乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,30]],"date-time":"2025-03-30T20:26:27Z","timestamp":1743366387927},"reference-count":53,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T00:00:00Z","timestamp":1700697600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T00:00:00Z","timestamp":1700697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"abstract":"Abstract<\/jats:title>Assigning or proposing a catalysing enzyme given a chemical or biochemical reaction is of great interest to life sciences and chemistry alike. The exploration and design of metabolic pathways and the challenge of finding more sustainable enzyme-catalysed alternatives to traditional organic reactions are just two examples of tasks that require an association between reaction and enzyme. However, given the lack of large and balanced annotated data sets of enzyme-catalysed reactions, assigning an enzyme to a reaction still relies on expert-curated rules and databases. Here, we present a data-driven explainable human-in-the-loop machine learning approach to support and ultimately automate the association of a catalysing enzyme with a given biochemical reaction. In addition, the proposed method is capable of predicting enzymes as candidate catalysts for organic reactions amendable to biocatalysis. Finally, the introduced explainability and visualisation methods can easily be generalised to support other machine-learning approaches involving chemical and biochemical reactions.<\/jats:p>","DOI":"10.1186\/s13321-023-00784-y","type":"journal-article","created":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T16:02:03Z","timestamp":1700755323000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification"],"prefix":"10.1186","volume":"15","author":[{"given":"Daniel","family":"Probst","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,23]]},"reference":[{"issue":"D1","key":"784_CR1","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1093\/nar\/gkw1092","volume":"45","author":"M Kanehisa","year":"2017","unstructured":"Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45(D1):353\u2013361. https:\/\/doi.org\/10.1093\/nar\/gkw1092","journal-title":"Nucleic Acids Res"},{"issue":"29","key":"784_CR2","doi-asserted-by":"publisher","first-page":"9880","DOI":"10.1073\/pnas.0802208105","volume":"105","author":"D-S Lee","year":"2008","unstructured":"Lee D-S, Park J, Kay KA, Christakis NA, Oltvai ZN, Barab\u00e1si A-L (2008) The implications of human metabolic network topology for disease comorbidity. Proc Natl Acad Sci 105(29):9880\u20139885. https:\/\/doi.org\/10.1073\/pnas.0802208105","journal-title":"Proc Natl Acad Sci"},{"issue":"1","key":"784_CR3","doi-asserted-by":"publisher","first-page":"3586","DOI":"10.1038\/s41467-019-11581-3","volume":"10","author":"H Lu","year":"2019","unstructured":"Lu H, Li F, S\u00e1nchez BJ, Zhu Z, Li G, Domenzain I, Marci\u0161auskas S, Anton PM, Lappa D, Lieven C, Beber ME, Sonnenschein N, Kerkhoven EJ, Nielsen J (2019) A consensus S. cerevisiae metabolic model Yeast8 and its ecosystem for comprehensively probing cellular metabolism. Nat Commun 10(1):3586. https:\/\/doi.org\/10.1038\/s41467-019-11581-3","journal-title":"Nat Commun"},{"issue":"1","key":"784_CR4","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1038\/s41467-017-02362-x","volume":"9","author":"A Kumar","year":"2018","unstructured":"Kumar A, Wang L, Ng CY, Maranas CD (2018) Pathway design using de novo steps through uncharted biochemical spaces. Nat Commun 9(1):184. https:\/\/doi.org\/10.1038\/s41467-017-02362-x","journal-title":"Nat