{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:25:35Z","timestamp":1790702735667,"version":"4.1.0"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T00:00:00Z","timestamp":1638316800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,12,11]],"date-time":"2021-12-11T00:00:00Z","timestamp":1639180800000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Macau Science and Technology Development Fund","award":["0029\/2018\/A1"],"award-info":[{"award-number":["0029\/2018\/A1"]}]},{"name":"University of Macau Research Grant","award":["MYRG2019\u201000041\u2010ICMS"],"award-info":[{"award-number":["MYRG2019\u201000041\u2010ICMS"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Rapid solvent selection is of great significance in chemistry. However, solubility prediction remains a crucial challenge. This study aimed to develop machine learning models that can accurately predict compound solubility in organic solvents. A dataset containing 5081 experimental temperature and solubility data of compounds in organic solvents was extracted and standardized. Molecular fingerprints were selected to characterize structural features. lightGBM was compared with deep learning and traditional machine learning (PLS, Ridge regression, kNN, DT, ET, RF, SVM) to develop models for predicting solubility in organic solvents at different temperatures. Compared to other models, lightGBM exhibited significantly better overall generalization (logS\u2009 \u00b1\u20090.20). For unseen solutes, our model gave a prediction accuracy (logS\u2009 \u00b1\u20090.59) close to the expected noise level of experimental solubility data. lightGBM revealed the physicochemical relationship between solubility and structural features. Our method enables rapid solvent screening in chemistry and may be applied to solubility prediction in other solvents.<\/jats:p>","DOI":"10.1186\/s13321-021-00575-3","type":"journal-article","created":{"date-parts":[[2021,12,11]],"date-time":"2021-12-11T10:02:34Z","timestamp":1639216954000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["Prediction of small-molecule compound solubility in organic solvents by machine learning algorithms"],"prefix":"10.1186","volume":"13","author":[{"given":"Zhuyifan","family":"Ye","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8052-4773","authenticated-orcid":false,"given":"Defang","family":"Ouyang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,12,11]]},"reference":[{"issue":"7","key":"575_CR1","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1016\/j.drudis.2020.04.016","volume":"25","author":"T Das","year":"2020","unstructured":"Das T, Mehta CH, Nayak UY (2020) Multiple approaches for achieving drug solubility: an in silico perspective. Drug Discovery Today 25(7):1206\u20131212","journal-title":"Drug Discovery Today"},{"issue":"1\u20132","key":"575_CR2","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.ijpharm.2018.01.044","volume":"540","author":"CAS Bergstr\u00f6m","year":"2018","unstructured":"Bergstr\u00f6m CAS, Larsson P (2018) Computational prediction of drug solubility in water-based systems: qualitative and quantitative approaches used in the current drug discovery and development setting. Int J Pharm 540(1\u20132):185\u2013193","journal-title":"Int J Pharm"},{"issue":"4","key":"575_CR3","doi-asserted-by":"crossref","first-page":"1395","DOI":"10.1021\/ci700096r","volume":"47","author":"J Wang","year":"2007","unstructured":"Wang J et al (2007) Development of reliable aqueous solubility models and their application in druglike analysis. J Chem Inf Model 47(4):1395\u20131404","journal-title":"J Chem Inf Model"},{"issue":"3","key":"575_CR4","doi-asserted-by":"crossref","first-page":"571","DOI":"10.1021\/ci800406y","volume":"49","author":"J Wang","year":"2009","unstructured":"Wang J, Hou T, Xu X (2009) Aqueous solubility prediction based on weighted atom type counts and solvent accessible surface areas. J Chem Inf Model 49(3):571\u2013581","journal-title":"J Chem Inf Model"},{"issue":"7","key":"575_CR5","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1021\/ci400187y","volume":"53","author":"A Lusci","year":"2013","unstructured":"Lusci A, Pollastri G, Baldi P (2013) Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J Chem Inf Model 53(7):1563\u20131575","journal-title":"J Chem Inf