{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:34:11Z","timestamp":1772138051937,"version":"3.50.1"},"reference-count":53,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T00:00:00Z","timestamp":1649203200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"name":"National Institutes of Health's National Institute on Aging","award":["R01 AG066750"],"award-info":[{"award-number":["R01 AG066750"]}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["OT2 TR003427"],"award-info":[{"award-number":["OT2 TR003427"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,5,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>Drug repositioning is an attractive alternative to de novo drug discovery due to reduced time and costs to bring drugs to market. Computational repositioning methods, particularly non-black-box methods that can account for and predict a drug\u2019s mechanism, may provide great benefit for directing future development. By tuning both data and algorithm to utilize relationships important to drug mechanisms, a computational repositioning algorithm can be trained to both predict and explain mechanistically novel indications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In this work, we examined the 123 curated drug mechanism paths found in the drug mechanism database (DrugMechDB) and after identifying the most important relationships, we integrated 18 data sources to produce a heterogeneous knowledge graph, MechRepoNet, capable of capturing the information in these paths. We applied the Rephetio repurposing algorithm to MechRepoNet using only a subset of relationships known to be mechanistic in nature and found adequate predictive ability on an evaluation set with AUROC value of 0.83. The resulting repurposing model allowed us to prioritize paths in our knowledge graph to produce a predicted treatment mechanism. We found that DrugMechDB paths, when present in the network were rated highly among predicted mechanisms. We then demonstrated MechRepoNet\u2019s ability to use mechanistic insight to identify a drug\u2019s mechanistic target, with a mean reciprocal rank of 0.525 on a test set of known drug\u2013target interactions. Finally, we walked through repurposing examples of the anti-cancer drug imatinib for use in the treatment of asthma, and metolazone for use in the treatment of osteoporosis, to demonstrate this method\u2019s utility in providing mechanistic insight into repurposing predictions it provides.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The Python code to reproduce the entirety of this analysis is available at: https:\/\/github.com\/SuLab\/MechRepoNet (archived at https:\/\/doi.org\/10.5281\/zenodo.6456335).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac205","type":"journal-article","created":{"date-parts":[[2022,4,6]],"date-time":"2022-04-06T09:43:07Z","timestamp":1649238187000},"page":"2880-2891","source":"Crossref","is-referenced-by-count":18,"title":["Design and application of a knowledge network for automatic prioritization of drug mechanisms"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7792-0150","authenticated-orcid":false,"given":"Michael","family":"Mayers","sequence":"first","affiliation":[{"name":"Department of Structural and Computational Biology, The Scripps Research Institute , La Jolla, CA 92037, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7899-1604","authenticated-orcid":false,"given":"Roger","family":"Tu","sequence":"additional","affiliation":[{"name":"Department of Structural and Computational Biology, The Scripps Research Institute , La Jolla, CA 92037, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0469-0868","authenticated-orcid":false,"given":"Dylan","family":"Steinecke","sequence":"additional","affiliation":[{"name":"Department of Structural and Computational Biology, The Scripps Research Institute , La Jolla, CA 92037, USA"}]},{"given":"Tong Shu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Structural and Computational Biology, The Scripps Research Institute , La Jolla, CA 92037, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0169-8159","authenticated-orcid":false,"given":"N\u00faria","family":"Queralt-Rosinach","sequence":"additional","affiliation":[{"name":"Department of Structural and Computational Biology, The Scripps Research Institute , La Jolla, CA 92037, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9859-4104","authenticated-orcid":false,"given":"Andrew I","family":"Su","sequence":"additional","affiliation":[{"name":"Department of Structural and Computational Biology, The Scripps Research Institute , La Jolla, CA 92037, USA"}]}],"member":"286","published-online":{"date-parts":[[2022,4,6]]},"reference":[{"key":"2023020109140430200_btac205-B1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.rmed.2011.09.007","article-title":"The role of mast cells in allergic inflammation","volume":"106","author":"Amin","year":"2012","journal-title":"Respir. 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