostic graph convolution network for bitter peptide prediction. The graph-based model learns the embedding of amino acids in the bitter peptide sequences and uses mixed pooling for bitter classification. BitterPep-GCN was benchmarked using BTP640, a publicly available bitter peptide dataset. The latent peptide embeddings generated by the trained model were used to analyze the activity of sequence motifs responsible for the bitter taste of the peptides. Particularly, we calculated the activity for individual amino acids and dipeptide, tripeptide, and tetrapeptide sequence motifs present in the peptides. Our analyses pinpoint specific amino acids, such as F, G, P, and R, as well as sequence motifs, notably tripeptide and tetrapeptide motifs containing FF, as key bitter signatures in peptides. This work not only provides a new predictor of bitter taste for a more efficient identification of bitter peptides in various food products but also gives a hint into the molecular basis of bitterness.<\/jats:p><jats:p><jats:bold>Scientific Contribution<\/jats:bold><\/jats:p><jats:p>Our work provides the first application of Graph Neural Networks for the prediction of peptide bitter taste. The best-developed model, BitterPep-GCN, learns the embedding of amino acids in the bitter peptide sequences and uses mixed pooling for bitter classification. The embeddings were used to analyze the sequence motifs responsible for the bitter taste.<\/jats:p>","DOI":"10.1186\/s13321-024-00909-x","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T21:01:46Z","timestamp":1728334906000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Bitter peptide prediction using graph neural networks"],"prefix":"10.1186","volume":"16","author":[{"given":"Prashant","family":"Srivastava","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexandra","family":"Steuer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Ferri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Nicoli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristian","family":"Schultz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saptarshi","family":"Bej","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonella","family":"Di Pizio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olaf","family":"Wolkenhauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,10,7]]},"reference":[{"key":"909_CR1","unstructured":"Ihde A.J. (1993). Book Review: The World of Peptides: A Brief History of Peptide Chemistry T. Wieland, M. Bodanszky"},{"key":"909_CR2","volume-title":"Handbook of biologically active peptides","year":"2013","unstructured":"Kastin A (ed) (2013) Handbook of biologically active peptides. Academic press, Cambridge"},{"issue":"12","key":"909_CR3","doi-asserted-by":"publisher","first-page":"4977","DOI":"10.1021\/jm4004285","volume":"57","author":"A Cherkasov","year":"2014","unstructured":"Cherkasov A, Muratov EN, Fourches D, Varnek A, Baskin II, Cronin MT, Dearden JC, Gramatica P, Martin YC, Todeschini R, Consonni V, Kuz\u2019min VE, Cramer RD, Benigni R, Yang C, Rathman JF, Terfloth L, Gasteiger J, Richard AM, Tropsha A (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57(12):4977\u20135010","journal-title":"J Med Chem"},{"issue":"11","key":"909_CR4","doi-asserted-by":"publisher","first-page":"3525","DOI":"10.1039\/D0CS00098A","volume":"49","author":"EN Muratov","year":"2020","unstructured":"Muratov EN, Bajorath J, Sheridan RP, Tetko IV, Filimonov DA, Poroikov VV, Oprea TI, Baskin II, Varnek A, Roitberg AE, Isayev O, Curtalolo S, Fourches D, Cohen Y, Aspuru-Guzik A, Winkler DA, Agrafiotis DK, Cherkasov A, Tropsha A (2020) QSAR without borders. Chem Soc Rev 49(11):3525\u20133564","journal-title":"Chem Soc Rev"},{"key":"909_CR5","first-page":"2431","volume":"13","author":"MR Keyvanpour","year":"2021","unstructured":"Keyvanpour MR, Barani Shirzad M, Moradi F (2021) PCAC: a new method for predicting compounds with activity cliff property in QSAR approach. Int J Inform Technol 13:2431\u20132437","journal-title":"Int J Inform Technol"},{"key":"909_CR6","doi-asserted-by":"publisher","first-page":"2102","DOI":"10.1093\/bioinformatics\/btac020","volume":"38","author":"N Brandes","year":"2021","unstructured":"Brandes N, Ofer D, Peleg Y, Rappoport N, Linial M (2021) ProteinBERT: a universal deep-learning model of protein sequence and function. Bioinformatics 38:2102\u20132110","journal-title":"Bioinformatics"},{"issue":"1","key":"909_CR7","doi-asserted-by":"publisher","first-page":"4348","DOI":"10.1038\/s41467-022-32007-7","volume":"13","author":"N