{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T12:44:56Z","timestamp":1787316296052,"version":"build-2736575974"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,4,22]],"date-time":"2020-04-22T00:00:00Z","timestamp":1587513600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,4,22]],"date-time":"2020-04-22T00:00:00Z","timestamp":1587513600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001823","name":"Ministerstvo \u0160kolstv\u00ed, Ml\u00e1de\u017ee a T\u011blov\u00fdchovy","doi-asserted-by":"publisher","award":["LTARF18013"],"award-info":[{"award-number":["LTARF18013"]}],"id":[{"id":"10.13039\/501100001823","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on the deep learning models and conventional atom-based approaches may result in invalid structures and fail to address their synthetic feasibility issues. On the other hand, conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide both better novelty and diversity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here we developed a new framework of fragment-based structure generation that, by design, results in the chemically valid structures and provides flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The framework was implemented as an open-source Python module and can be used to create custom workflows for the exploration of chemical space.<\/jats:p>","DOI":"10.1186\/s13321-020-00431-w","type":"journal-article","created":{"date-parts":[[2020,4,22]],"date-time":"2020-04-22T14:03:32Z","timestamp":1587564212000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":84,"title":["CReM: chemically reasonable mutations framework for structure generation"],"prefix":"10.1186","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5088-8149","authenticated-orcid":false,"given":"Pavel","family":"Polishchuk","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,4,22]]},"reference":[{"key":"431_CR1","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1007\/s10822-013-9672-4","volume":"27","author":"PG Polishchuk","year":"2013","unstructured":"Polishchuk PG, Madzhidov TI, Varnek A (2013) Estimation of the size of drug-like chemical space based on GDB-17 data. J Comput Aided Mol Des. 27:675\u2013679. https:\/\/doi.org\/10.1007\/s10822-013-9672-4","journal-title":"J Comput Aided Mol Des."},{"key":"431_CR2","doi-asserted-by":"publisher","first-page":"4077","DOI":"10.1021\/acs.jmedchem.5b01849","volume":"59","author":"P Schneider","year":"2016","unstructured":"Schneider P, Schneider G (2016) De novo design at the edge of chaos. J Med Chem 59:4077\u20134086. https:\/\/doi.org\/10.1021\/acs.jmedchem.5b01849","journal-title":"J Med Chem"},{"key":"431_CR3","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1038\/nrd.2017.232","volume":"17","author":"G Schneider","year":"2017","unstructured":"Schneider G (2017) Automating drug discovery. Nat Rev Drug Discovery 17:97. https:\/\/doi.org\/10.1038\/nrd.2017.232","journal-title":"Nat Rev Drug Discovery"},{"key":"431_CR4","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/bf00124387","volume":"6","author":"H-J B\u00f6hm","year":"1992","unstructured":"B\u00f6hm H-J (1992) The computer program LUDI: a new method for the de novo design of enzyme inhibitors. J Comput Aided Mol Des. 6:61\u201378. https:\/\/doi.org\/10.1007\/bf00124387","journal-title":"J Comput Aided Mol Des."},{"key":"431_CR5","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1007\/s0089400060498","volume":"6","author":"R Wang","year":"2000","unstructured":"Wang R, Gao Y, Lai L (2000) LigBuilder: a multi-purpose program for structure-based drug design. Mol Model Annu 6:498\u2013516. https:\/\/doi.org\/10.1007\/s0089400060498","journal-title":"Mol Model Annu"},{"key":"431_CR6","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1021\/ci034290p","volume":"44","author":"N Brown","year":"2004","unstructured":"Brown N, McKay B, Gilardoni F, Gasteiger J (2004) A graph-based genetic algorithm and its application to the multiobjective evolution of median molecules. J Chem Inform Comput Sci. 44:1079\u20131087. https:\/\/doi.org\/10.1021\/ci034290p","journal-title":"J Chem Inform Comput Sci."