{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T07:43:04Z","timestamp":1776325384876,"version":"3.50.1"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2017,12,18]],"date-time":"2017-12-18T00:00:00Z","timestamp":1513555200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Life Robotics"],"published-print":{"date-parts":[[2018,6]]},"DOI":"10.1007\/s10015-017-0416-8","type":"journal-article","created":{"date-parts":[[2017,12,18]],"date-time":"2017-12-18T05:58:31Z","timestamp":1513576711000},"page":"205-212","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["PKRank: a novel learning-to-rank method for ligand-based virtual screening using pairwise kernel and RankSVM"],"prefix":"10.1007","volume":"23","author":[{"given":"Shogo D.","family":"Suzuki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahito","family":"Ohue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yutaka","family":"Akiyama","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,12,18]]},"reference":[{"key":"416_CR1","first-page":"877","volume":"13","author":"A Mullard","year":"2014","unstructured":"Mullard A (2014) New drugs cost US$2.6 billion to develop. Nat Rev Drug Discov 13:877","journal-title":"Nat Rev Drug Discov"},{"issue":"23","key":"416_CR2","doi-asserted-by":"crossref","first-page":"2839","DOI":"10.2174\/09298673113209990001","volume":"20","author":"A Lavecchia","year":"2013","unstructured":"Lavecchia A, Di Giovanni C (2013) Virtual screening strategies in drug discovery: a critical review. Curr Med Chem 20(23):2839\u20132860","journal-title":"Curr Med Chem"},{"issue":"3","key":"416_CR3","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.drudis.2014.10.012","volume":"20","author":"A Lavecchia","year":"2015","unstructured":"Lavecchia A (2015) Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today 20(3):318\u2013331","journal-title":"Drug Discov Today"},{"key":"416_CR4","volume-title":"Learning to rank for information retrieval","author":"T Liu","year":"2009","unstructured":"Liu T (2009) Learning to rank for information retrieval. Springer, Berlin Heidelberg"},{"issue":"5","key":"416_CR5","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1021\/ci9003865","volume":"50","author":"S Agarwal","year":"2010","unstructured":"Agarwal S, Dugar D, Sengupta S (2010) Ranking chemical structures for drug discovery: a new machine learning approach. J Chem Inf Model 50(5):716\u2013731","journal-title":"J Chem Inf Model"},{"issue":"1","key":"416_CR6","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1021\/ci100308f","volume":"51","author":"F Rathke","year":"2011","unstructured":"Rathke F, Hansen K, Brefeld U, Muller KR (2011) Structrank: a new approach for ligand-based virtual screening. J Chem Inf Model 51(1):83\u201392","journal-title":"J Chem Inf Model"},{"key":"416_CR7","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s13321-015-0052-z","volume":"7","author":"W Zhang","year":"2015","unstructured":"Zhang W, Ji L, Chen Y, Tang K, Wang H, Zhu R, Jia W, Cao Z, Liu Q (2015) When drug discovery meets web search: learning to rank for ligand-based virtual screening. J Cheminform 7:5","journal-title":"J Cheminform"},{"issue":"5","key":"416_CR8","doi-asserted-by":"crossref","first-page":"734","DOI":"10.1093\/bib\/bbt056","volume":"15","author":"H Ding","year":"2014","unstructured":"Ding H, Takigawa I, Mamitsuka H, Zhu S (2014) Similarity-based machine learning methods for predicting drug-target interactions: a brief review. Brief Bioinform 15(5):734\u2013747","journal-title":"Brief Bioinform"},{"issue":"19","key":"416_CR9","doi-asserted-by":"crossref","first-page":"2149","DOI":"10.1093\/bioinformatics\/btn409","volume":"24","author":"L Jacob","year":"2008","unstructured":"Jacob L, Vert JP (2008) Protein-ligand interaction prediction: an improved chemogenomics approach. Bioinformatics 24(19):2149\u20132156","journal-title":"Bioinformatics"},{"issue":"D1","key":"416_CR10","doi-asserted-by":"crossref","first-page":"D1045","DOI":"10.1093\/nar\/gkv1072","volume":"44","author":"MK Gilson","year":"2016","unstructured":"Gilson MK, Liu T, Baitaluk M, Nicola G, Hwang L, Chong J (2016) BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res 44(D1):D1045\u2013D1053","journal-title":"Nucleic Acids Res"},{"issue":"4\u20135","key":"416_CR11","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/S1093-3263(00)00068-1","volume":"18","author":"P Labute","year":"2000","unstructured":"Labute P (2000) A widely applicable set of descriptors. J Mol Graph Model 18(4\u20135):464\u2013477","journal-title":"J Mol Graph Model"},{"issue":"Web Server issu","key":"416_CR12","first-page":"385","volume":"39","author":"HB Rao","year":"2011","unstructured":"Rao HB, Zhu F, Yang GB, Li ZR, Chen YZ (2011) Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence. Nucleic Acids Res 39(Web Server issue):385\u2013390","journal-title":"Nucleic Acids Res"},{"key":"416_CR13","doi-asserted-by":"crossref","first-page":"115","DOI":"10.7551\/mitpress\/1113.003.0010","volume-title":"Advances in large margin