Commun"},{"issue":"1","key":"784_CR5","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1038\/nrd.2017.152","volume":"17","author":"JA Harrigan","year":"2018","unstructured":"Harrigan JA, Jacq X, Martin NM, Jackson SP (2018) Deubiquitylating enzymes and drug discovery: emerging opportunities. Nat Rev Drug Discov 17(1):57\u201378. https:\/\/doi.org\/10.1038\/nrd.2017.152","journal-title":"Nat Rev Drug Discov"},{"key":"784_CR6","doi-asserted-by":"publisher","first-page":"54","DOI":"10.1016\/j.compbiomed.2019.01.008","volume":"106","author":"SR Kazmi","year":"2019","unstructured":"Kazmi SR, Jun R, Yu M-S, Jung C, Na D (2019) In silico approaches and tools for the prediction of drug metabolism and fate: A review. Comput Biol Med 106:54\u201364. https:\/\/doi.org\/10.1016\/j.compbiomed.2019.01.008","journal-title":"Comput Biol Med"},{"issue":"3","key":"784_CR7","doi-asserted-by":"publisher","first-page":"1968","DOI":"10.1039\/d0cs00763c","volume":"50","author":"S Slagman","year":"2020","unstructured":"Slagman S, Fessner W-D (2020) Biocatalytic routes to anti-viral agents and their synthetic intermediates. Chem Soc Rev 50(3):1968\u20132009. https:\/\/doi.org\/10.1039\/d0cs00763c","journal-title":"Chem Soc Rev"},{"issue":"2","key":"784_CR8","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1021\/acs.chemrev.7b00203","volume":"118","author":"RA Sheldon","year":"2018","unstructured":"Sheldon RA, Woodley JM (2018) Role of biocatalysis in sustainable chemistry. Chem Rev 118(2):801\u2013838. https:\/\/doi.org\/10.1021\/acs.chemrev.7b00203","journal-title":"Chem Rev"},{"issue":"1","key":"784_CR9","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1002\/anie.202006648","volume":"60","author":"S Wu","year":"2021","unstructured":"Wu S, Snajdrova R, Moore JC, Baldenius K, Bornscheuer UT (2021) Biocatalysis: enzymatic synthesis for industrial applications. Angew Chem Int Ed 60(1):88\u2013119. https:\/\/doi.org\/10.1002\/anie.202006648","journal-title":"Angew Chem Int Ed"},{"key":"784_CR10","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1016\/j.ymben.2017.12.002","volume":"45","author":"B Del\u00e9pine","year":"2018","unstructured":"Del\u00e9pine B, Duigou T, Carbonell P, Faulon J-L (2018) RetroPath2.0: a retrosynthesis workflow for metabolic engineers. Metab Eng 45:158\u2013170. https:\/\/doi.org\/10.1016\/j.ymben.2017.12.002","journal-title":"Metab Eng"},{"issue":"1","key":"784_CR11","doi-asserted-by":"publisher","first-page":"1560","DOI":"10.1038\/s41467-022-29238-z","volume":"13","author":"HM Peyhani","year":"2022","unstructured":"Peyhani HM, Hafner J, Sveshnikova A, Viterbo V, Hatzimanikatis V (2022) Expanding biochemical knowledge and illuminating metabolic dark matter with ATLASx. Nat Commun 13(1):1560. https:\/\/doi.org\/10.1038\/s41467-022-29238-z","journal-title":"Nat Commun"},{"issue":"1","key":"784_CR12","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1038\/s41467-022-28536-w","volume":"13","author":"D Probst","year":"2022","unstructured":"Probst D, Manica M, Teukam YGN, Castrogiovanni A, Paratore F, Laino T (2022) Biocatalysed synthesis planning using data-driven learning. Nat Commun 13(1):964. https:\/\/doi.org\/10.1038\/s41467-022-28536-w","journal-title":"Nat Commun"},{"issue":"25","key":"784_CR13","doi-asserted-by":"publisher","first-page":"8648","DOI":"10.1039\/d1sc02362d","volume":"12","author":"D Kreutter","year":"2021","unstructured":"Kreutter D, Schwaller P, Reymond J-L (2021) Predicting enzymatic reactions with a molecular transformer. Chem Sci 12(25):8648\u20138659. https:\/\/doi.org\/10.1039\/d1sc02362d","journal-title":"Chem Sci"},{"issue":"1","key":"784_CR14","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1186\/s12918-018-0593-7","volume":"12","author":"PD