},{"key":"431_CR7","doi-asserted-by":"publisher","first-page":"e1002380","DOI":"10.1371\/journal.pcbi.1002380","volume":"8","author":"M Hartenfeller","year":"2012","unstructured":"Hartenfeller M, Zettl H, Walter M, Rupp M, Reisen F, Proschak E, Weggen S, Stark H, Schneider G (2012) DOGS: reaction-driven de novo design of bioactive compounds. PLoS Comput Biol 8:e1002380","journal-title":"PLoS Comput Biol"},{"key":"431_CR8","doi-asserted-by":"publisher","first-page":"1169","DOI":"10.1021\/acs.jcim.5b00073","volume":"55","author":"NC Firth","year":"2015","unstructured":"Firth NC, Atrash B, Brown N, Blagg J (2015) MOARF, an integrated workflow for multiobjective optimization: implementation, synthesis, and biological evaluation. J Chem Inf Model 55:1169\u20131180. https:\/\/doi.org\/10.1021\/acs.jcim.5b00073","journal-title":"J Chem Inf Model"},{"key":"431_CR9","doi-asserted-by":"publisher","first-page":"4171","DOI":"10.1021\/acs.jmedchem.5b00886","volume":"59","author":"N Ch\u00e9ron","year":"2016","unstructured":"Ch\u00e9ron N, Jasty N, Shakhnovich EI (2016) OpenGrowth: an automated and rational algorithm for finding new protein ligands. J Med Chem 59:4171\u20134188. https:\/\/doi.org\/10.1021\/acs.jmedchem.5b00886","journal-title":"J Med Chem"},{"key":"431_CR10","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/1758-2946-6-7","volume":"6","author":"D Hoksza","year":"2014","unstructured":"Hoksza D, \u0160koda P, Vor\u0161il\u00e1k M, Svozil D (2014) Molpher: a software framework for systematic chemical space exploration. J Cheminform 6:7. https:\/\/doi.org\/10.1186\/1758-2946-6-7","journal-title":"J Cheminform"},{"key":"431_CR11","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 55:5904\u20135937. https:\/\/doi.org\/10.1002\/anie.201506101","journal-title":"Angew Chem Int Ed"},{"key":"431_CR12","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1021\/acscentsci.7b00401","volume":"4","author":"L Batiste","year":"2018","unstructured":"Batiste L, Unzue A, Dolbois A, Hassler F, Wang X, Deerain N, Zhu J, Spiliotopoulos D, Nevado C, Caflisch A (2018) Chemical space expansion of bromodomain ligands guided by in silico virtual couplings (AutoCouple). ACS Cent Sci 4:180\u2013188. https:\/\/doi.org\/10.1021\/acscentsci.7b00401","journal-title":"ACS Cent Sci"},{"key":"431_CR13","doi-asserted-by":"publisher","first-page":"5442","DOI":"10.1021\/acs.jmedchem.8b00494","volume":"61","author":"D Merk","year":"2018","unstructured":"Merk D, Grisoni F, Friedrich L, Gelzinyte E, Schneider G (2018) Computer-assisted discovery of retinoid X receptor modulating natural products and isofunctional mimetics. J Med Chem 61:5442\u20135447. https:\/\/doi.org\/10.1021\/acs.jmedchem.8b00494","journal-title":"J Med Chem"},{"key":"431_CR14","doi-asserted-by":"publisher","first-page":"1630","DOI":"10.1021\/ci9000458","volume":"49","author":"PS Kutchukian","year":"2009","unstructured":"Kutchukian PS, Lou D, Shakhnovich EI (2009) FOG: fragment optimized growth algorithm for the de novo generation of molecules occupying druglike chemical space. J Chem Inf Model 49:1630\u20131642. https:\/\/doi.org\/10.1021\/ci9000458","journal-title":"J Chem Inf Model"},{"key":"431_CR15","doi-asserted-by":"publisher","first-page":"627","DOI":"10.1021\/acs.jcim.6b00596","volume":"57","author":"T Liu","year":"2017","unstructured":"Liu T, Naderi M, Alvin C, Mukhopadhyay S, Brylinski M (2017) Break down in order to build up: decomposing small molecules for fragment-based drug design with eMolFrag. J Chem Inf Model 57:627\u2013631. https:\/\/doi.org\/10.1021\/acs.jcim.6b00596","journal-title":"J Chem Inf