{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:00:04Z","timestamp":1771261204587,"version":"3.50.1"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2020,1,13]],"date-time":"2020-01-13T00:00:00Z","timestamp":1578873600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,13]],"date-time":"2020-01-13T00:00:00Z","timestamp":1578873600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Comput Aided Mol Des"],"published-print":{"date-parts":[[2020,5]]},"DOI":"10.1007\/s10822-020-00279-0","type":"journal-article","created":{"date-parts":[[2020,1,13]],"date-time":"2020-01-13T06:02:41Z","timestamp":1578895361000},"page":"523-534","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A comparison of molecular representations for lipophilicity quantitative structure\u2013property relationships with results from the SAMPL6 logP Prediction Challenge"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4673-9030","authenticated-orcid":false,"given":"Raymond","family":"Lui","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6290-3166","authenticated-orcid":false,"given":"Davy","family":"Guan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1652-543X","authenticated-orcid":false,"given":"Slade","family":"Matthews","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,1,13]]},"reference":[{"issue":"23","key":"279_CR1","doi-asserted-by":"publisher","first-page":"5175","DOI":"10.1021\/ja01077a028","volume":"86","author":"T Fujita","year":"1964","unstructured":"Fujita T, Iwasa J, Hansch C (1964) A new substituent constant, \u03c0, derived from partition coefficients. J Am Chem Soc 86(23):5175\u20135180","journal-title":"J Am Chem Soc"},{"issue":"2","key":"279_CR2","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1021\/jm00326a002","volume":"8","author":"J Iwasa","year":"1965","unstructured":"Iwasa J, Fujita T, Hansch C (1965) Substituent constants for aliphatic functions obtained from partition coefficients. J Med Chem 8(2):150\u2013153","journal-title":"J Med Chem"},{"issue":"3","key":"279_CR3","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1021\/ci960169p","volume":"37","author":"R Wang","year":"1997","unstructured":"Wang R, Fu Y, Lai L (1997) A new atom-additive method for calculating partition coefficients. J Chem Inf Comput Sci 37(3):615\u2013621","journal-title":"J Chem Inf Comput Sci"},{"issue":"1","key":"279_CR4","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1248\/cpb.40.127","volume":"40","author":"I Moriguchi","year":"1992","unstructured":"Moriguchi I et al (1992) Simple method of calculating octanol\/water partition coefficient. Chem Pharm Bull 40(1):127\u2013130","journal-title":"Chem Pharm Bull"},{"issue":"8","key":"279_CR5","doi-asserted-by":"publisher","first-page":"1538","DOI":"10.1016\/j.drudis.2018.05.010","volume":"23","author":"Y-C Lo","year":"2018","unstructured":"Lo Y-C et al (2018) Machine learning in chemoinformatics and drug discovery. Drug Discov Today 23(8):1538\u20131546","journal-title":"Drug Discov Today"},{"issue":"5","key":"279_CR6","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1002\/wcms.1183","volume":"4","author":"JBO Mitchell","year":"2014","unstructured":"Mitchell JBO (2014) Machine learning methods in chemoinformatics. WIREs Comput Mol Sci 4(5):468\u2013481","journal-title":"WIREs Comput Mol Sci"},{"key":"279_CR7","doi-asserted-by":"publisher","first-page":"1997","DOI":"10.1007\/978-3-319-27282-5_50","volume-title":"Handbook of computational chemistry","author":"J Polanski","year":"2017","unstructured":"Polanski J, Gasteiger J (2017) Computer representation of chemical compounds. In: Leszczynski J et al (eds) Handbook of computational chemistry. Springer International Publishing, Cham, pp 1997\u20132039"},{"issue":"1","key":"279_CR8","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1002\/qsar.19910100108","volume":"10","author":"LH