Model"},{"issue":"8","key":"575_CR6","doi-asserted-by":"crossref","first-page":"2962","DOI":"10.1021\/mp500103r","volume":"11","author":"DS Palmer","year":"2014","unstructured":"Palmer DS, Mitchell JBO (2014) Is experimental data quality the limiting factor in predicting the aqueous solubility of druglike molecules? Mol Pharm 11(8):2962\u20132972","journal-title":"Mol Pharm"},{"issue":"3","key":"575_CR7","doi-asserted-by":"crossref","first-page":"1500","DOI":"10.1002\/jps.21922","volume":"99","author":"MH Abraham","year":"2010","unstructured":"Abraham MH et al (2010) Prediction of solubility of drugs and other compounds in organic solvents. J Pharm Sci 99(3):1500\u20131515","journal-title":"J Pharm Sci"},{"issue":"1","key":"575_CR8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0378-5173(96)04828-4","volume":"148","author":"BC Hancock","year":"1997","unstructured":"Hancock BC, York P, Rowe RC (1997) The use of solubility parameters in pharmaceutical dosage form design. Int J Pharm 148(1):1\u201321","journal-title":"Int J Pharm"},{"issue":"8","key":"575_CR9","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1063\/1.1750971","volume":"9","author":"PJ Flory","year":"1941","unstructured":"Flory PJ (1941) Thermodynamics of high polymer solutions. J Chem Phys 9(8):660\u2013661","journal-title":"J Chem Phys"},{"issue":"5","key":"575_CR10","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1063\/1.1750930","volume":"9","author":"ML Huggins","year":"1941","unstructured":"Huggins ML (1941) Solutions of long chain compounds. J Chem Phys 9(5):440","journal-title":"J Chem Phys"},{"issue":"2","key":"575_CR11","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.ajps.2013.12.004","volume":"9","author":"R Fule","year":"2014","unstructured":"Fule R, Amin P (2014) Development and evaluation of lafutidine solid dispersion via hot melt extrusion: investigating drug-polymer miscibility with advanced characterisation. Asian J Pharm Sci 9(2):92\u2013106","journal-title":"Asian J Pharm Sci"},{"key":"575_CR12","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.119.017801","author":"W Zhang","year":"2017","unstructured":"Zhang W, Gomez ED, Milner ST (2017) Predicting Flory-Huggins \u03c7 from simulations. Phys Rev Lett. https:\/\/doi.org\/10.1103\/PhysRevLett.119.017801","journal-title":"Phys Rev Lett"},{"issue":"9","key":"575_CR13","doi-asserted-by":"crossref","first-page":"3322","DOI":"10.1021\/ct300345m","volume":"8","author":"DS Palmer","year":"2012","unstructured":"Palmer DS et al (2012) First-principles calculation of the intrinsic aqueous solubility of crystalline druglike molecules. J Chem Theory Comput 8(9):3322\u20133337","journal-title":"J Chem Theory Comput"},{"issue":"3","key":"575_CR14","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1021\/ci4005805","volume":"54","author":"JL McDonagh","year":"2014","unstructured":"McDonagh JL et al (2014) Uniting cheminformatics and chemical theory to predict the intrinsic aqueous solubility of crystalline druglike molecules. J Chem Inf Model 54(3):844\u2013856","journal-title":"J Chem Inf Model"},{"issue":"6","key":"575_CR15","doi-asserted-by":"crossref","first-page":"1110","DOI":"10.1021\/op060074g","volume":"10","author":"H Hojjati","year":"2006","unstructured":"Hojjati H, Rohani S (2006) Measurement and prediction of solubility of paracetamol in water-isopropanol solution. Part 2. Prediction. Org Process Res Dev 10(6):1110\u20131118","journal-title":"Org Process Res Dev"},{"issue":"1","key":"575_CR16","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1002\/aic.690140124","volume":"14","author":"H Renon","year":"1968","unstructured":"Renon H, Prausnitz JM (1968) Local compositions in thermodynamic excess functions for liquid mixtures. AIChE J 14(1):135\u2013144","journal-title":"AIChE J"},{"issue":"1","key":"575_CR17","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1002\/aic.690210115","volume":"21","author":"DS Abrams","year":"1975","unstructured":"Abrams DS, Prausnitz JM (1975) Statistical thermodynamics of liquid mixtures: a new expression for the excess Gibbs energy of partly or completely miscible systems. AIChE J 21(1):116\u2013128","journal-title":"AIChE J"},{"issue":"6","key":"575_CR18","doi-asserted-by":"crossref","first-page":"1086","DOI":"10.1002\/aic.690210607","volume":"21","author":"A