{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:24:07Z","timestamp":1743114247855,"version":"3.40.3"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030504199"},{"type":"electronic","value":"9783030504205"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-50420-5_44","type":"book-chapter","created":{"date-parts":[[2020,6,19]],"date-time":"2020-06-19T11:04:32Z","timestamp":1592564672000},"page":"585-598","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Quantifying Overfitting Potential in Drug Binding Datasets"],"prefix":"10.1007","author":[{"given":"Brian","family":"Davis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kevin","family":"Mcloughlin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Allen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sally R.","family":"Ellingson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,6,15]]},"reference":[{"key":"44_CR1","unstructured":"RDkit, open-source cheminformatics. http:\/\/www.rdkit.org"},{"issue":"6","key":"44_CR2","doi-asserted-by":"publisher","first-page":"1447","DOI":"10.1021\/ci400115b","volume":"53","author":"MR Bauer","year":"2013","unstructured":"Bauer, M.R., Ibrahim, T.M., Vogel, S.M., Boeckler, F.M.: Evaluation and optimization of virtual screening workflows with DEKOIS 2.0-a public library of challenging docking benchmark sets. J. Chem. Inf. Model. 53(6), 1447\u20131462 (2013)","journal-title":"J. Chem. Inf. Model."},{"key":"44_CR3","unstructured":"Davis, B., Mcloughlin, K., Allen, J., Ellingson, S.: Split optimization for protein\/ligand binding models. arXiv preprint arXiv:2001.03207 (2020)"},{"issue":"6","key":"44_CR4","doi-asserted-by":"publisher","first-page":"129545","DOI":"10.1016\/j.bbagen.2020.129545","volume":"1846","author":"SR Ellingson","year":"2020","unstructured":"Ellingson, S.R., Davis, B., Allen, J.: Machine learning and ligand binding predictions: a review of data, methods, and obstacles. Biochimica et Biophysica Acta (BBA)-Gen. Subj. 1846(6), 129545 (2020)","journal-title":"Biochimica et Biophysica Acta (BBA)-Gen. Subj."},{"key":"44_CR5","first-page":"2171","volume":"13","author":"FA Fortin","year":"2012","unstructured":"Fortin, F.A., De Rainville, F.M., Gardner, M.A., Parizeau, M., Gagn\u00e9, C.: DEAP: evolutionary algorithms made easy. J. Mach. Learn. Res. 13, 2171\u20132175 (2012)","journal-title":"J. Mach. Learn. Res."},{"issue":"D1","key":"44_CR6","doi-asserted-by":"publisher","first-page":"D1045","DOI":"10.1093\/nar\/gkv1072","volume":"44","author":"MK Gilson","year":"2015","unstructured":"Gilson, M.K., Liu, T., Baitaluk, M., Nicola, G., Hwang, L., Chong, J.: BindingDB in 2015: a public database for medicinal chemistry, computational chemistry and systems pharmacology. Nucleic Acids Res. 44(D1), D1045\u2013D1053 (2015)","journal-title":"Nucleic Acids Res."},{"issue":"7","key":"44_CR7","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1021\/ci3001277","volume":"52","author":"JJ Irwin","year":"2012","unstructured":"Irwin, J.J., Sterling, T., Mysinger, M.M., Bolstad, E.S., Coleman, R.G.: Zinc: a free tool to discover chemistry for biology. J. Chem. Inf. Model. 52(7), 1757\u20131768 (2012)","journal-title":"J. Chem. Inf. Model."},{"issue":"7616","key":"44_CR8","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1038\/nature19057","volume":"536","author":"M Lek","year":"2016","unstructured":"Lek, M., et al.: Analysis of protein-coding genetic variation in 60,706 humans. Nature 536(7616), 285 (2016)","journal-title":"Nature"},{"key":"44_CR9","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"9","key":"44_CR10","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1021\/cn3000422","volume":"3","author":"JL Reymond","year":"2012","unstructured":"Reymond, J.L., Awale, M.: Exploring chemical space for drug discovery using the chemical universe database. ACS Chem. Neurosci. 3(9), 649\u2013657 (2012)","journal-title":"ACS Chem. Neurosci."},{"issue":"4","key":"44_CR11","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1021\/ci700099u","volume":"48","author":"SG Rohrer","year":"2008","unstructured":"Rohrer, S.G., Baumann, K.: Impact of benchmark data set topology on the validation of virtual screening methods: exploration and quantification by spatial statistics. J. Chem. Inf. Model. 48(4), 704\u2013718 (2008)","journal-title":"J. Chem. Inf. Model."},{"issue":"2","key":"44_CR12","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":"44_CR13","doi-asserted-by":"crossref","unstructured":"Sundar, V., Colwell, L.: Debiasing algorithms for protein ligand binding data do not improve generalisation (2019)","DOI":"10.26434\/chemrxiv.8139194"},{"issue":"5","key":"44_CR14","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1021\/acs.jcim.7b00403","volume":"58","author":"I Wallach","year":"2018","unstructured":"Wallach, I., Heifets, A.: Most ligand-based classification benchmarks reward memorization rather than generalization. J. Chem. Inf. Model. 58(5), 916\u2013932 (2018)","journal-title":"J. Chem. Inf. Model."}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-50420-5_44","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,18]],"date-time":"2024-06-18T23:22:29Z","timestamp":1718752949000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-50420-5_44"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030504199","9783030504205"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-50420-5_44","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"15 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Amsterdam","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 June 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"230","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"98","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.5","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"248 workshop papers were selected from 489 submissions to the thematic tracks. The conference was canceled due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}