{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T09:31:28Z","timestamp":1787045488379,"version":"build-2736575974"},"reference-count":25,"publisher":"American Chemical Society (ACS)","issue":"7","license":[{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R01-GM069832"],"award-info":[{"award-number":["R01-GM069832"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R24-GM145962"],"award-info":[{"award-number":["R24-GM145962"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,4,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Virtual screening using molecular docking is now routinely used for the rapid evaluation of very large ligand libraries in early stage drug discovery. As the size of compound libraries which can feasibly be screened grows, so do the challenges in result management and storage. Here we introduce Ringtail, a new Python tool in the AutoDock Suite for efficient storage and analysis of virtual screening data based on portable SQLite databases. Ringtail is designed to work with AutoDock-GPU and AutoDock Vina out-of-the-box. Its modular design also allows for easy extension to support input file types from other docking software, different storage solutions, and incorporation into other applications. Ringtail\u2019s SQLite database output can dramatically reduce the required disk storage (36\u201346 fold) by selecting individual poses to store and by taking advantage of the relational database format. Filtering times are also dramatically reduced, requiring minutes to filter millions of ligands. Thus, Ringtail is a tool that can immediately integrate into existing virtual screening pipelines using AutoDock-GPU and Vina, and is scriptable and modifiable to fit specific user needs.<\/jats:p>","DOI":"10.1021\/acs.jcim.3c00166","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T15:24:26Z","timestamp":1680017066000},"page":"1858-1864","source":"Crossref","is-referenced-by-count":1,"title":["Ringtail: A Python Tool for Efficient Management and\nStorage of Virtual Screening Results"],"prefix":"10.1021","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7064-1119","authenticated-orcid":true,"given":"Althea\nT.","family":"Hansel-Harris","sequence":"first","affiliation":[{"name":"Scripps Research , , , ,","place":["La Jolla, California, United States, 92037"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4622-3747","authenticated-orcid":true,"given":"Diogo","family":"Santos-Martins","sequence":"additional","affiliation":[{"name":"Scripps Research , , , ,","place":["La Jolla, California, United States, 92037"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0223-3214","authenticated-orcid":true,"given":"Niccolo\u0300","family":"Bruciaferri","sequence":"additional","affiliation":[{"name":"Scripps Research , , , ,","place":["La Jolla, California, United States, 92037"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1832-3030","authenticated-orcid":true,"given":"Andreas F.","family":"Tillack","sequence":"additional","affiliation":[{"name":"Scripps Research , , , ,","place":["La Jolla, California, United States, 92037"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8409-4344","authenticated-orcid":true,"given":"Matthew","family":"Holcomb","sequence":"additional","affiliation":[{"name":"Scripps Research , , , ,","place":["La Jolla, California, United States, 92037"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5964-7111","authenticated-orcid":true,"given":"Stefano","family":"Forli","sequence":"additional","affiliation":[{"name":"Scripps Research , , , ,","place":["La Jolla, California, United States, 92037"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"key":"2026081804442442400_cit1","doi-asserted-by":"publisher","first-page":"603","DOI":"10.4155\/fmc.12.18","article-title":"Analysis of\nstructure-based virtual\nscreening studies and characterization of identified active compounds","volume":"4","author":"Ripphausen","year":"2012","journal-title":"Future Medicinal Chemistry"},{"key":"2026081804442442400_cit2","doi-asserted-by":"publisher","first-page":"2489","DOI":"10.1021\/acs.jmedchem.0c02227","article-title":"Decision Making\nin Structure-Based Drug Discovery: Visual Inspection of Docking Results","volume":"64","author":"Fischer","year":"2021","journal-title":"J. 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