Karp","year":"2018","unstructured":"Karp PD, Weaver D, Latendresse M (2018) How accurate is automated gap filling of metabolic models? BMC Syst Biol 12(1):73. https:\/\/doi.org\/10.1186\/s12918-018-0593-7","journal-title":"BMC Syst Biol"},{"key":"784_CR15","doi-asserted-by":"publisher","unstructured":"Lowe D (2017) Chemical reactions from US patents (1976\u2013Sep2016). figshare https:\/\/doi.org\/10.6084\/M9.FIGSHARE.5104873.V1. https:\/\/figshare.com\/articles\/dataset\/Chemical_reactions_from_US_patents_1976-Sep2016_\/5104873\/1. Accessed 16 Dec 2022","DOI":"10.6084\/M9.FIGSHARE.5104873.V1"},{"issue":"Database","key":"784_CR16","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1093\/nar\/gky1049","volume":"47","author":"A Bateman","year":"2018","unstructured":"...Bateman A, Martin M-J, Orchard S, Magrane M, Alpi E, Bely B, Bingley M, Britto R, Bursteinas B, Busiello G, Bye-A-Jee H, Silva AD, Giorgi MD, Dogan T, Castro LG, Garmiri P, Georghiou G, Gonzales D, Gonzales L, Hatton-Ellis E, Ignatchenko A, Ishtiaq R, Jokinen P, Joshi V, Jyothi D, Lopez R, Luo J, Lussi Y, MacDougall A, Madeira F, Mahmoudy M, Menchi M, Nightingale A, Onwubiko J, Palka B, Pichler K, Pundir S, Qi G, Raj S, Renaux A, Lopez MR, Saidi R, Sawford T, Shypitsyna A, Speretta E, Turner E, Tyagi N, Vasudev P, Volynkin V, Wardell T, Warner K, Watkins X, Zaru R, Zellner H, Bridge A, Xenarios I, Poux S, Redaschi N, Aimo L, Argoud-Puy G, Auchincloss A, Axelsen K, Bansal P, Baratin D, Blatter M-C, Bolleman J, Boutet E, Breuza L, Casals-Casas C, de Castro E, Coudert E, Cuche B, Doche M, Dornevil D, Estreicher A, Famiglietti L, Feuermann M, Gasteiger E, Gehant S, Gerritsen V, Gos A, Gruaz N, Hinz U, Hulo C, Hyka-Nouspikel N, Jungo F, Keller G, Kerhornou A, Lara V, Lemercier P, Lieberherr D, Lombardot T, Martin X, Masson P, Morgat A, Neto TB, Paesano S, Pedruzzi I, Pilbout S, Pozzato M, Pruess M, Rivoire C, Sigrist C, Sonesson K, Stutz A, Sundaram S, Tognolli M, Verbregue L, Wu CH, Arighi CN, Arminski L, Chen C, Chen Y, Cowart J, Garavelli JS, Huang H, Laiho K, McGarvey P, Natale DA, Ross K, Vinayaka CR, Wang Q, Wang Y, Yeh L-S, Zhang J (2018) UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res 47(Database):1049. https:\/\/doi.org\/10.1093\/nar\/gky1049","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"784_CR17","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1093\/nar\/gkab1016","volume":"50","author":"P Bansal","year":"2021","unstructured":"Bansal P, Morgat A, Axelsen KB, Muthukrishnan V, Coudert E, Aimo L, Hyka-Nouspikel N, Gasteiger E, Kerhornou A, Neto TB, Pozzato M, Blatter M-C, Ignatchenko A, Redaschi N, Bridge A (2021) Rhea, the reaction knowledgebase in 2022. Nucleic Acids Res 50(D1):693\u2013700. https:\/\/doi.org\/10.1093\/nar\/gkab1016","journal-title":"Nucleic Acids Res"},{"issue":"Suppl\u20131","key":"784_CR18","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1093\/nar\/gkn582","volume":"37","author":"AG McDonald","year":"2009","unstructured":"McDonald AG, Boyce S, Tipton KF (2009) ExplorEnz: the primary source of the IUBMB enzyme list. Nucleic Acids Res 37(Suppl\u20131):593\u2013597. https:\/\/doi.org\/10.1093\/nar\/gkn582","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"784_CR19","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1093\/nar\/28.1.304","volume":"28","author":"A Bairoch","year":"2000","unstructured":"Bairoch A (2000) The ENZYME database in 2000. Nucleic Acids Res 28(1):304\u2013305. https:\/\/doi.org\/10.1093\/nar\/28.1.304","journal-title":"Nucleic Acids