{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T13:25:35Z","timestamp":1725542735790},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,1,14]],"date-time":"2023-01-14T00:00:00Z","timestamp":1673654400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,1,14]],"date-time":"2023-01-14T00:00:00Z","timestamp":1673654400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Science Center, Poland","award":["2018\/31\/B\/ST1\/00253","2018\/31\/B\/ST1\/00253"]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"Abstract<\/jats:title>Modern computer-assisted synthesis planning tools provide strong support for this problem. However, they are still limited by computational complexity. This limitation may be overcome by scoring the synthetic accessibility as a pre-retrosynthesis heuristic. A wide range of machine learning scoring approaches is available, however, their applicability and correctness were studied to a limited extent. Moreover, there is a lack of critical assessment of synthetic accessibility scores with common test conditions.In the present work, we assess if synthetic accessibility scores can reliably predict the outcomes of retrosynthesis planning. Using a specially prepared compounds database, we examine the outcomes of the retrosynthetic tool . We test whether synthetic accessibility scores: SAscore, SYBA, SCScore, and RAscore accurately predict the results of retrosynthesis planning. Furthermore, we investigate if synthetic accessibility scores can speed up retrosynthesis planning by better prioritizing explored partial synthetic routes and thus reducing the size of the search space. For that purpose, we analyze the partial solutions search trees, their structure, and complexity parameters, such as the number of nodes, or treewidth.We confirm that synthetic accessibility scores in most cases well discriminate feasible molecules from infeasible ones and can be potential boosters of retrosynthesis planning tools. Moreover, we show the current challenges of designing computer-assisted synthesis planning tools. We conclude that hybrid machine learning and human intuition-based synthetic accessibility scores can efficiently boost the effectiveness of computer-assisted retrosynthesis planning, however, they need to be carefully crafted for retrosynthesis planning algorithms.The source code of this work is publicly available at https:\/\/github.com\/grzsko\/ASAP<\/jats:ext-link>.<\/jats:p>","DOI":"10.1186\/s13321-023-00678-z","type":"journal-article","created":{"date-parts":[[2023,1,14]],"date-time":"2023-01-14T09:02:44Z","timestamp":1673686964000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Critical assessment of synthetic accessibility scores in computer-assisted synthesis planning"],"prefix":"10.1186","volume":"15","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-2010-4515","authenticated-orcid":false,"given":"Grzegorz","family":"Skoraczy\u0144ski","sequence":"first","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8160-3705","authenticated-orcid":false,"given":"Mateusz","family":"Kitlas","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3691-9372","authenticated-orcid":false,"given":"B\u0142a\u017cej","family":"Miasojedow","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-3476-3017","authenticated-orcid":false,"given":"Anna","family":"Gambin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,14]]},"reference":[{"issue":"1","key":"678_CR1","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1351\/pac196714010019","volume":"14","author":"EJ Corey","year":"1967","unstructured":"Corey EJ (1967) General methods for the construction of complex molecules. Pure Appl Chem 14(1):19\u201338","journal-title":"Pure Appl Chem"},{"issue":"2","key":"678_CR2","doi-asserted-by":"publisher","first-page":"440","DOI":"10.1021\/ja00757a022","volume":"94","author":"EJ Corey","year":"1972","unstructured":"Corey EJ, Cramer RD, Howe WJ (1972) Computer-assisted synthetic analysis for complex molecules. Methods and procedures for machine generation of synthetic intermediates. J Am Chem Soc 94(2):440\u2013459","journal-title":"J Am Chem