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j.jphs.2022.01.004.pdf | 536.31 kB | Adobe PDF | 見る/開く |
完全メタデータレコード
DCフィールド | 値 | 言語 |
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dc.contributor.author | Nagayasu, Kazuki | en |
dc.contributor.alternative | 永安, 一樹 | ja |
dc.date.accessioned | 2023-06-07T23:49:44Z | - |
dc.date.available | 2023-06-07T23:49:44Z | - |
dc.date.issued | 2022-03 | - |
dc.identifier.uri | http://hdl.handle.net/2433/283248 | - |
dc.description.abstract | Serotonin 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.iso | eng | - |
dc.publisher | Elsevier BV | en |
dc.rights | © 2022 The Authors. Production and hosting by Elsevier B.V. on behalf of Japanese Pharmacological Society. | en |
dc.rights | This is an open access article under the CC BY-NC-ND license. | en |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.subject | Serotonin transporter | en |
dc.subject | Machine learning | en |
dc.subject | Virtual screening | en |
dc.subject | Major depressive disorder | en |
dc.title | Serotonin transporter: Recent progress of in silico ligand prediction methods and structural biology towards structure-guided in silico design of therapeutic agents | en |
dc.type | journal article | - |
dc.type.niitype | Journal Article | - |
dc.identifier.jtitle | Journal of Pharmacological Sciences | en |
dc.identifier.volume | 148 | - |
dc.identifier.issue | 3 | - |
dc.identifier.spage | 295 | - |
dc.identifier.epage | 299 | - |
dc.relation.doi | 10.1016/j.jphs.2022.01.004 | - |
dc.textversion | publisher | - |
dc.identifier.pmid | 35177208 | - |
dcterms.accessRights | open access | - |
datacite.awardNumber | 20H04774 | - |
datacite.awardNumber | 20K07064 | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PUBLICLY-20H04774/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20K07064/ | - |
dc.identifier.pissn | 1347-8613 | - |
dc.identifier.eissn | 1347-8648 | - |
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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