Res"},{"issue":"16","key":"784_CR20","doi-asserted-by":"publisher","first-page":"10218","DOI":"10.1021\/acs.chemrev.1c00033","volume":"121","author":"M Meuwly","year":"2021","unstructured":"Meuwly M (2021) Mach Learn Chem React. Chemical Rev 121(16):10218\u201310239. https:\/\/doi.org\/10.1021\/acs.chemrev.1c00033","journal-title":"Chemical Rev"},{"key":"784_CR21","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.1604","author":"P Schwaller","year":"2022","unstructured":"Schwaller P, Vaucher AC, Laplaza R, Bunne C, Krause A, Corminboeuf C, Laino T (2022) Machine intelligence for chemical reaction space. Wiley Interdiscip Rev Comput Mol Sci. https:\/\/doi.org\/10.1002\/wcms.1604","journal-title":"Wiley Interdiscip Rev Comput Mol Sci"},{"key":"784_CR22","doi-asserted-by":"publisher","first-page":"714","DOI":"10.3389\/fgene.2018.00714","volume":"9","author":"Z Zou","year":"2019","unstructured":"Zou Z, Tian S, Gao X, Li Y (2019) mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning. Front Genet 9:714. https:\/\/doi.org\/10.3389\/fgene.2018.00714","journal-title":"Front Genet"},{"issue":"1","key":"784_CR23","doi-asserted-by":"publisher","first-page":"3168","DOI":"10.1038\/s41467-021-23303-9","volume":"12","author":"V Gligorijevi\u0107","year":"2021","unstructured":"Gligorijevi\u0107 V, Renfrew PD, Kosciolek T, Leman JK, Berenberg D, Vatanen T, Chandler C, Taylor BC, Fisk IM, Vlamakis H, Xavier RJ, Knight R, Cho K, Bonneau R (2021) Structure-based protein function prediction using graph convolutional networks. Nat Commun 12(1):3168. https:\/\/doi.org\/10.1038\/s41467-021-23303-9","journal-title":"Nat Commun"},{"issue":"1","key":"784_CR24","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1186\/s12859-018-2368-y","volume":"19","author":"A Dalkiran","year":"2018","unstructured":"Dalkiran A, Rifaioglu AS, Martin MJ, Cetin-Atalay R, Atalay V, Do\u01e7an T (2018) ECPred: a tool for the prediction of the enzymatic functions of protein sequences based on the EC nomenclature. BMC Bioinf 19(1):334. https:\/\/doi.org\/10.1186\/s12859-018-2368-y","journal-title":"BMC Bioinf"},{"issue":"28","key":"784_CR25","doi-asserted-by":"publisher","first-page":"13996","DOI":"10.1073\/pnas.1821905116","volume":"116","author":"JY Ryu","year":"2019","unstructured":"Ryu JY, Kim HU, Lee SY (2019) Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers. Proc Natl Acad Sci 116(28):13996\u201314001. https:\/\/doi.org\/10.1073\/pnas.1821905116","journal-title":"Proc Natl Acad Sci"},{"issue":"50","key":"784_CR26","doi-asserted-by":"publisher","first-page":"16487","DOI":"10.1021\/ja0466457","volume":"126","author":"M Kotera","year":"2004","unstructured":"Kotera M, Okuno Y, Hattori M, Goto S, Kanehisa M (2004) Computational assignment of the EC numbers for genomic-scale analysis of enzymatic reactions. J Am Chem Soc 126(50):16487\u201316498. https:\/\/doi.org\/10.1021\/ja0466457","journal-title":"J Am Chem Soc"},{"issue":"12","key":"784_CR27","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1093\/bioinformatics\/btp223","volume":"25","author":"Y Yamanishi","year":"2009","unstructured":"Yamanishi Y, Hattori M, Kotera M, Goto S, Kanehisa M (2009) E-zyme: predicting potential EC numbers from the chemical transformation pattern of substrate-product pairs. Bioinformatics 25(12):179\u2013186. https:\/\/doi.org\/10.1093\/bioinformatics\/btp223","journal-title":"Bioinformatics"},{"issue":"2","key":"784_CR28","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1038\/nmeth.2803","volume":"11","author":"SA Rahman","year":"2014","unstructured":"Rahman SA, Cuesta SM, Furnham N, Holliday GL, Thornton JM (2014) EC-BLAST: a tool to automatically search and compare