Fredenslund","year":"1975","unstructured":"Fredenslund A, Jones RL, Prausnitz JM (1975) Group-contribution estimation of activity coefficients in nonideal liquid mixtures. AIChE J 21(6):1086\u20131099","journal-title":"AIChE J"},{"key":"575_CR19","first-page":"799","volume":"5","author":"A Klamt","year":"1993","unstructured":"Klamt A, Sch\u00fc\u00fcrmann G (1993) COSMO: a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient. J Chem Soc 5:799\u2013805","journal-title":"J Chem Soc"},{"issue":"7","key":"575_CR20","doi-asserted-by":"crossref","first-page":"2224","DOI":"10.1021\/j100007a062","volume":"99","author":"A Klamt","year":"1995","unstructured":"Klamt A (1995) Conductor-like screening model for real solvents: a new approach to the quantitative calculation of solvation phenomena. J Phys Chem 99(7):2224\u20132235","journal-title":"J Phys Chem"},{"issue":"2","key":"575_CR21","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1002\/jcc.1168","volume":"23","author":"A Klamt","year":"2002","unstructured":"Klamt A et al (2002) Prediction of aqueous solubility of drugs and pesticides with COSMO-RS. J Comput Chem 23(2):275\u2013281","journal-title":"J Comput Chem"},{"issue":"17","key":"575_CR22","doi-asserted-by":"crossref","first-page":"5107","DOI":"10.1021\/acs.iecr.6b05035","volume":"56","author":"R Hern\u00e1ndez-Bravo","year":"2017","unstructured":"Hern\u00e1ndez-Bravo R et al (2017) Calculation of the solubility parameter by COSMO-RS methods and its influence on asphaltene-ionic liquid interactions. Ind Eng Chem Res 56(17):5107\u20135115","journal-title":"Ind Eng Chem Res"},{"issue":"4","key":"575_CR23","doi-asserted-by":"crossref","first-page":"1664","DOI":"10.1021\/acs.molpharmaceut.8b00056","volume":"15","author":"Q Zhao","year":"2018","unstructured":"Zhao Q et al (2018) Computer-aided formulation design for a highly soluble lutein-cyclodextrin multiple-component delivery system. Mol Pharm 15(4):1664\u20131673","journal-title":"Mol Pharm"},{"issue":"8","key":"575_CR24","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1002\/jps.2600690814","volume":"69","author":"SH Yalkowsky","year":"1980","unstructured":"Yalkowsky SH, Valvani SC (1980) Solubility and partitioning I: solubility of nonelectrolytes in water. J Pharm Sci 69(8):912\u2013922","journal-title":"J Pharm Sci"},{"issue":"2","key":"575_CR25","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1002\/1520-6017(200102)90:2<234::AID-JPS14>3.0.CO;2-V","volume":"90","author":"N Jain","year":"2001","unstructured":"Jain N, Yalkowsky SH (2001) Estimation of the aqueous solubility I: application to organic nonelectrolytes. J Pharm Sci 90(2):234\u2013252","journal-title":"J Pharm Sci"},{"issue":"20","key":"575_CR26","doi-asserted-by":"crossref","first-page":"5114","DOI":"10.1021\/ie011014w","volume":"41","author":"S Gracin","year":"2002","unstructured":"Gracin S, Brinck T, Rasmuson \u00c5C (2002) Prediction of solubility of solid organic compounds in solvents by UNIFAC. Ind Eng Chem Res 41(20):5114\u20135124","journal-title":"Ind Eng Chem Res"},{"key":"575_CR27","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1039\/9781847557681-00188","volume-title":"Developments and applications in solubility","author":"F Eckert","year":"2007","unstructured":"Eckert F (2007) Chapter 12 prediction of solubility with COSMO-RS. Developments and applications in solubility. The Royal Society of Chemistry, London, pp 188\u2013200"},{"issue":"1","key":"575_CR28","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.fluid.2011.06.032","volume":"309","author":"B Bouillot","year":"2011","unstructured":"Bouillot B, Teychen\u00e9 S, Biscans B (2011) An evaluation of thermodynamic models for the prediction of drug and drug-like molecule solubility in organic solvents. Fluid Phase Equilib 309(1):36\u201352","journal-title":"Fluid Phase Equilib"},{"key":"575_CR29","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.fluid.2015.07.032","volume":"406","author":"H Matsuda","year":"2015","unstructured":"Matsuda H et al (2015) Determination and prediction of solubilities of active pharmaceutical ingredients in selected organic solvents. Fluid Phase Equilib 406:116\u2013123","journal-title":"Fluid Phase Equilib"},{"issue":"1","key":"575_CR30","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1007\/s13738-018-1483-z","volume":"16","author":"A