Ferruz","year":"2022","unstructured":"Ferruz N, Schmidt S, H\u00f6cker B (2022) ProtGPT2 is a deep unsupervised language model for protein design. Nat Commun 13(1):4348","journal-title":"Nat Commun"},{"issue":"8","key":"909_CR8","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1016\/j.clinthera.2013.06.007","volume":"35","author":"JA Mennella","year":"2013","unstructured":"Mennella JA, Spector AC, Reed DR, Coldwell SE (2013) The bad taste of medicines: overview of basic research on bitter taste. Clin Ther 35(8):1225\u201346","journal-title":"Clin Ther"},{"issue":"6","key":"909_CR9","doi-asserted-by":"publisher","first-page":"1402","DOI":"10.3390\/ijms20061402","volume":"20","author":"A Di Pizio","year":"2019","unstructured":"Di Pizio A, Behrens M, Krautwurst D (2019) Beyond the flavour: the potential druggability of chemosensory G protein-coupled receptors. Int J Mol Sci 20(6):1402","journal-title":"Int J Mol Sci"},{"issue":"38","key":"909_CR10","doi-asserted-by":"publisher","first-page":"10414","DOI":"10.1021\/acs.jafc.9b07863","volume":"68","author":"A Dunkel","year":"2020","unstructured":"Dunkel A, Hofmann T, Di Pizio A (2020) In silico investigation of bitter hop-derived compounds and their cognate bitter taste receptors. J Agric Food Chem 68(38):10414\u201310423","journal-title":"J Agric Food Chem"},{"key":"909_CR11","doi-asserted-by":"publisher","first-page":"2215","DOI":"10.1007\/s00217-022-04044-5","volume":"248","author":"M Malavolta","year":"2022","unstructured":"Malavolta M, Pallante L, Mavkov B, Stojceski F, Grasso G, Korfiati A, Mavroudi S, Kalogeras AP, Alexakos C, Martos VM, Amoroso D, Di Benedetto G, Piga D, Theofilatos KA, Deriu MA (2022) A survey on computational taste predictors. Eur Food Res Technol 248:2215\u20132235","journal-title":"Eur Food Res Technol"},{"issue":"5","key":"909_CR12","doi-asserted-by":"publisher","first-page":"414","DOI":"10.1111\/j.1399-3011.1991.tb00756.x","volume":"37","author":"S Hellberg","year":"2009","unstructured":"Hellberg S, Eriksson L, Jonsson J, Lindgren F, Sj\u00f6str\u00f6m M, Skagerberg B, Wold S, Andrews PR (2009) Minimum analogue peptide sets (MAPS) for quantitative structure-activity relationships. Int J Pept Protein Res 37(5):414\u201324","journal-title":"Int J Pept Protein Res"},{"issue":"4","key":"909_CR13","doi-asserted-by":"publisher","first-page":"2813","DOI":"10.1016\/j.ygeno.2020.03.019","volume":"112","author":"P Charoenkwan","year":"2020","unstructured":"Charoenkwan P, Yana J, Schaduangrat N, Nantasenamat C, Hasan MM, Shoombuatong W (2020) iBitter-SCM: identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides. Genomics 112(4):2813\u20132822","journal-title":"Genomics"},{"issue":"17","key":"909_CR14","doi-asserted-by":"publisher","first-page":"2556","DOI":"10.1093\/bioinformatics\/btab133","volume":"37","author":"P Charoenkwan","year":"2021","unstructured":"Charoenkwan P, Nantasenamat C, Hasan MM, Manavalan B, Shoombuatong W (2021) BERT4Bitter: a bidirectional encoder representations from transformers (BERT)-based model for improving the prediction of bitter peptides. Bioinformatics 37(17):2556\u20132562","journal-title":"Bioinformatics"},{"issue":"16","key":"909_CR15","doi-asserted-by":"publisher","first-page":"8958","DOI":"10.3390\/ijms22168958","volume":"22","author":"P Charoenkwan","year":"2021","unstructured":"Charoenkwan P, Nantasenamat C, Hasan MM, Moni MA, Lio\u2019 P, Shoombuatong W (2021) iBitter-fuse: a novel sequence-based bitter peptide predictor by fusing multi-view features. Int J Mol Sci 22(16):8958","journal-title":"Int J Mol Sci"},{"issue":"14","key":"909_CR16","doi-asserted-by":"publisher","first-page":"7877","DOI":"10.3390\/ijms23147877","volume":"23","author":"J Jiang","year":"2022","unstructured":"Jiang J, Lin X, Jiang Y, Jiang L, Lv Z (2022) Identify bitter peptides by using deep representation learning features. Int J Mol Sci 23(14):7877","journal-title":"Int J Mol Sci"},{"key":"909_CR17","doi-asserted-by":"publisher","first-page":"1052923","DOI":"10.3389\/fmed.2023.1052923","volume":"10","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Wang Y, Gu Z, Pan X, Li J, Ding H, Zhang Y, Deng K (2023) Bitter-RF: a random forest machine model for recognizing bitter peptides. Front Med 10:1052923","journal-title":"Front Med"},{"issue":"8","key":"909_CR18","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1109\/TCS.1978.1084515","volume":"25","author":"FRANK Rubin","year":"1978","unstructured":"Rubin FRANK (1978) Enumerating all simple paths in a graph. IEEE Trans Circ Syst 