enzyme reactions. Nat Methods 11(2):171\u2013174. https:\/\/doi.org\/10.1038\/nmeth.2803","journal-title":"Nat Methods"},{"issue":"7","key":"784_CR29","doi-asserted-by":"publisher","first-page":"1839","DOI":"10.1021\/ci900104b","volume":"49","author":"DARS Latino","year":"2009","unstructured":"Latino DARS, Aires-de-Sousa J (2009) Assignment of EC numbers to enzymatic reactions with MOLMAP reaction descriptors and random forests. J Chem Inf Model 49(7):1839\u20131846. https:\/\/doi.org\/10.1021\/ci900104b","journal-title":"J Chem Inf Model"},{"issue":"1","key":"784_CR30","doi-asserted-by":"publisher","first-page":"1000661","DOI":"10.1371\/journal.pcbi.1000661","volume":"6","author":"V Egelhofer","year":"2010","unstructured":"Egelhofer V, Schomburg I, Schomburg D (2010) Automatic assignment of EC numbers. PLoS Comput Biol 6(1):1000661. https:\/\/doi.org\/10.1371\/journal.pcbi.1000661","journal-title":"PLoS Comput Biol"},{"issue":"12","key":"784_CR31","doi-asserted-by":"publisher","first-page":"52901","DOI":"10.1371\/journal.pone.0052901","volume":"7","author":"Q-N Hu","year":"2012","unstructured":"Hu Q-N, Zhu H, Li X, Zhang M, Deng Z, Yang X, Deng Z (2012) Assignment of EC numbers to enzymatic reactions with reaction difference fingerprints. PLoS ONE 7(12):52901. https:\/\/doi.org\/10.1371\/journal.pone.0052901","journal-title":"PLoS ONE"},{"issue":"12","key":"784_CR32","doi-asserted-by":"publisher","first-page":"2153","DOI":"10.1093\/bioinformatics\/bty065","volume":"34","author":"P Carbonell","year":"2018","unstructured":"Carbonell P, Wong J, Swainston N, Takano E, Turner NJ, Scrutton NS, Kell DB, Breitling R, Faulon J-L (2018) Selenzyme: enzyme selection tool for pathway design. Bioinformatics 34(12):2153\u20132154. https:\/\/doi.org\/10.1093\/bioinformatics\/bty065","journal-title":"Bioinformatics"},{"issue":"3","key":"784_CR33","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1093\/bioinformatics\/bts700","volume":"29","author":"Y Matsuta","year":"2013","unstructured":"Matsuta Y, Ito M, Tohsato Y (2013) ECOH: an Enzyme Commission number predictor using mutual information and a support vector machine. Bioinformatics 29(3):365\u2013372. https:\/\/doi.org\/10.1093\/bioinformatics\/bts700","journal-title":"Bioinformatics"},{"issue":"15","key":"784_CR34","doi-asserted-by":"publisher","first-page":"7298","DOI":"10.1073\/pnas.1818877116","volume":"116","author":"N Hadadi","year":"2019","unstructured":"Hadadi N, MohammadiPeyhani H, Miskovic L, Seijo M, Hatzimanikatis V (2019) Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites. Proc Natl Acad Sci 116(15):7298\u20137307. https:\/\/doi.org\/10.1073\/pnas.1818877116","journal-title":"Proc Natl Acad Sci"},{"issue":"3","key":"784_CR35","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1007\/s11023-022-09603-z","volume":"32","author":"J Borrego-D\u00edaz","year":"2022","unstructured":"Borrego-D\u00edaz J, Gal\u00e1n-P\u00e1ez J (2022) Explainable artificial intelligence in data science. Minds Mach 32(3):485\u2013531. https:\/\/doi.org\/10.1007\/s11023-022-09603-z","journal-title":"Minds Mach"},{"key":"784_CR36","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2018.07.007","volume":"267","author":"T Miller","year":"2019","unstructured":"Miller T (2019) Explanation in artificial intelligence: Insights from the social sciences. Artificial Intelligence 267:1\u201338. https:\/\/doi.org\/10.1016\/j.artint.2018.07.007","journal-title":"Artificial Intelligence"},{"key":"784_CR37","doi-asserted-by":"publisher","DOI":"10.1038\/s41576-022-00532-2","author":"G Novakovsky","year":"2022","unstructured":"Novakovsky G, Dexter N, Libbrecht