{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T12:56:50Z","timestamp":1784293010802,"version":"3.55.0"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,6,26]],"date-time":"2018-06-26T00:00:00Z","timestamp":1529971200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81402853"],"award-info":[{"award-number":["81402853"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Basic Research Program","award":["2015CB910700"],"award-info":[{"award-number":["2015CB910700"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Cheminform"],"published-print":{"date-parts":[[2018,12]]},"DOI":"10.1186\/s13321-018-0283-x","type":"journal-article","created":{"date-parts":[[2018,6,26]],"date-time":"2018-06-26T00:01:03Z","timestamp":1529971263000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":759,"title":["ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database"],"prefix":"10.1186","volume":"10","author":[{"given":"Jie","family":"Dong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning-Ning","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhi-Jiang","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Defang","family":"Ouyang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ai-Ping","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3604-3785","authenticated-orcid":false,"given":"Dong-Sheng","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2018,6,26]]},"reference":[{"issue":"2","key":"283_CR1","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1038\/nrd4239","volume":"13","author":"A Mullard","year":"2014","unstructured":"Mullard A (2014) 2013 FDA drug approvals. Nat Rev Drug Discov. 13(2):85\u201389","journal-title":"Nat Rev Drug Discov."},{"issue":"2","key":"283_CR2","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1038\/nrd.2017.14","volume":"16","author":"A Mullard","year":"2017","unstructured":"Mullard A (2017) 2016 FDA drug approvals. Nat Rev Drug Discov. 16(2):73\u201376","journal-title":"Nat Rev Drug Discov."},{"issue":"15","key":"283_CR3","doi-asserted-by":"publisher","first-page":"1567","DOI":"10.1016\/j.jacc.2015.03.016","volume":"65","author":"CB Fordyce","year":"2015","unstructured":"Fordyce CB, Roe MT, Ahmad T, Libby P, Borer JS, Hiatt WR et al (2015) Cardiovascular drug development: is it dead or just hibernating? J Am Coll Cardiol 65(15):1567\u20131582","journal-title":"J Am Coll Cardiol"},{"issue":"11","key":"283_CR4","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.2174\/15680266113139990033","volume":"13","author":"F Cheng","year":"2013","unstructured":"Cheng F, Li W, Liu G, Tang Y (2013) In silico ADMET prediction: recent advances, current challenges and future trends. Curr Top Med Chem 13(11):1273\u20131289","journal-title":"Curr Top Med Chem"},{"issue":"4","key":"283_CR5","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1017\/S0033583515000190","volume":"48","author":"Y Wang","year":"2015","unstructured":"Wang Y, Xing J, Xu Y, Zhou N, Peng J, Xiong Z et al (2015) In silico ADME\/T modelling for rational drug design. Q Rev Biophys 48(4):488\u2013515","journal-title":"Q Rev Biophys"},{"issue":"6","key":"283_CR6","doi-asserted-by":"publisher","first-page":"349","DOI":"10.2165\/00126839-200708060-00003","volume":"8","author":"DS Wishart","year":"2007","unstructured":"Wishart DS (2007) Improving early drug discovery through ADME modelling: an overview. Drugs R&D 8(6):349\u2013362","journal-title":"Drugs R&D"},{"issue":"3","key":"283_CR7","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1517\/17460441.2015.1005071","volume":"10","author":"MC Rosales-Hernandez","year":"2015","unstructured":"Rosales-Hernandez MC, Correa-Basurto J (2015) The importance of employing computational resources for the automation of drug discovery. Expert Opin Drug Discov 10(3):213\u2013219","journal-title":"Expert Opin Drug Discov"},{"key":"283_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.addr.2015.06.006","volume":"86","author":"T Hou","year":"2015","unstructured":"Hou T (2015) Theme title: in silico ADMET predictions in pharmaceutical research. Adv Drug Deliver Rev. 86:1","journal-title":"Adv Drug Deliver Rev."},{"key":"283_CR9","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.addr.2015.03.014","volume":"86","author":"L Tao","year":"2015","unstructured":"Tao L, Zhang P, Qin C, Chen SY, Zhang C, Chen Z et al (2015) Recent progresses in the exploration of machine learning methods as in silico ADME prediction tools. Adv Drug Deliver Rev. 86:83\u2013100","journal-title":"Adv Drug Deliver Rev."