25(8):641\u2013642","journal-title":"IEEE Trans Circ Syst"},{"key":"909_CR19","doi-asserted-by":"publisher","DOI":"10.4855\/arXiv.1609.02907","author":"TN Kipf","year":"2016","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint. https:\/\/doi.org\/10.4855\/arXiv.1609.02907","journal-title":"arXiv preprint"},{"issue":"3","key":"909_CR20","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1109\/MSP.2012.2235192","volume":"30","author":"DI Shuman","year":"2013","unstructured":"Shuman DI, Narang SK, Frossard P, Ortega A, Vandergheynst P (2013) The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process Mag 30(3):83\u201398","journal-title":"IEEE Signal Process Mag"},{"key":"909_CR21","doi-asserted-by":"publisher","DOI":"10.4855\/arXiv.1803.02155","author":"P Shaw","year":"2018","unstructured":"Shaw P, Uszkoreit J, Vaswani A (2018) Self-attention with relative position representations. arXiv preprint. https:\/\/doi.org\/10.4855\/arXiv.1803.02155","journal-title":"arXiv preprint"},{"key":"909_CR22","doi-asserted-by":"publisher","DOI":"10.4855\/arXiv.1710.10903","author":"P Velickovic","year":"2017","unstructured":"Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. arXiv preprint. https:\/\/doi.org\/10.4855\/arXiv.1710.10903","journal-title":"arXiv preprint"},{"key":"909_CR23","unstructured":"Hamilton W, Ying Z, Leskovec J. (2017). Inductive representation learning on large graphs. Advances in neural information processing systems, 30"},{"issue":"1","key":"909_CR24","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"issue":"8","key":"909_CR25","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1011288","volume":"19","author":"T Chari","year":"2023","unstructured":"Chari T, Pachter L (2023) The specious art of single-cell genomics. PLOS Comput Biol 19(8):e1011288","journal-title":"PLOS Comput Biol"},{"issue":"1","key":"909_CR26","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1038\/s41387-022-00206-2","volume":"12","author":"S Bej","year":"2022","unstructured":"Bej S, Sarkar J, Biswas S, Mitra P, Chakrabarti P, Wolkenhauer O (2022) Identification and epidemiological characterization of type-2 diabetes sub-population using an unsupervised machine learning approach. Nutr Diabetes 12(1):27","journal-title":"Nutr Diabetes"},{"issue":"2","key":"909_CR27","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1038\/s41587-020-00809-z","volume":"39","author":"D Kobak","year":"2021","unstructured":"Kobak D, Linderman GC (2021) Initialization is critical for preserving global data structure in both t-SNE and UMAP. Nat Biotechnol 39(2):156\u2013157","journal-title":"Nat Biotechnol"},{"key":"909_CR28","doi-asserted-by":"crossref","unstructured":"Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) \u2018Learning Deep Features for Discriminative Localization\u2019, presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921-2929","DOI":"10.1109\/CVPR.2016.319"},{"issue":"2","key":"909_CR29","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","volume":"128","author":"RR Selvaraju","year":"2020","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2020) Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis 128(2):336\u2013359. https:\/\/doi.org\/10.1007\/s11263-019-01228-7","journal-title":"Int J Comput Vis"},{"key":"909_CR30","doi-asserted-by":"publisher","unstructured":"Pope P. E, Kolouri S, Rostami M, Martin C. E, Hoffmann H (2019) \u2018Explainability Methods for Graph Convolutional Neural Networks\u2019, in 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA: IEEE, Jun. pp. 10764-10773. https:\/\/doi.org\/10.1109\/CVPR.2019.01103.","DOI":"10.1109\/CVPR.2019.01103."},{"key":"909_CR31","doi-asserted-by":"crossref","unstructured":"Yuan Y, Wang W, Pang W (2021). Which hyperparameters to optimise? an investigation of evolutionary hyperparameter optimisation in graph neural network for molecular property prediction. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (pp. 1403-1404)","DOI":"10.1145\/3449726.3463192"},{"issue":"1","key":"909_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13321-020-00479-8","volume":"13","author":"D Jiang","year":"2021","unstructured":"Jiang D, Wu Z, Hsieh CY, Chen G, Liao B, Wang Z, Hou T (2021) Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models. J Cheminform 13(1):1\u201323","journal-title":"J Cheminform"},{"issue":"16","key":"909_CR33","doi-asserted-by":"publisher","first-page":"8749","DOI":"10.1021\/acs.jmedchem.9b00959","volume":"63","author":"ZP Xiong","year":"2020","unstructured":"Xiong ZP, Wang DY, Liu XH, Zhong FS, Wan XZ, Li XT, Li ZJ, Luo XM, Chen KX, Jiang HL, Zheng MY (2020) Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism. J Med Chem 63(16):8749\u20138760","journal-title":"J Med Chem"},{"issue":"24","key":"909_CR34","doi-asserted-by":"publisher","first-page":"3772","DOI":"10.1002\/cmdc.202100418","volume":"16","author":"C Grebner","year":"2021","unstructured":"Grebner C, Matter H, Kofink D, Wenzel J, Schmidt F, Hessler G (2021) Application of deep neural network models in drug discovery programs. ChemMedChem 16(24):3772\u20133786","journal-title":"ChemMedChem"},{"key":"909_CR35","doi-asserted-by":"crossref","unstructured":"Rao J, Zheng S, Yang Y (2021). Quantitative evaluation of explainable graph neural networks for molecular property prediction. Patterns, 3","DOI":"10.1016\/j.patter.2022.100628"},{"issue":"8","key":"909_CR36","doi-asserted-by":"publisher","first-page":"3789","DOI":"10.1021\/acs.jcim.1c00181","volume":"61","author":"J Chen","year":"2021","unstructured":"Chen J, Cheong HH, Siu SWI (2021) xDeep-AcPEP: deep learning method for anticancer peptide activity prediction based on convolutional neural network and multitask learning. J Chem Inform Model 61(8):3789\u20133803","journal-title":"J Chem Inform Model"},{"issue":"22","key":"909_CR37","doi-asserted-by":"publisher","first-page":"4240","DOI":"10.1021\/ja00881a009","volume":"84","author":"C Tanford","year":"1962","unstructured":"Tanford C (1962) Contribution of hydrophobic interactions to the stability of the globular conformation of proteins. J Am Chem Soc 84(22):4240\u20134247","journal-title":"J Am Chem Soc"},{"key":"909_CR38","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1007\/BF01879606","volume":"147","author":"KH Ney","year":"1971","unstructured":"Ney KH (1971) Prediction of bitterness of peptides from their amino acid composition. Zeitschrift f\u00fcr Lebensmittel-Untersuchung und-Forschung 147:64\u201368","journal-title":"Zeitschrift f\u00fcr Lebensmittel-Untersuchung und-Forschung"},{"key":"909_CR39","doi-asserted-by":"crossref","unstructured":"Di Pizio, A,\u00a0Levit A, Slutzki M, Behrens M, Karaman R, Niv MY\u00a0(2016) Comparing class A GPCRs to bitter taste receptors: structural motifs, ligand interactions and agonist-to-antagonist ratios. Method Cell Biol 132:401\u2013427","DOI":"10.1016\/bs.mcb.2015.10.005"},{"issue":"1","key":"909_CR40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00018-023-05025-x","volume":"81","author":"S Silvia","year":"2024","unstructured":"Schaefer S, Ziegler F, Lang T, Steuer A, Di Pizio A, Behrens M (2024) Membrane-bound chemoreception of bitter bile acids and peptides is mediated by the same subset of bitter taste receptors. Cell Mol Life Sci 81(1):1\u201313","journal-title":"Cell Mol Life Sci"},{"key":"909_CR41","doi-asserted-by":"publisher","first-page":"1189508","DOI":"10.3389\/fendo.2023.1189508","volume":"14","author":"A Di Pizio","year":"2023","unstructured":"Di Pizio A et al (2023) Peptide-binding GPCRs coming of age. Front Endocrinol 14:1189508","journal-title":"Front Endocrinol"},{"issue":"20","key":"909_CR42","doi-asserted-by":"publisher","first-page":"4623","DOI":"10.3390\/molecules25204623","volume":"25","author":"P Di Antonella","year":"2020","unstructured":"Di Pizio, A, Nicoli A (2020) In silico molecular study of tryptophan bitterness. Molecules 25(20):4623","journal-title":"Molecules"},{"issue":"20","key":"909_CR43","doi-asserted-by":"publisher","first-page":"4724","DOI":"10.3390\/molecules25204724","volume":"25","author":"A Kaiser","year":"2020","unstructured":"Kaiser A, Coin I (2020) Capturing peptide-GPCR interactions and their dynamics. Molecules 25(20):4724","journal-title":"Molecules"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-024-00909-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-024-00909-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-024-00909-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,8]],"date-time":"2024-10-08T13:06:13Z","timestamp":1728392773000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-024-00909-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,7]]},"references-count":43,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["909"],"URL":"https:\/\/doi.org\/10.1186\/s13321-024-00909-x","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,7]]},"assertion":[{"value":"31 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 September 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they do not have any competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"111"}}