MW, Wasserman WW, Mostafavi S (2022) Obtaining genetics insights from deep learning via explainable artificial intelligence. Nat Rev Genet. https:\/\/doi.org\/10.1038\/s41576-022-00532-2","journal-title":"Nat Rev Genet"},{"key":"784_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107161","volume":"226","author":"HW Loh","year":"2022","unstructured":"Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR (2022) Application of explainable artificial intelligence for healthcare: a systematic review of the last decade (2011\u20132022). Comput Methods Progr Biomed 226:107161. https:\/\/doi.org\/10.1016\/j.cmpb.2022.107161","journal-title":"Comput Methods Progr Biomed"},{"key":"784_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.caeai.2022.100074","volume":"3","author":"H Khosravi","year":"2022","unstructured":"Khosravi H, Shum SB, Chen G, Conati C, Tsai Y-S, Kay J, Knight S, Martinez-Maldonado R, Sadiq S, Ga\u0161evi\u0107 D (2022) Explainable artificial intelligence in education. Comput Educ Artifl Intell 3:100074. https:\/\/doi.org\/10.1016\/j.caeai.2022.100074","journal-title":"Comput Educ Artifl Intell"},{"issue":"10","key":"784_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.isci.2022.105043","volume":"25","author":"A Mastropietro","year":"2022","unstructured":"Mastropietro A, Pasculli G, Feldmann C, Rodr\u00edguez-P\u00e9rez R, Bajorath J (2022) EdgeSHAPer: bond-centric Shapley value-based explanation method for graph neural networks. iScience 25(10):105043. https:\/\/doi.org\/10.1016\/j.isci.2022.105043","journal-title":"iScience"},{"issue":"1","key":"784_CR41","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/s13321-022-00673-w","volume":"15","author":"H Heberle","year":"2023","unstructured":"Heberle H, Zhao L, Schmidt S, Wolf T, Heinrich J (2023) XSMILES: interactive visualization for molecules, SMILES and XAI attribution scores. J Cheminf 15(1):2. https:\/\/doi.org\/10.1186\/s13321-022-00673-w","journal-title":"J Cheminf"},{"issue":"13","key":"784_CR42","doi-asserted-by":"publisher","first-page":"3697","DOI":"10.1039\/d1sc05259d","volume":"13","author":"GP Wellawatte","year":"2022","unstructured":"Wellawatte GP, Seshadri A, White AD (2022) Model agnostic generation of counterfactual explanations for molecules. Chem Sci 13(13):3697\u20133705. https:\/\/doi.org\/10.1039\/d1sc05259d","journal-title":"Chem Sci"},{"issue":"1","key":"784_CR43","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. https:\/\/doi.org\/10.1021\/ci00057a005","journal-title":"J Chem Inf Model"},{"issue":"2","key":"784_CR44","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1039\/d1dd00006c","volume":"1","author":"D Probst","year":"2022","unstructured":"Probst D, Schwaller P, Reymond J-L (2022) Reaction classification and yield prediction using the differential reaction fingerprint DRFP. Digital Discov 1(2):91\u201397. https:\/\/doi.org\/10.1039\/d1dd00006c","journal-title":"Digital Discov"},{"issue":"D1","key":"784_CR45","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1093\/nar\/gkaa1025","volume":"49","author":"A Chang","year":"2020","unstructured":"Chang A, Jeske L, Ulbrich S, Hofmann J, Koblitz J, Schomburg I, Neumann-Schaal M, Jahn D, Schomburg D (2020) BRENDA, the ELIXIR core data resource in 2021: new developments and updates. Nucleic Acids Res 49(D1):498\u2013508. https:\/\/doi.org\/10.1093\/nar\/gkaa1025","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"784_CR46","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1093\/nar\/gkz861","volume":"48","author":"DS Wishart","year":"2019","unstructured":"Wishart DS, Li C, Marcu A, Badran H, Pon A, Budinski Z, Patron