Yousefi","year":"2019","unstructured":"Yousefi A, Movagharnejad K (2019) Prediction of the pharmaceutical solubility in water and organic solvents via different soft computing models. Iranian J Chem Eng 16(1):83\u2013100","journal-title":"Iranian J Chem Eng"},{"key":"575_CR31","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-19594-z","author":"S Boobier","year":"2020","unstructured":"Boobier S et al (2020) Machine learning with physicochemical relationships: solubility prediction in organic solvents and water. Nat Commun. https:\/\/doi.org\/10.1038\/s41467-020-19594-z","journal-title":"Nat Commun"},{"issue":"6245","key":"575_CR32","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"MI Jordan","year":"2015","unstructured":"Jordan MI, Mitchell TM (2015) Machine learning: trends, perspectives, and prospects. Science 349(6245):255\u2013260","journal-title":"Science"},{"issue":"7553","key":"575_CR33","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"issue":"1","key":"575_CR34","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.apsb.2018.09.010","volume":"9","author":"Y Yang","year":"2019","unstructured":"Yang Y et al (2019) Deep learning for in vitro prediction of pharmaceutical formulations. Acta Pharm Sin B 9(1):177\u2013185","journal-title":"Acta Pharm Sin B"},{"issue":"2","key":"575_CR35","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1021\/acs.molpharmaceut.8b00816","volume":"16","author":"Z Ye","year":"2019","unstructured":"Ye Z et al (2019) An integrated transfer learning and multitask learning approach for pharmacokinetic parameter prediction. Mol Pharm 16(2):533\u2013541","journal-title":"Mol Pharm"},{"issue":"7","key":"575_CR36","doi-asserted-by":"crossref","first-page":"8023","DOI":"10.1021\/acsnano.9b02774","volume":"13","author":"J Lazarovits","year":"2019","unstructured":"Lazarovits J et al (2019) Supervised learning and mass spectrometry predicts the in vivo fate of nanomaterials. ACS Nano 13(7):8023\u20138034","journal-title":"ACS Nano"},{"key":"575_CR37","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.jconrel.2020.03.043","volume":"322","author":"Y He","year":"2020","unstructured":"He Y et al (2020) Can machine learning predict drug nanocrystals? J Control Release 322:274\u2013285","journal-title":"J Control Release"},{"key":"575_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.cplett.2020.137354","author":"H Gao","year":"2020","unstructured":"Gao H et al (2020) Predicting drug\/phospholipid complexation by the lightGBM method. Chem Phys Lett. https:\/\/doi.org\/10.1016\/j.cplett.2020.137354","journal-title":"Chem Phys Lett"},{"issue":"4","key":"575_CR39","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.ajps.2018.01.003","volume":"13","author":"R Han","year":"2018","unstructured":"Han R et al (2018) Predicting oral disintegrating tablet formulations by neural network techniques. Asian J Pharm Sci 13(4):336\u2013342","journal-title":"Asian J Pharm Sci"},{"issue":"5","key":"575_CR40","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","volume":"29","author":"JH Friedman","year":"2001","unstructured":"Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29(5):1189\u20131232","journal-title":"Ann Stat"},{"key":"575_CR41","volume-title":"lightGBM: a highly efficient gradient boosting decision tree","author":"G Ke","year":"2017","unstructured":"Ke G et al (2017) lightGBM: a highly efficient gradient boosting decision tree. California, Neural Information Processing Systems Foundation"},{"issue":"6","key":"575_CR42","doi-asserted-by":"crossref","first-page":"1241","DOI":"10.1016\/j.apsb.2019.04.004","volume":"9","author":"Q Zhao","year":"2019","unstructured":"Zhao Q et al (2019) Predicting complexation performance between cyclodextrins and guest molecules by integrated machine learning and molecular modeling techniques. Acta Pharm Sin B 9(6):1241\u20131252","journal-title":"Acta Pharm Sin B"},{"issue":"5","key":"575_CR43","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"D Rogers","year":"2010","unstructured":"Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50(5):742\u2013754","journal-title":"J Chem Inf Model"},{"key":"575_CR44","volume-title":"RDKit: open-source cheminformatics","author":"G Landrum","year":"2006","unstructured":"Landrum G (2006) RDKit: open-source