Soc"},{"issue":"10","key":"678_CR3","doi-asserted-by":"publisher","first-page":"1887","DOI":"10.1351\/pac199062101887","volume":"62","author":"S Hanessian","year":"1990","unstructured":"Hanessian S, Franco J, Larouche B (1990) The psychobiological basis of heuristic synthesis planning - man, machine and the chiron approach. Pure Appl Chem 62(10):1887\u20131910","journal-title":"Pure Appl Chem"},{"issue":"23\u201324","key":"678_CR4","doi-asserted-by":"publisher","first-page":"2613","DOI":"10.1002\/anie.199526131","volume":"34","author":"W-D Ihlenfeldt","year":"1996","unstructured":"Ihlenfeldt W-D, Gasteiger J (1996) Computer-assisted planning of organic syntheses: the second generation of programs. Angew Chem Int Ed Engl 34(23\u201324):2613\u20132633","journal-title":"Angew Chem Int Ed Engl"},{"issue":"2","key":"678_CR5","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1002\/anie.199302011","volume":"32","author":"I Ugi","year":"1993","unstructured":"Ugi I, Bauer J, Bley K, Dengler A, Dietz A, Fontain E, Gruber B, Herges R, Knauer M, Reitsam K, Stein N (1993) Computer-assisted solution of chemical problems-the historical development and the present state of the art of a new discipline of chemistry. Angew Chem Int Ed Engl 32(2):201\u2013227","journal-title":"Angew Chem Int Ed Engl"},{"issue":"20","key":"678_CR6","doi-asserted-by":"publisher","first-page":"5904","DOI":"10.1002\/anie.201506101","volume":"55","author":"S Szymku\u0107","year":"2016","unstructured":"Szymku\u0107 S, Gajewska EP, Klucznik T, Molga K, Dittwald P, Startek M, Bajczyk M, Grzybowski BA (2016) Computer-assisted synthetic planning: the end of the beginning. Angew Chem Int Ed 55(20):5904\u20135937","journal-title":"Angew Chem Int Ed"},{"issue":"3","key":"678_CR7","doi-asserted-by":"publisher","first-page":"522","DOI":"10.1016\/j.chempr.2018.02.002","volume":"4","author":"T Klucznik","year":"2018","unstructured":"Klucznik T, Mikulak-Klucznik B, McCormack MP, Lima H, Szymku\u0107 S, Bhowmick M, Molga K, Zhou Y, Rickershauser L, Gajewska EP, Toutchkine A, Dittwald P, Startek MP, Kirkovits GJ, Roszak R, Adamski A, Sieredzi\u0144ska B, Mrksich M, Trice SLJ, Grzybowski BA (2018) Efficient syntheses of diverse, medicinally relevant targets planned by computer and executed in the laboratory. Chem 4(3):522\u2013532","journal-title":"Chem"},{"issue":"12","key":"678_CR8","doi-asserted-by":"publisher","first-page":"3316","DOI":"10.1039\/C9SC05704H","volume":"11","author":"P Schwaller","year":"2020","unstructured":"Schwaller P, Petraglia R, Zullo V, Nair VH, Haeuselmann RA, Pisoni R, Bekas C, Iuliano A, Laino T (2020) Predicting retrosynthetic pathways using transformer-based models and a hyper-graph exploration strategy. Chem Sci 11(12):3316\u20133325","journal-title":"Chem Sci"},{"key":"678_CR9","doi-asserted-by":"crossref","unstructured":"Watson IA, Wang J, Nicolaou CA (2019) A retrosynthetic analysis algorithm implementation. J Cheminformatics 11:1","DOI":"10.1186\/s13321-018-0323-6"},{"key":"678_CR10","doi-asserted-by":"crossref","unstructured":"Genheden S, Thakkar A, Chadimov\u00e1 V, Reymond J-L, Engkvist O, Bjerrum E (2020) AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. J Cheminformatics 12: 70","DOI":"10.1186\/s13321-020-00472-1"},{"issue":"1","key":"678_CR11","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1039\/C9SC04944D","volume":"11","author":"A Thakkar","year":"2019","unstructured":"Thakkar A, Kogej T, Reymond J-L, Engkvist O, Bjerrum EJ (2019) Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain. Chem Sci 11(1):154\u2013168","journal-title":"Chem Sci"},{"issue":"7698","key":"678_CR12","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1038\/nature25978","volume":"555","author":"MHS Segler","year":"2018","unstructured":"Segler MHS, Preuss M, Waller MP (2018) Planning chemical syntheses with deep neural networks and symbolic AI. Nature 555(7698):604\u2013610","journal-title":"Nature"},{"issue":"40","key":"678_CR13","doi-asserted-by":"publisher","first-page":"10959","DOI":"10.1039\/D0SC04184J","volume":"11","author":"X Wang","year":"2020","unstructured":"Wang X, Qian Y, Gao H, Coley CW, Mo Y, Barzilay R, Jensen KF (2020) Towards