{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T13:53:30Z","timestamp":1726149210254},"reference-count":112,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T00:00:00Z","timestamp":1632700800000},"content-version":"vor","delay-in-days":2,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R35GM133346-01"],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["#1452656"],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,1,17]]},"abstract":"Abstract<\/jats:title>\n The rapid development of machine learning and deep learning algorithms in the recent decade has spurred an outburst of their applications in many research fields. In the chemistry domain, machine learning has been widely used to aid in drug screening, drug toxicity prediction, quantitative structure\u2013activity relationship prediction, anti-cancer synergy score prediction, etc. This review is dedicated to the application of machine learning in drug response prediction. Specifically, we focus on molecular representations, which is a crucial element to the success of drug response prediction and other chemistry-related prediction tasks. We introduce three types of commonly used molecular representation methods, together with their implementation and application examples. This review will serve as a brief introduction of the broad field of molecular representations.<\/jats:p>","DOI":"10.1093\/bib\/bbab393","type":"journal-article","created":{"date-parts":[[2021,9,8]],"date-time":"2021-09-08T11:20:51Z","timestamp":1631100051000},"source":"Crossref","is-referenced-by-count":22,"title":["Representation of molecules for drug response prediction"],"prefix":"10.1093","volume":"23","author":[{"given":"Xin","family":"An","sequence":"first","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, MI, USA"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, MI, USA"}]},{"given":"Daiyao","family":"Yi","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, MI, USA"}]},{"given":"Hongyang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, MI, USA"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8275-2852","authenticated-orcid":false,"given":"Yuanfang","family":"Guan","sequence":"additional","affiliation":[{"name":"Department of Computational Medicine and Bioinformatics, University of Michigan , Ann Arbor, MI, USA"}]}],"member":"286","published-online":{"date-parts":[[2021,9,25]]},"reference":[{"key":"2022092500075618000_ref1","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1006752","article-title":"Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer","volume":"15","author":"Malyutina","year":"2019","journal-title":"PLoS Comput Biol"},{"key":"2022092500075618000_ref2","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1038\/nature11003","article-title":"The 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