Model"},{"key":"431_CR16","doi-asserted-by":"publisher","first-page":"1518","DOI":"10.1021\/ci400078g","volume":"53","author":"AR Beccari","year":"2013","unstructured":"Beccari AR, Cavazzoni C, Beato C, Costantino G (2013) LiGen: a high performance workflow for chemistry driven de novo design. J Chem Inf Model 53:1518\u20131527. https:\/\/doi.org\/10.1021\/ci400078g","journal-title":"J Chem Inf Model"},{"key":"431_CR17","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1002\/wcms.49","volume":"1","author":"M Hartenfeller","year":"2011","unstructured":"Hartenfeller M, Schneider G (2011) Enabling future drug discovery by de novo design. Wiley Interdiscip Rev 1:742\u2013759. https:\/\/doi.org\/10.1002\/wcms.49","journal-title":"Wiley Interdiscip Rev"},{"key":"431_CR18","doi-asserted-by":"publisher","first-page":"48","DOI":"10.1186\/s13321-017-0235-x","volume":"9","author":"M Olivecrona","year":"2017","unstructured":"Olivecrona M, Blaschke T, Engkvist O, Chen H (2017) Molecular de-novo design through deep reinforcement learning. Journal of Cheminformatics 9:48. https:\/\/doi.org\/10.1186\/s13321-017-0235-x","journal-title":"Journal of Cheminformatics"},{"issue":"7","key":"431_CR19","doi-asserted-by":"publisher","first-page":"eaap7885","DOI":"10.1126\/sciadv.aap7885","volume":"4","author":"M Popova","year":"2018","unstructured":"Popova M, Isayev O, Tropsha A (2018) Deep reinforcement learning for de-novo drug design. Scie Adv 4(7):eaap7885. https:\/\/doi.org\/10.1126\/sciadv.aap7885","journal-title":"Scie Adv"},{"key":"431_CR20","doi-asserted-by":"publisher","first-page":"875","DOI":"10.1021\/acs.jcim.6b00754","volume":"57","author":"W Yuan","year":"2017","unstructured":"Yuan W, Jiang D, Nambiar DK, Liew LP, Hay MP, Bloomstein J, Lu P, Turner B, Le Q-T, Tibshirani R, Khatri P, Moloney MG, Koong AC (2017) Chemical space mimicry for drug discovery. J Chem Inf Model 57:875\u2013882. https:\/\/doi.org\/10.1021\/acs.jcim.6b00754","journal-title":"J Chem Inf Model"},{"issue":"1","key":"431_CR21","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s13321-018-0287-6","volume":"10","author":"Y Li","year":"2018","unstructured":"Li Y, Zhang L, Liu Z (2018) Multi-objective de novo drug design with conditional graph generative model. J Cheminform. 10(1):33. https:\/\/doi.org\/10.1186\/s13321-018-0287-6","journal-title":"J Cheminform."},{"key":"431_CR22","doi-asserted-by":"publisher","first-page":"4398","DOI":"10.1021\/acs.molpharmaceut.8b00839","volume":"15","author":"D Polykovskiy","year":"2018","unstructured":"Polykovskiy D, Zhebrak A, Vetrov D, Ivanenkov Y, Aladinskiy V, Mamoshina P, Bozdaganyan M, Aliper A, Zhavoronkov A, Kadurin A (2018) Entangled conditional adversarial autoencoder for de novo drug discovery. Mol Pharm 15:4398\u20134405. https:\/\/doi.org\/10.1021\/acs.molpharmaceut.8b00839","journal-title":"Mol Pharm"},{"key":"431_CR23","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.7b00690","author":"E Putin","year":"2018","unstructured":"Putin E, Asadulaev A, Ivanenkov Y, Aladinskiy V, Sanchez-Lengeling B, Aspuru-Guzik A, Zhavoronkov A (2018) Reinforced adversarial neural computer for de novo molecular design. J Chem Inf Model. https:\/\/doi.org\/10.1021\/acs.jcim.7b00690","journal-title":"J Chem Inf Model"},{"key":"431_CR24","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1021\/acscentsci.7b00512","volume":"4","author":"MHS Segler","year":"2018","unstructured":"Segler MHS, Kogej T, Tyrchan C, Waller MP (2018) Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci 4:120\u2013131. https:\/\/doi.org\/10.1021\/acscentsci.7b00512","journal-title":"ACS Cent Sci"},{"key":"431_CR25","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1021\/ci00014a017","volume":"33","author":"MI Skvortsova","year":"1993","unstructured":"Skvortsova MI, Baskin II, Slovokhotova OL, Palyulin VA, Zefirov NS (1993) Inverse problem in QSAR\/QSPR studies for the case of topological indexes characterizing molecular shape (Kier indices). J Chem Inform Comput Sci. 33:630\u2013634. https:\/\/doi.org\/10.1021\/ci00014a017","journal-title":"J Chem Inform Comput Sci."