efficient discovery of green synthetic pathways with Monte Carlo tree search and reinforcement learning. Chem Sci 11(40):10959\u201310972","journal-title":"Chem Sci"},{"issue":"12","key":"678_CR14","doi-asserted-by":"publisher","first-page":"3355","DOI":"10.1039\/C9SC03666K","volume":"11","author":"K Lin","year":"2020","unstructured":"Lin K, Xu Y, Pei J, Lai L (2020) Automatic retrosynthetic route planning using template-free models. Chem Sci 11(12):3355\u20133364","journal-title":"Chem Sci"},{"issue":"11","key":"678_CR15","doi-asserted-by":"publisher","first-page":"3127","DOI":"10.1002\/cjoc.202100273","volume":"39","author":"Z Wang","year":"2021","unstructured":"Wang Z, Zhang W, Liu B (2021) Computational analysis of synthetic planning: past and future. Chin J Chem 39(11):3127\u20133143","journal-title":"Chin J Chem"},{"key":"678_CR16","unstructured":"Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. Adv Neural Inf Process Syst 27"},{"issue":"12","key":"678_CR17","doi-asserted-by":"publisher","first-page":"3599","DOI":"10.1021\/ja00402a071","volume":"103","author":"SH Bertz","year":"1981","unstructured":"Bertz SH (1981) The first general index of molecular complexity. J Am Chem Soc 103(12):3599\u20133601","journal-title":"J Am Chem Soc"},{"issue":"5","key":"678_CR18","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1016\/S0092-8240(83)80030-5","volume":"45","author":"SH Bertz","year":"1983","unstructured":"Bertz SH (1983) On the complexity of graphs and molecules. Bull Math Biol 45(5):849\u2013855","journal-title":"Bull Math Biol"},{"issue":"2","key":"678_CR19","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1021\/ci000145p","volume":"41","author":"R Barone","year":"2001","unstructured":"Barone R, Chanon M (2001) A new and simple approach to chemical complexity. Application to the synthesis of natural products. J Chem Inf Comput Sci 41(2):269\u2013272","journal-title":"J Chem Inf Comput Sci"},{"issue":"6","key":"678_CR20","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1007\/s10822-006-9099-2","volume":"21","author":"K Boda","year":"2007","unstructured":"Boda K, Seidel T, Gasteiger J (2007) Structure and reaction based evaluation of synthetic accessibility. J Comput Aided Mol Des 21(6):311\u2013325","journal-title":"J Comput Aided Mol Des"},{"key":"678_CR21","doi-asserted-by":"crossref","unstructured":"Ertl P, Schuffenhauer A (2009) Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminformatics 1:8","DOI":"10.1186\/1758-2946-1-8"},{"key":"678_CR22","doi-asserted-by":"crossref","unstructured":"Vor\u0161il\u00e1k M, Kol\u00e1\u0159 M, \u010cmelo I, Svozil D (2020) SYBA: Bayesian estimation of synthetic accessibility of organic compounds. J Cheminformatics 12:35","DOI":"10.1186\/s13321-020-00439-2"},{"issue":"12","key":"678_CR23","doi-asserted-by":"publisher","first-page":"2973","DOI":"10.1021\/acs.jcim.2c00038","volume":"62","author":"J Yu","year":"2022","unstructured":"Yu J, Wang J, Zhao H, Gao J, Kang Y, Cao D, Wang Z, Hou T (2022) Organic compound synthetic accessibility prediction based on the graph attention mechanism. J Chem Inf Model 62(12):2973\u20132986","journal-title":"J Chem Inf Model"},{"issue":"2","key":"678_CR24","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1021\/acs.jcim.7b00622","volume":"58","author":"CW Coley","year":"2018","unstructured":"Coley CW, Rogers L, Green WH, Jensen KF (2018) SCScore: synthetic complexity learned from a reaction corpus. J Chem Inf Model 58(2):252\u2013261","journal-title":"J Chem Inf Model"},{"issue":"9","key":"678_CR25","doi-asserted-by":"publisher","first-page":"3339","DOI":"10.1039\/D0SC05401A","volume":"12","author":"A Thakkar","year":"2021","unstructured":"Thakkar A, Chadimov\u00e1 V, Bjerrum EJ, Engkvist O, Reymond J-L (2021) Retrosynthetic accessibility score (RAscore)\u2014rapid machine learned synthesizability classification from AI driven retrosynthetic planning. Chem Sci 12(9):3339\u20133349","journal-title":"Chem Sci"},{"issue":"3","key":"678_CR26","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.3390\/molecules27031039","volume":"27","author":"B Li","year":"2022","unstructured":"Li B, Chen H (2022) Prediction of compound synthesis accessibility based on reaction knowledge graph. Molecules 27(3):1039","journal-title":"Molecules"},{"issue":"10","key":"678_CR27","doi-asserted-by":"publisher","first-page":"2293","DOI":"10.1021\/acs.jcim.1c01476","volume":"62","author":"C-H Liu","year":"2022","unstructured":"Liu C-H, Korablyov M, Jastrzebski S, W\u0142odarczyk-Pruszy\u0144ski P, Bengio Y, Segler M (2022) Retrognn: fast estimation of synthesizability for virtual screening and de novo design by learning from slow retrosynthesis software. J Chem Inf Model 62(10):2293\u20132300","journal-title":"J Chem Inf Model"},{"issue":"4","key":"678_CR28","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1039\/D2DD00015F","volume":"1","author":"S Genheden","year":"2022","unstructured":"Genheden S, Bjerrum E (2022) PaRoutes: towards a framework for benchmarking retrosynthesis route predictions. Digit Discov 1(4):527\u2013539","journal-title":"Digit Discov"},{"key":"678_CR29","doi-asserted-by":"publisher","first-page":"679","DOI":"10.1016\/j.ejmech.2012.06.024","volume":"54","author":"P Bonnet","year":"2012","unstructured":"Bonnet P (2012) Is chemical synthetic accessibility computationally predictable for drug and lead-like molecules? A comparative assessment between medicinal and computational chemists. Eur J Med Chem 54:679\u2013689","journal-title":"Eur J Med Chem"},{"key":"678_CR30","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1016\/j.jmgm.2018.01.011","volume":"80","author":"Y Baba","year":"2018","unstructured":"Baba Y, Isomura T, Kashima H (2018) Wisdom of crowds for synthetic accessibility evaluation. J Mol Graph Model 80:217\u2013223","journal-title":"J Mol Graph Model"},{"issue":"5","key":"678_CR31","doi-asserted-by":"publisher","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"},{"issue":"3","key":"678_CR32","doi-asserted-by":"publisher","first-page":"283","DOI":"10.1007\/s11030-006-9041-5","volume":"10","author":"M Hassan","year":"2006","unstructured":"Hassan M, Brown RD, Varma-O\u2019Brien S, Rogers D (2006) Cheminformatics analysis and learning in a data pipelining environment. Mol Divers 10(3):283\u2013299","journal-title":"Mol Divers"},{"issue":"D1","key":"678_CR33","doi-asserted-by":"publisher","first-page":"1388","DOI":"10.1093\/nar\/gkaa971","volume":"49","author":"S Kim","year":"2021","unstructured":"Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, Zaslavsky L, Zhang J, Bolton EE (2021) PubChem in 2021: new data content and improved web interfaces. Nucleic Acids Res 49(D1):1388\u20131395","journal-title":"Nucleic Acids Res"},{"key":"678_CR34","unstructured":"RDKit: Open-source cheminformatics. https:\/\/rdkit.org\/"},{"issue":"1","key":"678_CR35","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s13321-017-0206-2","volume":"9","author":"M Vor\u0161il\u00e1k","year":"2017","unstructured":"Vor\u0161il\u00e1k M, Svozil D (2017) Nonpher: computational method for design of hard-to-synthesize structures. J Cheminformatics 9:20","journal-title":"J Cheminformatics"},{"issue":"4","key":"678_CR36","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF02478259","volume":"5","author":"WS McCulloch","year":"1943","unstructured":"McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5(4):115\u2013133","journal-title":"Bull Math Biophys"},{"key":"678_CR37","doi-asserted-by":"crossref","unstructured":"Lawson A.J, Swienty-Busch J, G\u00e9oui T, Evans D (2014) Chap. 8. The making of Reaxys\u2014towards unobstructed access to relevant chemistry information. In: the future of the history of chemical information. ACS symposium series, vol 1164, pp 127\u2013148. American Chemical Society, Washington, DC","DOI":"10.1021\/bk-2014-1164.ch008"},{"issue":"2","key":"678_CR38","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1021\/c160017a018","volume":"5","author":"HL Morgan","year":"1965","unstructured":"Morgan HL (1965) The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service. J Chem Doc 5(2):107\u2013113","journal-title":"J Chem Doc"},{"issue":"D1","key":"678_CR39","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1093\/nar\/gkw1074","volume":"45","author":"A