},{"issue":"31","key":"283_CR10","doi-asserted-by":"publisher","first-page":"19007","DOI":"10.1039\/C6RA28442F","volume":"7","author":"N Wang","year":"2017","unstructured":"Wang N, Huang C, Dong J, Yao Z, Zhu M, Deng Z et al (2017) Predicting human intestinal absorption with modified random forest approach: a comprehensive evaluation of molecular representation, unbalanced data, and applicability domain issues. RSC Adv. 7(31):19007\u201319018","journal-title":"RSC Adv."},{"issue":"4","key":"283_CR11","doi-asserted-by":"publisher","first-page":"763","DOI":"10.1021\/acs.jcim.5b00642","volume":"56","author":"NN Wang","year":"2016","unstructured":"Wang NN, Dong J, Deng YH, Zhu MF, Wen M, Yao ZJ et al (2016) ADME properties evaluation in drug discovery: prediction of Caco-2 cell permeability using a combination of NSGA-II and boosting. J Chem Inf Model 56(4):763\u2013773","journal-title":"J Chem Inf Model"},{"issue":"9","key":"283_CR12","doi-asserted-by":"publisher","first-page":"4066","DOI":"10.1021\/acs.jmedchem.5b00104","volume":"58","author":"DEV Pires","year":"2015","unstructured":"Pires DEV, Blundell TL, Ascher DB (2015) pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem 58(9):4066\u20134072","journal-title":"J Med Chem"},{"issue":"10","key":"283_CR13","doi-asserted-by":"publisher","first-page":"1695","DOI":"10.1093\/bioinformatics\/btv010","volume":"31","author":"M Davies","year":"2015","unstructured":"Davies M, Dedman N, Hersey A, Papadatos G, Hall MD, Cucurull-Sanchez L et al (2015) ADME SARfari: comparative genomics of drug metabolizing systems. Bioinformatics 31(10):1695\u20131697","journal-title":"Bioinformatics"},{"key":"283_CR14","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.chemolab.2017.10.006","volume":"171","author":"J Dong","year":"2017","unstructured":"Dong J, Wang NN, Liu KY, Zhu MF, Yun YH, Zeng WB et al (2017) ChemBCPP: a freely available web server for calculating commonly used physicochemical properties. Chemometr Intell Lab Syst 171:65\u201373","journal-title":"Chemometr Intell Lab Syst"},{"key":"283_CR15","unstructured":"Landrum. RDKit: open-source cheminformatics. Release 2014.03.1. 2010"},{"issue":"1","key":"283_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1752-153X-2-1","volume":"2","author":"NM O\u2019Boyle","year":"2008","unstructured":"O\u2019Boyle NM, Morley C, Hutchison GR (2008) Pybel: a Python wrapper for the OpenBabel cheminformatics toolkit. Chem Cent J 2(1):1\u20137","journal-title":"Chem Cent J"},{"issue":"8","key":"283_CR17","doi-asserted-by":"publisher","first-page":"1092","DOI":"10.1093\/bioinformatics\/btt105","volume":"29","author":"D Cao","year":"2013","unstructured":"Cao D, Xu Q, Hu Q, Liang Y (2013) ChemoPy: freely available python package for computational biology and chemoinformatics. Bioinformatics 29(8):1092\u20131094","journal-title":"Bioinformatics"},{"issue":"1","key":"283_CR18","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1186\/s13321-015-0109-z","volume":"7","author":"J Dong","year":"2015","unstructured":"Dong J, Cao D, Miao H, Liu S, Deng B, Yun Y et al (2015) ChemDes: an integrated web-based platform for molecular descriptor and fingerprint computation. J Cheminform 7(1):60","journal-title":"J Cheminform."},{"issue":"1","key":"283_CR19","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1186\/s13321-016-0146-2","volume":"8","author":"J Dong","year":"2016","unstructured":"Dong J, Yao ZJ, Wen M, Zhu MF, Wang NN, Miao HY et al (2016) BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins. DNAs\/RNAs and their interactions. J Cheminform 8(1):34","journal-title":"J Cheminform."},{"issue":"10","key":"283_CR20","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2012","unstructured":"Pedregosa F, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M et al (2012) Scikit-learn: machine learning in Python. J Mach Learn Res. 12(10):2825\u20132830","journal-title":"J Mach Learn Res."