cheminformatics. Bethesda, NCBI"},{"issue":"D1","key":"575_CR45","doi-asserted-by":"crossref","first-page":"D1202","DOI":"10.1093\/nar\/gkv951","volume":"44","author":"S Kim","year":"2016","unstructured":"Kim S et al (2016) PubChem substance and compound databases. Nucleic Acids Res 44(D1):D1202\u2013D1213","journal-title":"Nucleic Acids Res"},{"key":"575_CR46","unstructured":"Abadi M, Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, Ghemawat S, Irving G, Isard M, Kudlur M, Levenberg J, Monga R, Moore S, Murray DG, Steiner B, Tucker P, Vasudevan V, Warden P, Wicke M, Yu Y, Zheng X (2016) TensorFlow: A system for large-scale machine learning. In: Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, OSDI 2016. USENIX Association, pp 265\u2013283. ISBN; 9781931971331"},{"key":"575_CR47","volume-title":"Theano: a CPU and GPU math compiler in Python","author":"J Bergstra","year":"2010","unstructured":"Bergstra J et al (2010) Theano: a CPU and GPU math compiler in Python. Proceedings of the 9th Python in Science Conference, Texas"},{"key":"575_CR48","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F et al (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"issue":"19","key":"575_CR49","doi-asserted-by":"crossref","first-page":"4350","DOI":"10.1021\/jm020155c","volume":"45","author":"YC Martin","year":"2002","unstructured":"Martin YC, Kofron JL, Traphagen LM (2002) Do structurally similar molecules have similar biological activity? J Med Chem 45(19):4350\u20134358","journal-title":"J Med Chem"},{"issue":"1","key":"575_CR50","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1177\/1087057103260006","volume":"9","author":"KV Balakin","year":"2004","unstructured":"Balakin KV et al (2004) In silico estimation of DMSO solubility of organic compounds for bioscreening. J Biomol Screen 9(1):22\u201331","journal-title":"J Biomol Screen"},{"key":"575_CR51","volume-title":"General chemistry: an integrated approach","author":"JW Hill","year":"2002","unstructured":"Hill JW, Petrucci RH (2002) General chemistry: an integrated approach. Prentice Hall, Hoboken"},{"issue":"4","key":"575_CR52","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1021\/je0500781","volume":"50","author":"H Sun","year":"2005","unstructured":"Sun H, Gong JB, Wang JK (2005) Solubility of Lovastatin in acetone, methanol, ethanol, ethyl acetate, and butyl acetate between 283 K and 323 K. J Chem Eng Data 50(4):1389\u20131391","journal-title":"J Chem Eng Data"},{"issue":"1","key":"575_CR53","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/0045-6535(83)90185-6","volume":"12","author":"AJ Dobbs","year":"1983","unstructured":"Dobbs AJ, Williams N (1983) Fat solubility\u2014a property of environmental relevance? Chemosphere 12(1):97\u2013104","journal-title":"Chemosphere"},{"issue":"56","key":"575_CR54","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1080\/10629360701430124","volume":"18","author":"OA Raevsky","year":"2007","unstructured":"Raevsky OA, Perlovich GL, Schaper KJ (2007) Physicochemical properties\/descriptors governing the solubility and partitioning of chemicals in water-solvent-gas systems. Part 2. Solubility in 1-octanol. SAR QSAR Environ Res 18(56):543\u2013578","journal-title":"SAR QSAR Environ Res"},{"key":"575_CR55","volume-title":"Compendium of chemical terminology","author":"AD McNaught","year":"1997","unstructured":"McNaught AD, Wilkinson A (1997) Compendium of chemical terminology, vol 1669. Blackwell Science, Oxford"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-021-00575-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-021-00575-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-021-00575-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,11]],"date-time":"2021-12-11T22:07:46Z","timestamp":1639260466000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-021-00575-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12]]},"references-count":55,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["575"],"URL":"https:\/\/doi.org\/10.1186\/s13321-021-00575-3","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12]]},"assertion":[{"value":"22 June 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 December 2021","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 there is no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"98"}}