{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T16:13:16Z","timestamp":1772208796304,"version":"3.50.1"},"reference-count":5,"publisher":"Oxford University Press (OUP)","issue":"21","license":[{"start":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T00:00:00Z","timestamp":1662595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,10,31]]},"abstract":"Abstract<\/jats:title>\n \n Summary<\/jats:title>\n We present Icolos, a workflow manager written in Python as a tool for automating complex structure-based workflows for drug design. Icolos can be used as a standalone tool, for example in virtual screening campaigns, or can be used in conjunction with deep learning-based molecular generation facilitated for example by REINVENT, a previously published molecular de novo design package. In this publication, we focus on the internal structure and general capabilities of Icolos, using molecular docking experiments as an illustrative example.<\/jats:p>\n <\/jats:sec>\n \n Availability and implementation<\/jats:title>\n The source code is freely available at https:\/\/github.com\/MolecularAI\/Icolos under the Apache 2.0 license. Tutorial notebooks containing minimal working examples can be found at https:\/\/github.com\/MolecularAI\/IcolosCommunity.<\/jats:p>\n <\/jats:sec>\n \n Supplementary information<\/jats:title>\n Supplementary data are available at Bioinformatics online.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac614","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T09:30:05Z","timestamp":1662629405000},"page":"4951-4952","source":"Crossref","is-referenced-by-count":8,"title":["Icolos: a workflow manager for structure-based post-processing of\n de novo<\/i>\n generated small molecules"],"prefix":"10.1093","volume":"38","author":[{"given":"J Harry","family":"Moore","sequence":"first","affiliation":[{"name":"Molecular AI, Discovery Sciences, R&D, AstraZeneca , Gothenburg 431 83, Sweden"}]},{"given":"Matthias R","family":"Bauer","sequence":"additional","affiliation":[{"name":"Structure & Biophysics, Discovery Sciences, R&D, AstraZeneca , Cambridge CB2 8PA, UK"}]},{"given":"Jeff","family":"Guo","sequence":"additional","affiliation":[{"name":"Molecular AI, Discovery Sciences, R&D, AstraZeneca , Gothenburg 431 83, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9797-6573","authenticated-orcid":false,"given":"Atanas","family":"Patronov","sequence":"additional","affiliation":[{"name":"Molecular AI, Discovery Sciences, R&D, AstraZeneca , Gothenburg 431 83, Sweden"}]},{"given":"Ola","family":"Engkvist","sequence":"additional","affiliation":[{"name":"Molecular AI, Discovery Sciences, R&D, AstraZeneca , Gothenburg 431 83, Sweden"},{"name":"Computer Science and Engineering, Chalmers University of Technology , Gothenburg 412 96, Sweden"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5473-6318","authenticated-orcid":false,"given":"Christian","family":"Margreitter","sequence":"additional","affiliation":[{"name":"Molecular AI, Discovery Sciences, R&D, AstraZeneca , Gothenburg 431 83, Sweden"}]}],"member":"286","published-online":{"date-parts":[[2022,9,8]]},"reference":[{"key":"2022103112481385100_btac614-B1","first-page":"58","volume-title":"KNIME\u2014The Konstanz Information Miner. ACM SIGKDD Explorations Newsletter","author":"Berthold","year":"2009"},{"key":"2022103112481385100_btac614-B2","author":"BIOVIA-Dassault Syst\u00e8mes","year":"2022"},{"key":"2022103112481385100_btac614-B3","doi-asserted-by":"crossref","first-page":"3891","DOI":"10.1021\/acs.jcim.1c00203","article-title":"AutoDock vina 1.2.0: new docking methods, expanded force field, and python bindings","volume":"61","author":"Eberhardt","year":"2021","journal-title":"J. Chem. Inf. Model"},{"key":"2022103112481385100_btac614-B4","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper","year":"2021","journal-title":"Nature"},{"key":"2022103112481385100_btac614-B5","author":"Landrum","year":"2022"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btac614\/45947864\/btac614.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/21\/4951\/46697929\/btac614.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/21\/4951\/46697929\/btac614.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,31]],"date-time":"2022-10-31T08:48:36Z","timestamp":1667206116000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/21\/4951\/6694041"}},"subtitle":[],"editor":[{"given":"Alfonso","family":"Valencia","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2022,9,8]]},"references-count":5,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,9,8]]},"published-print":{"date-parts":[[2022,10,31]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac614","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv-2022-sjcp3","asserted-by":"object"}]},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,11,1]]},"published":{"date-parts":[[2022,9,8]]}}}