},{"issue":"2","key":"283_CR21","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/MCSE.2011.37","volume":"13","author":"S Walt van der","year":"2011","unstructured":"van der Walt S, Colbert SC, Varoquaux G (2011) The NumPy array: a structure for efficient numerical computation. Comput Sci Eng 13(2):22\u201330","journal-title":"Comput Sci Eng"},{"key":"283_CR22","volume-title":"Python for data analysis: data wrangling with Pandas, NumPy, and IPython","author":"W Mckinney","year":"2017","unstructured":"Mckinney W (2017) Python for data analysis: data wrangling with Pandas, NumPy, and IPython. O\u2019Reilly Media, Inc., Sebastopol"},{"issue":"D1","key":"283_CR23","doi-asserted-by":"publisher","first-page":"D945","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 et al (2017) The ChEMBL database in 2017. Nucleic Acids Res 45(D1):D945\u2013D954","journal-title":"Nucleic Acids Res"},{"key":"283_CR24","unstructured":"EPA. https:\/\/www.epa.gov\/ . Accessed at 2018 Jan 15"},{"issue":"SI","key":"283_CR25","doi-asserted-by":"publisher","first-page":"D668","DOI":"10.1093\/nar\/gkj067","volume":"34","author":"DS Wishart","year":"2006","unstructured":"Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P et al (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res. 34(SI):D668\u2013D672","journal-title":"Nucleic Acids Res."},{"issue":"1","key":"283_CR26","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/s13321-017-0215-1","volume":"9","author":"J Dong","year":"2017","unstructured":"Dong J, Yao ZJ, Zhu MF, Wang NN, Lu B, Chen AF et al (2017) ChemSAR: an online pipelining platform for molecular SAR modeling. J Cheminform 9(1):27","journal-title":"J Cheminform"},{"issue":"1","key":"283_CR27","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"issue":"1","key":"283_CR28","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1002\/cem.1416","volume":"26","author":"D Cao","year":"2012","unstructured":"Cao D, Yang Y, Zhao J, Yan J, Liu S, Hu Q et al (2012) Computer-aided prediction of toxicity with substructure pattern and random forest. J Chemometr 26(1):7\u201315","journal-title":"J Chemometr"},{"issue":"1\u20132","key":"283_CR29","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.aca.2011.02.010","volume":"692","author":"D Cao","year":"2011","unstructured":"Cao D, Hu Q, Xu Q, Yang Y, Zhao J, Lu H et al (2011) In silico classification of human maximum recommended daily dose based on modified random forest and substructure fingerprint. Anal Chim Acta 692(1\u20132):50\u201356","journal-title":"Anal Chim Acta"},{"key":"283_CR30","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1016\/j.chemolab.2015.07.009","volume":"146","author":"D Cao","year":"2015","unstructured":"Cao D, Dong J, Wang N, Wen M, Deng B, Zeng W et al (2015) In silico toxicity prediction of chemicals from EPA toxicity database by kernel fusion-based support vector machines. Chemometr Intell Lab. 146:494\u2013502","journal-title":"Chemometr Intell Lab."},{"issue":"4","key":"283_CR31","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1037\/a0016973","volume":"14","author":"C Strobl","year":"2009","unstructured":"Strobl C, Malley J, Tutz G (2009) An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychol Methods 14(4):323\u2013348","journal-title":"Psychol Methods"},{"issue":"2","key":"283_CR32","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/S0169-7439(01)00155-1","volume":"58","author":"S Wold","year":"2001","unstructured":"Wold S, Sjostrom M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab. 58(2):109\u2013130","journal-title":"Chemometr Intell Lab."},{"issue":"9","key":"283_CR33","doi-asserted-by":"crossref","first-page":"584","DOI":"10.1002\/cem.1321","volume":"24","author":"D Cao","year":"2010","unstructured":"Cao D, Xu Q, Liang Y, Chen X, Li H (2010) Prediction of aqueous solubility of druglike organic compounds using partial least squares, back-propagation network and support vector machine. J Chemometr. 24(9):584\u2013595","journal-title":"J Chemometr."},{"issue":"2","key":"283_CR34","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1002\/ijc.31054","volume":"142","author":"W Jiang","year":"2018","unstructured":"Jiang W, Shen Y, Ding Y, Ye C, Zheng Y, Zhao P et al (2018) A naive Bayes algorithm for tissue origin diagnosis (TOD-Bayes) of synchronous multifocal tumors in the hepatobiliary and pancreatic system. Int J Cancer 142(2):357\u2013368","journal-title":"Int J Cancer"},{"key":"283_CR35","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1016\/j.eswa.2017.10.022","volume":"93","author":"Y Xia","year":"2018","unstructured":"Xia Y, Liu C, Da B, Xie F (2018) A novel heterogeneous ensemble credit scoring model based on bstacking approach. Expert Syst Appl 93:182\u2013199","journal-title":"Expert Syst Appl"},{"issue":"1\u20133","key":"283_CR36","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/S0169-409X(00)00129-0","volume":"46","author":"CA Lipinski","year":"2001","unstructured":"Lipinski CA, Lombardo F, Dominy BW, Feeney PJ (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliver Rev. 46(1\u20133):3\u201326","journal-title":"Adv Drug Deliver Rev."