classifiers","author":"R Herbrich","year":"2000","unstructured":"Herbrich R, Graepel T, Obermayer K (2000) Large margin rank boundaries for ordinal regression. In: Smola AJ, Bartlett P, Scholkopf B, Schuurmans D (eds) Advances in large margin classifiers. MIT Press, Cambridge, pp 115\u2013132"},{"issue":"5","key":"416_CR14","doi-asserted-by":"crossref","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":"6","key":"416_CR15","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1021\/ci9800211","volume":"38","author":"P Willett","year":"1998","unstructured":"Willett P, Barnard JM, Downs GM (1998) Chemical similarity searching. J Chem Inf Comput Sci 38(6):983\u2013996","journal-title":"J Chem Inf Comput Sci"},{"issue":"1","key":"416_CR16","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/0022-2836(81)90087-5","volume":"147","author":"TF Smith","year":"1981","unstructured":"Smith TF, Waterman MS (1981) Identification of common molecular subsequences. J Mol Biol 147(1):195\u2013197","journal-title":"J Mol Biol"},{"issue":"4","key":"416_CR17","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1021\/ci9803381","volume":"39","author":"D Butina","year":"1999","unstructured":"Butina D (1999) Unsupervised database clustering based on daylight\u2019s fingerprint and Tanimoto similarity: a fast and automated way to cluster small and large datasets. J Chem Inf Comput Sci 39(4):747\u2013750","journal-title":"J Chem Inf Comput Sci"},{"issue":"D1","key":"416_CR18","doi-asserted-by":"crossref","first-page":"D158","DOI":"10.1093\/nar\/gkw1099","volume":"45","author":"The UniProt Consortium","year":"2017","unstructured":"The UniProt Consortium (2017) UniProt: the universal protein knowledgebase. Nucleic Acids Res 45(D1):D158\u2013D169","journal-title":"Nucleic Acids Res"},{"issue":"4","key":"416_CR19","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1145\/582415.582418","volume":"20","author":"K Jarvelin","year":"2002","unstructured":"Jarvelin K, Kekalainen J (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans Inform Syst 20(4):422\u2013446","journal-title":"ACM Trans Inform Syst"},{"key":"416_CR20","doi-asserted-by":"crossref","unstructured":"Kuo T-M, Lee C-P, Lin C-J (2014) Large-scale kernel RankSVM. In: Proceedings of the 2014 SIAM international conference on data mining (SDM14), pp 812\u2013820","DOI":"10.1137\/1.9781611973440.93"},{"key":"416_CR21","unstructured":"RDKit: Open-source cheminformatics; http:\/\/www.rdkit.org . Accessed 13 Nov 2017"},{"issue":"6","key":"416_CR22","doi-asserted-by":"crossref","first-page":"276","DOI":"10.1016\/S0168-9525(00)02024-2","volume":"16","author":"P Rice","year":"2000","unstructured":"Rice P, Longden I, Bleasby A (2000) EMBOSS: the European molecular biology open software suite. Trends Genet 16(6):276\u2013277","journal-title":"Trends Genet"},{"issue":"Suppl 1","key":"416_CR23","doi-asserted-by":"crossref","first-page":"i38","DOI":"10.1093\/bioinformatics\/bti1016","volume":"21","author":"A Ben-Hur","year":"2005","unstructured":"Ben-Hur A, Noble WS (2005) Kernel methods for predicting protein-protein interactions. Bioinformatics 21(Suppl 1):i38\u201346","journal-title":"Bioinformatics"},{"issue":"2","key":"416_CR24","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1093\/bioinformatics\/btm580","volume":"24","author":"JL Faulon","year":"2008","unstructured":"Faulon JL, Misra M, Martin S, Sale K, Sapra R (2008) Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor. Bioinformatics 24(2):225\u2013233","journal-title":"Bioinformatics"},{"key":"416_CR25","doi-asserted-by":"crossref","unstructured":"Oyama S, Manning DC (2004) Using feature conjunctions across examples for learning pairwise classifiers. In: Proceedings of 15th European conference on machine learning (ECML2004), pp 322\u2013333","DOI":"10.1007\/978-3-540-30115-8_31"},{"key":"416_CR26","doi-asserted-by":"crossref","unstructured":"Raymond R, Kashima H (2010) Fast and Scalable algorithms for semi-supervised link prediction on static and dynamic graphs. In: Proceedings of the 2010 European conference on machine learning and knowledge discovery in databases (ECMLPKDD2010), pp 131\u2013147","DOI":"10.1007\/978-3-642-15939-8_9"}],"container-title":["Artificial Life and Robotics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10015-017-0416-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10015-017-0416-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10015-017-0416-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,29]],"date-time":"2024-06-29T20:47:03Z","timestamp":1719694023000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10015-017-0416-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,12,18]]},"references-count":26,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2018,6]]}},"alternative-id":["416"],"URL":"https:\/\/doi.org\/10.1007\/s10015-017-0416-8","relation":{},"ISSN":["1433-5298","1614-7456"],"issn-type":[{"value":"1433-5298","type":"print"},{"value":"1614-7456","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,12,18]]}}}