Gaulton","year":"2017","unstructured":"Gaulton A, Hersey A, Nowotka M, Bento AP, Chambers J, Mendez D, Mutowo P, Atkinson F, Bellis LJ, Cibri\u00e1n-Uhalte E, Davies M, Dedman N, Karlsson A, Magari\u00f1os MP, Overington JP, Papadatos G, Smit I, Leach AR (2017) The ChEMBL database in 2017. Nucleic Acids Res 45(D1):945\u2013954","journal-title":"Nucleic Acids Res"},{"issue":"5","key":"678_CR40","doi-asserted-by":"publisher","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":"678_CR41","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1007\/11871842_29","volume-title":"Machine learning: ECML 2006. Lecture notes in computer Science","author":"L Kocsis","year":"2006","unstructured":"Kocsis L, Szepesv\u00e1ri C (2006) Bandit based Monte-Carlo planning. In: F\u00fcrnkranz J, Scheffer T, Spiliopoulou M (eds) Machine learning: ECML 2006. Lecture notes in computer Science. Springer, Berlin, Heidelberg, pp 282\u2013293"},{"key":"678_CR42","doi-asserted-by":"crossref","unstructured":"Coulom R (2007) Efficient selectivity and backup operators in Monte-Carlo tree search. In: van den Herik HJ, Ciancarini P, Donkers HHLMJ, eds. Computers and games. Lecture notes in computer science, pp 72\u201383. Springer, Berlin, Heidelberg","DOI":"10.1007\/978-3-540-75538-8_7"},{"issue":"03","key":"678_CR43","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1142\/S1793005708001094","volume":"04","author":"GMJ-B Chaslot","year":"2008","unstructured":"Chaslot GMJ-B, Winands MHM, Herik HJVD, Uiterwijk JWHM, Bouzy B (2008) Progressive strategies for Monte-Carlo tree search. New Math Natural Comput 04(03):343\u2013357","journal-title":"New Math Natural Comput"},{"issue":"1","key":"678_CR44","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00057a005","volume":"28","author":"D Weininger","year":"1988","unstructured":"Weininger D (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J Chem Inf Model 28(1):31\u201336","journal-title":"J Chem Inf Model"},{"issue":"1","key":"678_CR45","doi-asserted-by":"publisher","first-page":"3582","DOI":"10.1038\/s41598-017-02303-0","volume":"7","author":"G Skoraczy\u0144ski","year":"2017","unstructured":"Skoraczy\u0144ski G, Dittwald P, Miasojedow B, Szymku\u0107 S, Gajewska EP, Grzybowski BA, Gambin A (2017) Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient? Sci Rep 7(1):3582","journal-title":"Sci Rep"},{"key":"678_CR46","volume":"1","author":"JL Medina-Franco","year":"2021","unstructured":"Medina-Franco JL (2021) Grand challenges of computer-aided drug design: the road ahead. Front Drug Discov 1:728551","journal-title":"Front Drug Discov"},{"key":"678_CR47","doi-asserted-by":"crossref","unstructured":"Jiang D, Wu Z, Hsieh C-Y, Chen G, Liao B, Wang Z, Shen C, Cao D, Wu J, 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 Cheminformatics 13:12","DOI":"10.1186\/s13321-020-00479-8"},{"issue":"1","key":"678_CR48","doi-asserted-by":"publisher","first-page":"72","DOI":"10.2307\/1412159","volume":"15","author":"C Spearman","year":"1904","unstructured":"Spearman C (1904) The proof and measurement of association between two things. Am J Psychol 15(1):72\u2013101","journal-title":"Am J Psychol"},{"key":"678_CR49","doi-asserted-by":"crossref","unstructured":"Student (1908) The probable error of a mean. Biometrika 6(1): 1\u201325","DOI":"10.2307\/2331554"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00678-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-023-00678-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00678-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,14]],"date-time":"2023-01-14T09:04:55Z","timestamp":1673687095000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-023-00678-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,14]]},"references-count":49,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["678"],"URL":"https:\/\/doi.org\/10.1186\/s13321-023-00678-z","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,14]]},"assertion":[{"value":"6 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 January 2023","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 have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"6"}}