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dc.contributor.authorNagayasu, Kazukien
dc.contributor.alternative永安, 一樹ja
dc.date.accessioned2023-06-07T23:49:44Z-
dc.date.available2023-06-07T23:49:44Z-
dc.date.issued2022-03-
dc.identifier.urihttp://hdl.handle.net/2433/283248-
dc.description.abstractSerotonin transporter (SERT) is a membrane transporter which terminates neurotransmission of serotonin through its reuptake. This transporter as well as its substrate have long drawn attention as a key mediator and drug target in a variety of diseases including mental disorders. Accordingly, its structural basis has been studied by X-ray crystallography to gain insights into a design of ligand with high affinity and high specificity over closely related transporters. Recent progress in structural biology including single particle cryo-EM have made big strides also in determination of the structures of human SERT in complex with its ligands. Moreover, rapid progress in machine learning such as deep learning accelerates computer-assisted drug design. Here, we would like to summarize recent progresses in our understanding of SERT using these two rapidly growing technologies, limitations, and future perspectives.en
dc.language.isoeng-
dc.publisherElsevier BVen
dc.rights© 2022 The Authors. Production and hosting by Elsevier B.V. on behalf of Japanese Pharmacological Society.en
dc.rightsThis is an open access article under the CC BY-NC-ND license.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/-
dc.subjectSerotonin transporteren
dc.subjectMachine learningen
dc.subjectVirtual screeningen
dc.subjectMajor depressive disorderen
dc.titleSerotonin transporter: Recent progress of in silico ligand prediction methods and structural biology towards structure-guided in silico design of therapeutic agentsen
dc.typejournal article-
dc.type.niitypeJournal Article-
dc.identifier.jtitleJournal of Pharmacological Sciencesen
dc.identifier.volume148-
dc.identifier.issue3-
dc.identifier.spage295-
dc.identifier.epage299-
dc.relation.doi10.1016/j.jphs.2022.01.004-
dc.textversionpublisher-
dc.identifier.pmid35177208-
dcterms.accessRightsopen access-
datacite.awardNumber20H04774-
datacite.awardNumber20K07064-
datacite.awardNumber.urihttps://kaken.nii.ac.jp/grant/KAKENHI-PUBLICLY-20H04774/-
datacite.awardNumber.urihttps://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20K07064/-
dc.identifier.pissn1347-8613-
dc.identifier.eissn1347-8648-
jpcoar.funderName日本学術振興会ja
jpcoar.funderName日本学術振興会ja
jpcoar.awardTitle低分子から中分子に至るあらゆる化学構造のヒト作用予測モデルの開発ja
jpcoar.awardTitleうつ病態発症・治療の決定因子の同定ja
出現コレクション:学術雑誌掲載論文等

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