J, Lipton D, Cao X, Oler E, Li K, Paccoud M, Hong C, Guo AC, Chan C, Wei W, Ramirez-Gaona M (2019) PathBank: a comprehensive pathway database for model organisms. Nucleic Acids Res 48(D1):470\u2013478. https:\/\/doi.org\/10.1093\/nar\/gkz861","journal-title":"Nucleic Acids Res"},{"issue":"D1","key":"784_CR47","doi-asserted-by":"publisher","first-page":"992","DOI":"10.1093\/nar\/gkaa992","volume":"49","author":"S Moretti","year":"2020","unstructured":"Moretti S, Tran V, Mehl F, Ibberson M, Pagni M (2020) MetaNetX\/MNXref: unified namespace for metabolites and biochemical reactions in the context of metabolic models. Nucleic Acids Res 49(D1):992. https:\/\/doi.org\/10.1093\/nar\/gkaa992","journal-title":"Nucleic Acids Res"},{"key":"784_CR48","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.1704.02685","author":"A Shrikumar","year":"2017","unstructured":"Shrikumar A, Greenside P, Kundaje A (2017) Learning important features through propagating activation differences. arXiv. https:\/\/doi.org\/10.48550\/arxiv.1704.02685","journal-title":"arXiv"},{"key":"784_CR49","doi-asserted-by":"publisher","DOI":"10.48550\/arxiv.1705.07874","author":"S Lundberg","year":"2017","unstructured":"Lundberg S, Lee S-I (2017) A unified approach to interpreting model predictions. arXiv. https:\/\/doi.org\/10.48550\/arxiv.1705.07874","journal-title":"arXiv"},{"issue":"1","key":"784_CR50","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1186\/s13321-018-0321-8","volume":"10","author":"D Probst","year":"2018","unstructured":"Probst D, Reymond J-L (2018) A probabilistic molecular fingerprint for big data settings. J Cheminf 10(1):66. https:\/\/doi.org\/10.1186\/s13321-018-0321-8","journal-title":"J Cheminf"},{"issue":"1","key":"784_CR51","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1021\/acs.jcim.7b00425","volume":"58","author":"D Probst","year":"2018","unstructured":"Probst D, Reymond J-L (2018) SmilesDrawer: parsing and drawing SMILES-encoded molecular structures using client-side Javascript. J Chem Inf Model 58(1):1\u20137. https:\/\/doi.org\/10.1021\/acs.jcim.7b00425","journal-title":"J Chem Inf Model"},{"key":"784_CR52","doi-asserted-by":"publisher","unstructured":"Hoyt CT. Rhea differential reaction fingerprints for enzyme classification prediction. https:\/\/doi.org\/10.5281\/zenodo.7591839","DOI":"10.5281\/zenodo.7591839"},{"key":"784_CR53","unstructured":"Bernhardsson E (2017) Annoy: approximate nearest neighbors in c++\/python optimized for memory usage and loading\/saving to disk. GitHub. https:\/\/github.com\/spotify\/annoy. Accessed 6 Sept 2022"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00784-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13321-023-00784-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-023-00784-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T16:02:20Z","timestamp":1700755340000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-023-00784-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,23]]},"references-count":53,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["784"],"URL":"https:\/\/doi.org\/10.1186\/s13321-023-00784-y","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,23]]},"assertion":[{"value":"22 May 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 November 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":"This study did not require ethics approval nor participation consents.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"This study does not require consents for publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The author declares no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"113"}}