},{"key":"431_CR26","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1021\/ci020346o","volume":"43","author":"J-L Faulon","year":"2003","unstructured":"Faulon J-L, Churchwell CJ, Visco DP (2003) The signature molecular descriptor. 2. enumerating molecules from their extended valence sequences. J Chem Inform Comput Sci. 43:721\u2013734. https:\/\/doi.org\/10.1021\/ci020346o","journal-title":"J Chem Inform Comput Sci."},{"key":"431_CR27","doi-asserted-by":"publisher","first-page":"707","DOI":"10.1021\/ci020345w","volume":"43","author":"J-L Faulon","year":"2003","unstructured":"Faulon J-L, Visco DP, Pophale RS (2003) The signature molecular descriptor. 1. Using extended valence sequences in QSAR and QSPR studies. J Chem Inform Comput Sci. 43:707\u2013720. https:\/\/doi.org\/10.1021\/ci020345w","journal-title":"J Chem Inform Comput Sci."},{"key":"431_CR28","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1002\/minf.200900038","volume":"29","author":"T Miyao","year":"2010","unstructured":"Miyao T, Arakawa M, Funatsu K (2010) Exhaustive structure generation for inverse-QSPR\/QSAR. Mol Inform 29:111\u2013125. https:\/\/doi.org\/10.1002\/minf.200900038","journal-title":"Mol Inform"},{"key":"431_CR29","doi-asserted-by":"publisher","first-page":"286","DOI":"10.1021\/acs.jcim.5b00628","volume":"56","author":"T Miyao","year":"2016","unstructured":"Miyao T, Kaneko H, Funatsu K (2016) Inverse QSPR\/QSAR analysis for chemical structure generation (from y to x). J Chem Inf Model 56:286\u2013299. https:\/\/doi.org\/10.1021\/acs.jcim.5b00628","journal-title":"J Chem Inf Model"},{"key":"431_CR30","doi-asserted-by":"publisher","first-page":"1700030","DOI":"10.1002\/minf.201700030","volume":"36","author":"T Miyao","year":"2017","unstructured":"Miyao T, Funatsu K (2017) Finding chemical structures corresponding to a set of coordinates in chemical descriptor space. Mol Inform 36:1700030. https:\/\/doi.org\/10.1002\/minf.201700030","journal-title":"Mol Inform"},{"key":"431_CR31","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","volume":"4","author":"R G\u00f3mez-Bombarelli","year":"2018","unstructured":"G\u00f3mez-Bombarelli R, Wei JN, Duvenaud D, Hern\u00e1ndez-Lobato JM, S\u00e1nchez-Lengeling B, Sheberla D, Aguilera-Iparraguirre J, Hirzel TD, Adams RP, Aspuru-Guzik A (2018) Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci 4:268\u2013276. https:\/\/doi.org\/10.1021\/acscentsci.7b00572","journal-title":"ACS Cent Sci"},{"key":"431_CR32","doi-asserted-by":"publisher","first-page":"828","DOI":"10.1039\/C9ME00039A","volume":"4","author":"DC Elton","year":"2019","unstructured":"Elton DC, Boukouvalas Z, Fuge MD, Chung PW (2019) Deep learning for molecular design\u2014a review of the state of the art. Mol Syst Des Eng 4:828\u2013849. https:\/\/doi.org\/10.1039\/C9ME00039A","journal-title":"Mol Syst Des Eng"},{"key":"431_CR33","doi-asserted-by":"publisher","first-page":"902","DOI":"10.1021\/acs.jcim.8b00173","volume":"58","author":"A Dalke","year":"2018","unstructured":"Dalke A, Hert J, Kramer C (2018) mmpdb: an Open-Source matched molecular pair platform for large multiproperty data sets. J Chem Inf Model 58:902\u2013910. https:\/\/doi.org\/10.1021\/acs.jcim.8b00173","journal-title":"J Chem Inf Model"},{"key":"431_CR34","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1021\/ci900450m","volume":"50","author":"J Hussain","year":"2010","unstructured":"Hussain J, Rea C (2010) Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets. J Chem Inf Model 50:339\u2013348. https:\/\/doi.org\/10.1021\/ci900450m","journal-title":"J Chem Inf Model"},{"key":"431_CR35","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/1758-2946-1-8","volume":"1","author":"P