{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:52:45Z","timestamp":1740099165998,"version":"3.37.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030017675"},{"type":"electronic","value":"9783030017682"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018]]},"DOI":"10.1007\/978-3-030-01768-2_31","type":"book-chapter","created":{"date-parts":[[2018,10,4]],"date-time":"2018-10-04T12:48:08Z","timestamp":1538657288000},"page":"380-391","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learned Feature Generation for Molecules"],"prefix":"10.1007","author":[{"given":"Patrick","family":"Winter","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Christian","family":"Borgelt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael R.","family":"Berthold","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,10,5]]},"reference":[{"key":"31_CR1","unstructured":"ChEMBL. https:\/\/www.ebi.ac.uk\/chembl\/"},{"key":"31_CR2","unstructured":"Deepchem. https:\/\/deepchem.io\/"},{"key":"31_CR3","unstructured":"DUD - A Directory of Useful Decoys. http:\/\/dud.docking.org\/"},{"issue":"7","key":"31_CR4","doi-asserted-by":"publisher","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","volume":"30","author":"AP Bradley","year":"1997","unstructured":"Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 30(7), 1145\u20131159 (1997)","journal-title":"Pattern Recognit."},{"issue":"6604","key":"31_CR5","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1038\/384014a0","volume":"384","author":"JR Broach","year":"1996","unstructured":"Broach, J.R., Thorner, J., et al.: High-throughput screening for drug discovery. Nature 384(6604), 14\u201316 (1996)","journal-title":"Nature"},{"issue":"6","key":"31_CR6","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1021\/ci010132r","volume":"42","author":"JL Durant","year":"2002","unstructured":"Durant, J.L., Leland, B.A., Henry, D.R., Nourse, J.G.: Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comput. Sci. 42(6), 1273\u20131280 (2002)","journal-title":"J. Chem. Inf. Comput. Sci."},{"issue":"D1","key":"31_CR7","doi-asserted-by":"publisher","first-page":"D1100","DOI":"10.1093\/nar\/gkr777","volume":"40","author":"A Gaulton","year":"2011","unstructured":"Gaulton, A., et al.: ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 40(D1), D1100\u2013D1107 (2011)","journal-title":"Nucleic Acids Res."},{"issue":"7","key":"31_CR8","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.1021\/jm030644s","volume":"47","author":"TA Halgren","year":"2004","unstructured":"Halgren, T.A., et al.: Glide: a new approach for rapid, accurate docking and scoring. 2. enrichment factors in database screening. J. Med. Chem. 47(7), 1750\u20131759 (2004)","journal-title":"J. Med. Chem."},{"issue":"3\u20134","key":"31_CR9","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s10822-008-9189-4","volume":"22","author":"JJ Irwin","year":"2008","unstructured":"Irwin, J.J.: Community benchmark for virtual screening. J. Comput.-Aided Mol. Des. 22(3\u20134), 193\u2013199 (2008)","journal-title":"J. Comput.-Aided Mol. Des."},{"issue":"8","key":"31_CR10","doi-asserted-by":"publisher","first-page":"595","DOI":"10.1007\/s10822-016-9938-8","volume":"30","author":"S Kearnes","year":"2016","unstructured":"Kearnes, S., McCloskey, K., Berndl, M., Pande, V., Riley, P.: Molecular graph convolutions: moving beyond fingerprints. J. Comput.-Aided Mol. Des. 30(8), 595\u2013608 (2016)","journal-title":"J. Comput.-Aided Mol. Des."},{"issue":"24","key":"31_CR11","doi-asserted-by":"publisher","first-page":"7315","DOI":"10.1021\/ja00336a004","volume":"106","author":"G Klopman","year":"1984","unstructured":"Klopman, G.: Artificial intelligence approach to structure-activity studies. computer automated structure evaluation of biological activity of organic molecules. J. Am. Chem. Soc. 106(24), 7315\u20137321 (1984)","journal-title":"J. Am. Chem. Soc."},{"key":"31_CR12","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"key":"31_CR13","unstructured":"Landrum, G.A., et al.: RDKit: Open-source cheminformatics. https:\/\/www.rdkit.org\/ (2006)"},{"key":"31_CR14","unstructured":"Le Cun, Y., et al.: Handwritten zip code recognition with multilayer networks. In: Proceedings. 10th International Conference on Pattern Recognition, 1990, vol. 2, pp. 35\u201340. IEEE (1990)"},{"key":"31_CR15","doi-asserted-by":"publisher","first-page":"80","DOI":"10.3389\/fenvs.2015.00080","volume":"3","author":"A Mayr","year":"2016","unstructured":"Mayr, A., Klambauer, G., Unterthiner, T., Hochreiter, S.: Deeptox: toxicity prediction using deep learning. Front. Environ. Sci. 3, 80 (2016)","journal-title":"Front. Environ. Sci."},{"key":"31_CR16","doi-asserted-by":"publisher","first-page":"xi","DOI":"10.1016\/B978-0-12-396549-3.00013-6","volume-title":"Feature Extraction & Image Processing for Computer Vision","author":"Mark S. Nixon","year":"2012","unstructured":"Nixon, M.S., Aguado, A.S.: Feature Extraction & Image Processing for Computer Vision. Academic Press, New York (2012)"},{"key":"31_CR17","unstructured":"Ramsundar, B., Kearnes, S., Riley, P., Webster, D., Konerding, D., Pande, V.: Massively multitask networks for drug discovery. arXiv preprint arXiv:1502.02072 (2015)"},{"issue":"1","key":"31_CR18","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1186\/1758-2946-5-26","volume":"5","author":"S Riniker","year":"2013","unstructured":"Riniker, S., Landrum, G.A.: Open-source platform to benchmark fingerprints for ligand-based virtual screening. J. Cheminformatics 5(1), 26 (2013)","journal-title":"J. Cheminformatics"},{"issue":"5","key":"31_CR19","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1021\/ci100050t","volume":"50","author":"David Rogers","year":"2010","unstructured":"Rogers, D., Hahn, M.: Extended-connectivity fingerprints. J. Chem. Inf. Model. 50(5), 742\u2013754 (2010)","journal-title":"Journal of Chemical Information and Modeling"},{"issue":"2","key":"31_CR20","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1021\/ci8002649","volume":"49","author":"SG Rohrer","year":"2009","unstructured":"Rohrer, S.G., Baumann, K.: Maximum unbiased validation (MUV) data sets for virtual screening based on pubchem bioactivity data. J. Chem. Inf. Model. 49(2), 169\u2013184 (2009)","journal-title":"J. Chem. Inf. Model."},{"key":"31_CR21","unstructured":"Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)"},{"issue":"1","key":"31_CR22","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"31_CR23","unstructured":"Todeschini, R., Consonni, V.: Handbook of Molecular Descriptors, vol. 11. Wiley, New York (2008)"},{"key":"31_CR24","first-page":"1","volume":"27","author":"T Unterthiner","year":"2014","unstructured":"Unterthiner, T., et al.: Deep learning as an opportunity in virtual screening. Proc. Deep Learn. Workshop NIPS 27, 1\u20139 (2014)","journal-title":"Proc. Deep Learn. Workshop NIPS"},{"key":"31_CR25","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"}],"container-title":["Lecture Notes in Computer Science","Advances in Intelligent Data Analysis XVII"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-01768-2_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,10,25]],"date-time":"2019-10-25T07:02:24Z","timestamp":1571986944000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-01768-2_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783030017675","9783030017682"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-01768-2_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"IDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Intelligent Data Analysis","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"\u2018s-Hertogenbosch","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"The Netherlands","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 October 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ida2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ida2018.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}