Hall","year":"1991","unstructured":"Hall LH, Mohney B, Kier LB (1991) The electrotopological state: an atom index for QSAR. Quant Struct Act Relat 10(1):43\u201351","journal-title":"Quant Struct Act Relat"},{"issue":"8","key":"279_CR9","doi-asserted-by":"publisher","first-page":"801","DOI":"10.1023\/A:1015952613760","volume":"7","author":"LB Kier","year":"1990","unstructured":"Kier LB, Hall LH (1990) An electrotopological-state index for atoms in molecules. Pharm Res 7(8):801\u2013807","journal-title":"Pharm Res"},{"issue":"6","key":"279_CR10","doi-asserted-by":"publisher","first-page":"1039","DOI":"10.1021\/ci00028a014","volume":"35","author":"LH Hall","year":"1995","unstructured":"Hall LH, Kier LB (1995) Electrotopological state indices for atom types: a novel combination of electronic, topological, and valence state information. J Chem Inf Comput Sci 35(6):1039\u20131045","journal-title":"J Chem Inf Comput Sci"},{"issue":"5","key":"279_CR11","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":"7","key":"279_CR12","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1002\/cem.2718","volume":"29","author":"J-B Wang","year":"2015","unstructured":"Wang J-B et al (2015) In silico evaluation of logD7,4 and comparison with other prediction methods. J Chemom 29(7):389\u2013398","journal-title":"J Chemom"},{"issue":"1","key":"279_CR13","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1023\/A:1008763405023","volume":"19","author":"R Wang","year":"2000","unstructured":"Wang R, Gao Y, Lai L (2000) Calculating partition coefficient by atom-additive method. Perspect Drug Discov Des 19(1):47\u201366","journal-title":"Perspect Drug Discov Des"},{"issue":"2","key":"279_CR14","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1111\/j.1747-0285.2009.00840.x","volume":"74","author":"H-F Chen","year":"2009","unstructured":"Chen H-F (2009) In silico log P prediction for a large data set with support vector machines, radial basis neural networks and multiple linear regression. Chem Biol Drug Des 74(2):142\u2013147","journal-title":"Chem Biol Drug Des"},{"key":"279_CR15","doi-asserted-by":"crossref","unstructured":"Lowe EW et al (2011) Comparative analysis of machine learning techniques for the prediction of logP. In: 2011 IEEE symposium on computational intelligence in bioinformatics and computational biology (CIBCB), IEEE, Paris","DOI":"10.1109\/CIBCB.2011.5948478"},{"issue":"1","key":"279_CR16","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1021\/acs.jcim.6b00625","volume":"57","author":"Q Zang","year":"2017","unstructured":"Zang Q et al (2017) In silico prediction of physicochemical properties of environmental chemicals using molecular fingerprints and machine learning. J Chem Inf Model. 57(1):36\u201349","journal-title":"J Chem Inf Model."},{"issue":"7","key":"279_CR17","doi-asserted-by":"publisher","first-page":"1466","DOI":"10.1002\/jcc.21707","volume":"32","author":"CW Yap","year":"2011","unstructured":"Yap CW (2011) PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem 32(7):1466\u20131474","journal-title":"J Comput Chem"},{"key":"279_CR18","doi-asserted-by":"publisher","DOI":"10.1002\/9783527628766","volume-title":"Molecular descriptors for chemoinformatics: volume I: alphabetical listing\/volume II: appendices, references","author":"R Todeschini","year":"2009","unstructured":"Todeschini, R, V Consonni (2009) Molecular descriptors for chemoinformatics: volume I: alphabetical listing\/volume II: appendices, references, vol 41. Wiley, Weinheim"},{"key":"279_CR19","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F et al (2011) Scikit-learn: machine learning in python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"issue":"23","key":"279_CR20","doi-asserted-by":"publisher","first-page":"5571","DOI":"10.1021\/jm0705713","volume":"50","author":"L Peltason","year":"2007","unstructured":"Peltason L (2007) J Bajorath, SAR index: quantifying the nature of structure\u2013activity relationships. J Med Chem 50(23):5571\u20135578","journal-title":"J Med Chem"},{"issue":"3","key":"279_CR21","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1021\/ci7004093","volume":"48","author":"R Guha","year":"2008","unstructured":"Guha R, Van Drie JH (2008) Structure\u2013activity landscape index: identifying and quantifying activity cliffs. J Chem Inf Model 48(3):646\u2013658","journal-title":"J Chem Inf Model"},{"issue":"3","key":"279_CR22","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297\u2013302","journal-title":"Ecology"},{"issue":"1","key":"279_CR23","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1186\/s13321-015-0069-3","volume":"7","author":"D Bajusz","year":"2015","unstructured":"Bajusz D (2015) A R\u00e1cz, K H\u00e9berger, Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations? J Cheminform 7(1):20","journal-title":"J Cheminform"},{"issue":"6","key":"279_CR24","doi-asserted-by":"publisher","first-page":"2140","DOI":"10.1021\/ci700257y","volume":"47","author":"T Cheng","year":"2007","unstructured":"Cheng T et al (2007) Computation of octanol\u2212water partition coefficients by guiding an additive model with knowledge. J Chem Inf Model 47(6):2140\u20132148","journal-title":"J Chem Inf Model"},{"issue":"1","key":"279_CR25","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s13321-018-0263-1","volume":"10","author":"K Mansouri","year":"2018","unstructured":"Mansouri K et al (2018) OPERA models for predicting physicochemical properties and environmental fate endpoints. J Cheminform 10(1):10","journal-title":"J Cheminform"},{"issue":"1\u20132","key":"279_CR26","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1016\/j.ejps.2012.10.019","volume":"48","author":"S Martel","year":"2013","unstructured":"Martel S et al (2013) Large, chemically diverse dataset of logP measurements for benchmarking studies. Eur J Pharm Sci 48(1\u20132):21\u201329","journal-title":"Eur J Pharm Sci"},{"issue":"12","key":"279_CR27","doi-asserted-by":"publisher","first-page":"3284","DOI":"10.1021\/ci500467k","volume":"54","author":"A Daina","year":"2014","unstructured":"Daina A (2014) O Michielin, V Zoete, iLOGP: a simple, robust, and efficient description of n-octanol\/water partition coefficient for drug design using the GB\/SA approach. J Chem Inf Model 54(12):3284\u20133301","journal-title":"J Chem Inf Model"},{"issue":"12","key":"279_CR28","doi-asserted-by":"publisher","first-page":"2361","DOI":"10.1021\/acs.jcim.6b00003","volume":"56","author":"JGEM Fraaije","year":"2016","unstructured":"Fraaije JGEM et al (2016) Coarse-grained models for automated fragmentation and parametrization of molecular databases. J Chem Inf Model 56(12):2361\u20132377","journal-title":"J Chem Inf Model"},{"issue":"8","key":"279_CR29","doi-asserted-by":"publisher","first-page":"1847","DOI":"10.1021\/acs.jcim.7b00315","volume":"57","author":"P Gedeck","year":"2017","unstructured":"Gedeck P (2017) S Skolnik, S Rodde, Developing collaborative QSAR models without sharing structures. J Chem Inf Model 57(8):1847\u20131858","journal-title":"J Chem Inf Model"},{"issue":"1","key":"279_CR30","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1186\/s13321-018-0316-5","volume":"10","author":"J Plante","year":"2018","unstructured":"Plante J (2018) S Werner, JPlogP: an improved logP predictor trained using predicted data. J Cheminform 10(1):61","journal-title":"J Cheminform"},{"key":"279_CR31","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-019-00271-3","author":"M I\u015f\u0131k","year":"2019","unstructured":"I\u015f\u0131k M et al (2019) Octanol-water partition coefficient measurements for the SAMPL6 Blind Prediction Challenge. J Comput Aided Mol Des. https:\/\/doi.org\/10.1007\/s10822-019-00271-3","journal-title":"J Comput Aided Mol