{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:09:14Z","timestamp":1775560154250,"version":"3.50.1"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"23","license":[{"start":{"date-parts":[[2017,3,29]],"date-time":"2017-03-29T00:00:00Z","timestamp":1490745600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"JSPS","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003382","name":"Core Research for Evolutional Science and Technology","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003382","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002241","name":"Japan Science and Technology Agency","doi-asserted-by":"publisher","award":["JST"],"award-info":[{"award-number":["JST"]}],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>Recently, the number of available protein tertiary structures and compounds has increased. However, structure-based virtual screening is computationally expensive owing to docking simulations. Thus, methods that filter out obviously unnecessary compounds prior to computationally expensive docking simulations have been proposed. However, the calculation speed of these methods is not fast enough to evaluate\u2009\u2265\u200910 million compounds.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>In this article, we propose a novel, docking-based pre-screening protocol named Spresso (Speedy PRE-Screening method with Segmented cOmpounds). Partial structures (fragments) are common among many compounds; therefore, the number of fragment variations needed for evaluation is smaller than that of compounds. Our method increases calculation speeds by \u223c200-fold compared to conventional methods.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and Implementation<\/jats:title><jats:p>Spresso is written in C\u2009++ and Python, and is available as an open-source code (http:\/\/www.bi.cs.titech.ac.jp\/spresso\/) under the GPLv3 license.<\/jats:p><\/jats:sec><jats:sec><jats:title>Supplementary information<\/jats:title><jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx178","type":"journal-article","created":{"date-parts":[[2017,3,28]],"date-time":"2017-03-28T20:07:11Z","timestamp":1490731631000},"page":"3836-3843","source":"Crossref","is-referenced-by-count":15,"title":["Spresso: an ultrafast compound pre-screening method based on compound decomposition"],"prefix":"10.1093","volume":"33","author":[{"given":"Keisuke","family":"Yanagisawa","sequence":"first","affiliation":[{"name":"Department of Computer Science, School of Computing, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan"},{"name":"Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama City, Kanagawa, Japan"}]},{"given":"Shunta","family":"Komine","sequence":"additional","affiliation":[{"name":"Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama City, Kanagawa, Japan"},{"name":"Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan"}]},{"given":"Shogo D","family":"Suzuki","sequence":"additional","affiliation":[{"name":"Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama City, Kanagawa, Japan"},{"name":"Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan"}]},{"given":"Masahito","family":"Ohue","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computing, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan"},{"name":"Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan"},{"name":"Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, Yokohama City, Kanagawa, Japan"}]},{"given":"Takashi","family":"Ishida","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computing, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan"},{"name":"Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama City, Kanagawa, Japan"},{"name":"Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan"},{"name":"Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, Yokohama City, Kanagawa, Japan"}]},{"given":"Yutaka","family":"Akiyama","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Computing, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan"},{"name":"Education Academy of Computational Life Sciences (ACLS), Tokyo Institute of Technology, Yokohama City, Kanagawa, Japan"},{"name":"Department of Computer Science, Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Ookayama, Meguro-ku, Tokyo, Japan"},{"name":"Advanced Computational Drug Discovery Unit (ACDD), Institute of Innovative Research, Tokyo Institute of Technology, Yokohama City, Kanagawa, Japan"}]}],"member":"286","published-online":{"date-parts":[[2017,3,30]]},"reference":[{"key":"2023020207021068500_btx178-B1","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1093\/nar\/gkt1031","article-title":"The ChEMBL bioactivity database: an update","volume":"42","author":"Bento","year":"2014","journal-title":"Nucleic Acids Res"},{"key":"2023020207021068500_btx178-B2","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1208\/s12248-012-9322-0","article-title":"Structure-based virtual screening for drug discovery: a problem-centric review","volume":"14","author":"Cheng","year":"2012","journal-title":"AAPS J"},{"key":"2023020207021068500_btx178-B3","doi-asserted-by":"crossref","first-page":"17209.","DOI":"10.1038\/srep17209","article-title":"Identification of potential inhibitors based on compound proposal contest: tyrosine-protein kinase Yes as a target","volume":"5","author":"Chiba","year":"2015","journal-title":"Sci. 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