{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T06:20:09Z","timestamp":1773382809886,"version":"3.50.1"},"reference-count":60,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2020,6,29]],"date-time":"2020-06-29T00:00:00Z","timestamp":1593388800000},"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":[[2021,5,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Drug similarity studies are driven by the hypothesis that similar drugs should display similar therapeutic actions and thus can potentially treat a similar constellation of diseases. Drug\u2013drug similarity has been derived by variety of direct and indirect sources of evidence and frequently shown high predictive power in discovering validated repositioning candidates as well as other in-silico drug development applications. Yet, existing resources either have limited coverage or rely on an individual source of evidence, overlooking the wealth and diversity of drug-related data sources. Hence, there has been an unmet need for a comprehensive resource integrating diverse drug-related information to derive multi-evidenced drug\u2013drug similarities. We addressed this resource gap by compiling heterogenous information for an exhaustive set of small-molecule drugs (total of 10 367 in the current version) and systematically integrated multiple sources of evidence to derive a multi-modal drug\u2013drug similarity network. The resulting database, \u2018DrugSimDB\u2019 currently includes 238 635 drug pairs with significant aggregated similarity, complemented with an interactive user-friendly web interface (http:\/\/vafaeelab.com\/drugSimDB.html), which not only enables database ease of access, search, filtration and export, but also provides a variety of complementary information on queried drugs and interactions. The integration approach can flexibly incorporate further drug information into the similarity network, providing an easily extendable platform. The database compilation and construction source-code has been well-documented and semi-automated for any-time upgrade to account for new drugs and up-to-date drug information.<\/jats:p>","DOI":"10.1093\/bib\/bbaa126","type":"journal-article","created":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T07:06:44Z","timestamp":1590390404000},"source":"Crossref","is-referenced-by-count":16,"title":["A comprehensive integrated drug similarity resource for\n                    <i>in-silico<\/i>\n                    drug repositioning and beyond"],"prefix":"10.1093","volume":"22","author":[{"given":"A K M","family":"Azad","sequence":"first","affiliation":[{"name":"bioinformatics and computational biology at UNSW Sydney"}]},{"given":"Mojdeh","family":"Dinarvand","sequence":"additional","affiliation":[{"name":"Drug discovery and microbiology at UNSW Sydney"}]},{"given":"Alireza","family":"Nematollahi","sequence":"additional","affiliation":[{"name":"UNSW Sydney, the School of BABS"}]},{"given":"Joshua","family":"Swift","sequence":"additional","affiliation":[{"name":"School of BABS at UNSW Sydney and is the founder of ZiggyLabs"}]},{"given":"Louise","family":"Lutze-Mann","sequence":"additional","affiliation":[{"name":"UNSW Sydney, the School of BABS"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7521-2417","authenticated-orcid":false,"given":"Fatemeh","family":"Vafaee","sequence":"additional","affiliation":[{"name":"University of New South Wales (UNSW Sydney)"}]}],"member":"286","published-online":{"date-parts":[[2020,6,29]]},"reference":[{"issue":"3","key":"2021052110083017200_ref1","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1093\/jamia\/ocw142","article-title":"MeSHDD: literature-based drug-drug similarity for drug repositioning","volume":"24","author":"Brown","year":"2017","journal-title":"J Am Med Inform 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