Des"},{"issue":"6","key":"279_CR32","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1021\/ci100091e","volume":"50","author":"L Peltason","year":"2010","unstructured":"Peltason L (2010) P Iyer, J Bajorath, Rationalizing three-dimensional activity landscapes and the influence of molecular representations on landscape topology and the formation of activity cliffs. J Chem Inf Model 50(6):1021\u20131033","journal-title":"J Chem Inf Model"},{"issue":"4","key":"279_CR33","doi-asserted-by":"publisher","first-page":"337","DOI":"10.1023\/A:1011107422318","volume":"15","author":"R Mannhold","year":"2001","unstructured":"Mannhold R, van de Waterbeemd H (2001) Substructure and whole molecule approaches for calculating log P J Comput Aided Mol Des 15(4), 337\u2013354.","journal-title":"J Comput Aided Mol Des"},{"issue":"11","key":"279_CR34","doi-asserted-by":"publisher","first-page":"4613","DOI":"10.1021\/acs.jcim.9b00526","volume":"59","author":"AV Zakharov","year":"2019","unstructured":"Zakharov AV et al (2019) Novel consensus architecture to improve performance of large-scale multitask deep learning QSAR models. J Chem Inf Model 59(11):4613\u20134624","journal-title":"J Chem Inf Model"},{"issue":"1","key":"279_CR35","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/s13321-018-0258-y","volume":"10","author":"H Moriwaki","year":"2018","unstructured":"Moriwaki H et al (2018) Mordred: a molecular descriptor calculator. J Cheminform 10(1):4","journal-title":"J Cheminform"},{"issue":"12","key":"279_CR36","doi-asserted-by":"publisher","first-page":"4977","DOI":"10.1021\/jm4004285","volume":"57","author":"A Cherkasov","year":"2014","unstructured":"Cherkasov A et al (2014) QSAR modeling: where have you been? Where are you going to? J Med Chem 57(12):4977\u20135010","journal-title":"J Med Chem"},{"issue":"2","key":"279_CR37","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1039\/C7SC02664A","volume":"9","author":"Z Wu","year":"2018","unstructured":"Wu Z et al (2018) MoleculeNet: a benchmark for molecular machine learning. Chem Sci 9(2):513\u2013530","journal-title":"Chem Sci"},{"issue":"5","key":"279_CR38","doi-asserted-by":"publisher","first-page":"1647","DOI":"10.1021\/ci034255i","volume":"44","author":"P Ti\u00f1o","year":"2004","unstructured":"Ti\u00f1o P et al (2004) Nonlinear prediction of quantitative structure\u2212activity relationships. J Chem Inf Comput Sci 44(5):1647\u20131653","journal-title":"J Chem Inf Comput Sci"},{"key":"279_CR39","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1007\/978-3-030-05318-5_8","volume-title":"Automated machine learning: methods, systems, challenges","author":"RS Olson","year":"2019","unstructured":"Olson RS, Moore JH (2019) TPOT: a tree-based pipeline optimization tool for automating machine learning. In: Hutter F, Kotthoff L, Vanschoren J (eds) Automated machine learning: methods, systems, challenges. Springer, Cham, pp 151\u2013160"}],"container-title":["Journal of Computer-Aided Molecular Design"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-020-00279-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10822-020-00279-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10822-020-00279-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,12]],"date-time":"2021-01-12T21:45:56Z","timestamp":1610487956000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10822-020-00279-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,13]]},"references-count":39,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2020,5]]}},"alternative-id":["279"],"URL":"https:\/\/doi.org\/10.1007\/s10822-020-00279-0","relation":{},"ISSN":["0920-654X","1573-4951"],"issn-type":[{"value":"0920-654X","type":"print"},{"value":"1573-4951","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,13]]},"assertion":[{"value":"16 October 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 January 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}