Ertl","year":"2009","unstructured":"Ertl P, Schuffenhauer A (2009) Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminform 1:8. https:\/\/doi.org\/10.1186\/1758-2946-1-8","journal-title":"J Cheminform"},{"key":"431_CR36","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 58:252\u2013261. https:\/\/doi.org\/10.1021\/acs.jcim.7b00622","journal-title":"J Chem Inf Model"},{"key":"431_CR37","doi-asserted-by":"publisher","first-page":"2719","DOI":"10.1021\/jm901137j","volume":"53","author":"JB Baell","year":"2010","unstructured":"Baell JB, Holloway GA (2010) New substructure filters for removal of pan assay interference compounds (PAINS) from screening libraries and for their exclusion in bioassays. J Med Chem 53:2719\u20132740. https:\/\/doi.org\/10.1021\/jm901137j","journal-title":"J Med Chem"},{"key":"431_CR38","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00839","author":"N Brown","year":"2019","unstructured":"Brown N, Fiscato M, Segler MHS, Vaucher AC (2019) GuacaMol: benchmarking models for de novo molecular design. J Chem Inf Model. https:\/\/doi.org\/10.1021\/acs.jcim.8b00839","journal-title":"J Chem Inf Model"},{"key":"431_CR39","doi-asserted-by":"crossref","unstructured":"Polykovskiy D, Zhebrak A, Sanchez-Lengeling B, Golovanov S, Tatanov O, Belyaev S, Kurbanov R, Artamonov A, Aladinskiy V, Veselov M, Kadurin A (2019) Molecular Sets (MOSES): a benchmarking platform for molecular generation models. arxiv","DOI":"10.3389\/fphar.2020.565644"},{"key":"431_CR40","unstructured":"Structure sanitization workflow (2019). https:\/\/bitbucket.imtm.cz\/projects\/STD\/repos\/std\/browse"},{"key":"431_CR41","unstructured":"JChem 19.2.0 (2019). ChemAxon http:\/\/www.chemaxon.com"},{"key":"431_CR42","unstructured":"RDKit: Open-Source Cheminformatics Software 2017.09 (2017). http:\/\/rdkit.org\/"},{"key":"431_CR43","doi-asserted-by":"publisher","first-page":"1529","DOI":"10.1021\/ci100209a","volume":"50","author":"K Schomburg","year":"2010","unstructured":"Schomburg K, Ehrlich H-C, Stierand K, Rarey M (2010) From structure diagrams to visual chemical patterns. J Chem Inf Model 50:1529\u20131535. https:\/\/doi.org\/10.1021\/ci100209a","journal-title":"J Chem Inf Model"},{"key":"431_CR44","doi-asserted-by":"publisher","first-page":"6752","DOI":"10.1021\/jm901241e","volume":"52","author":"F Lovering","year":"2009","unstructured":"Lovering F, Bikker J, Humblet C (2009) Escape from flatland: increasing saturation as an approach to improving clinical success. J Med Chem 52:6752\u20136756. https:\/\/doi.org\/10.1021\/jm901241e","journal-title":"J Med Chem"},{"key":"431_CR45","doi-asserted-by":"publisher","first-page":"7709","DOI":"10.1021\/jm1008456","volume":"53","author":"Y Yang","year":"2010","unstructured":"Yang Y, Chen H, Nilsson I, Muresan S, Engkvist O (2010) Investigation of the relationship between topology and selectivity for druglike molecules. J Med Chem 53:7709\u20137714. https:\/\/doi.org\/10.1021\/jm1008456","journal-title":"J Med Chem"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-020-00431-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-020-00431-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-020-00431-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T20:04:31Z","timestamp":1619035471000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-020-00431-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,22]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["431"],"URL":"https:\/\/doi.org\/10.1186\/s13321-020-00431-w","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.2.23402\/v2","asserted-by":"object"},{"id-type":"doi","id":"10.21203\/rs.2.23402\/v1","asserted-by":"object"}]},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,22]]},"assertion":[{"value":"11 February 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The author declares no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"28"}}