},{"key":"283_CR37","doi-asserted-by":"crossref","unstructured":"Ghose AK, Viswanadhan VN, Wendoloski JJ. A knowledge based approach in designing combinatorial and medicinal chemistry libraries for drug discovery: 1. Qualitative and quantitative definitions of a drug like molecule. In: Abstracts of papers of the American Chemical Society, vol. 217, no. 1; 1999. p. U708.","DOI":"10.1021\/cc9800071"},{"issue":"3","key":"283_CR38","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1023\/A:1008130001697","volume":"14","author":"TI Oprea","year":"2000","unstructured":"Oprea TI (2000) Property distribution of drug-related chemical databases. J Comput Aid Mol Des. 14(3):251\u2013264","journal-title":"J Comput Aid Mol Des."},{"issue":"12","key":"283_CR39","doi-asserted-by":"publisher","first-page":"2615","DOI":"10.1021\/jm020017n","volume":"45","author":"DF Veber","year":"2002","unstructured":"Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45(12):2615\u20132623","journal-title":"J Med Chem"},{"issue":"3","key":"283_CR40","doi-asserted-by":"publisher","first-page":"1098","DOI":"10.1021\/jm901371v","volume":"53","author":"MVS Varma","year":"2010","unstructured":"Varma MVS, Obach RS, Rotter C, Miller HR, Chang G, Steyn SJ et al (2010) Physicochemical space for optimum oral bioavailability: contribution of human intestinal absorption and first-pass elimination. J Med Chem 53(3):1098\u20131108","journal-title":"J Med Chem"},{"key":"283_CR41","unstructured":"Lazar, https:\/\/www.predictive-toxicology.org\/ . Accessed at 2018 Jan 15"},{"issue":"11","key":"283_CR42","doi-asserted-by":"publisher","first-page":"3099","DOI":"10.1021\/ci300367a","volume":"52","author":"F Cheng","year":"2012","unstructured":"Cheng F, Li W, Zhou Y, Shen J, Wu Z, Liu G et al (2012) admetSAR: a comprehensive source and free tool for assessment of chemical ADMET properties. J Chem Inf Model 52(11):3099\u20133105","journal-title":"J Chem Inf Model"},{"key":"283_CR43","unstructured":"PreADMET. https:\/\/preadmet.bmdrc.kr\/ . Accessed at 2018 Jan 15"},{"issue":"22","key":"283_CR44","doi-asserted-by":"publisher","first-page":"3658","DOI":"10.1093\/bioinformatics\/btx491","volume":"33","author":"D Lagorce","year":"2017","unstructured":"Lagorce D, Bouslama L, Becot J, Miteva MA, Villoutreix BO (2017) FAF-Drugs4: free ADME-tox filtering computations for chemical biology and early stages drug discovery. Bioinformatics 33(22):3658\u20133660","journal-title":"Bioinformatics"},{"key":"283_CR45","doi-asserted-by":"publisher","first-page":"42717","DOI":"10.1038\/srep42717","volume":"7","author":"A Daina","year":"2017","unstructured":"Daina A, Michielin O, Zoete V (2017) SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep UK 7:42717","journal-title":"Sci Rep UK"},{"issue":"6","key":"283_CR46","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1007\/s10822-005-8694-y","volume":"19","author":"IV Tetko","year":"2005","unstructured":"Tetko IV, Gasteiger J, Todeschini R, Mauri A, Livingstone D, Ertl P et al (2005) Virtual computational chemistry laboratory - design and description. J Comput Aid Mol Des. 19(6):453\u2013463","journal-title":"J Comput Aid Mol Des."},{"key":"283_CR47","unstructured":"Molinspiration, http:\/\/www.molinspiration.com\/ . Accessed at 2018 Jan 15"},{"key":"283_CR48","doi-asserted-by":"publisher","first-page":"889","DOI":"10.3389\/fphar.2017.00889","volume":"8","author":"P Schyman","year":"2017","unstructured":"Schyman P, Liu R, Desai V et al (2017) vNN web server for ADMET predictions. Front Pharmacol 8:889","journal-title":"Front Pharmacol"}],"container-title":["Journal of Cheminformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-018-0283-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s13321-018-0283-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13321-018-0283-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,7]],"date-time":"2024-07-07T18:30:02Z","timestamp":1720377002000},"score":1,"resource":{"primary":{"URL":"https:\/\/jcheminf.biomedcentral.com\/articles\/10.1186\/s13321-018-0283-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,26]]},"references-count":48,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2018,12]]}},"alternative-id":["283"],"URL":"https:\/\/doi.org\/10.1186\/s13321-018-0283-x","relation":{},"ISSN":["1758-2946"],"issn-type":[{"value":"1758-2946","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,26]]},"assertion":[{"value":"13 February 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2018","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2018","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"29"}}