| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-07-09 |
| タイトル |
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タイトル |
Bridging Structure- and Ligand-Based Virtual Screening through Fragmented Interaction Fingerprint |
| 言語 |
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言語 |
eng |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Bioactive compounds |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
Ligands |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
Monomers |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Peptides and proteins |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Screening assays |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Syahdi, Rezi Riadhi
Jasial, Swarit
Maeda, Itsuki
宮尾, 知幸
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
Ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS), and their combinations, are frequently conducted in modern drug discovery campaigns. As a form of combination, an amalgamation of methods from ligand- and structure-based information, termed hybrid VS approaches, has been extensively investigated such as using interaction fingerprints (IFPs) in combination with machine learning (ML) models. This approach has the potential to prioritize active compounds in terms of protein$2013ligand binding and ligand structural characteristics, which is assumed to be difficult using either one of the approaches. Herein, we present an IFP, named the fragmented interaction fingerprint (FIFI), for hybrid VS approaches. FIFI is constructed from the extended connectivity fingerprint atom environments of a ligand proximal to the protein residues in the binding site. Each unique ligand substructure within each amino acid residue is encoded as a bit in FIFI while retaining sequence order. From the retrospective evaluation of activity prediction using a limited number and variety of active compounds for six biological targets, FIFI consistently showed higher prediction accuracy than that using previously proposed IFPs. For the same data sets, the screening performance of LBVS, SBVS sequential VS, parallel VS, and other hybrid VS approaches was investigated. Compared to these approaches, FIFI in combination with ML showed overall stable and high prediction accuracy, except for one target: the kappa opioid receptor, where the extended connectivity fingerprint combined with ML models showed better performance than other approaches by wide margins. |
| 書誌情報 |
en : ACS Omega
巻 9,
号 37,
p. 38957-38969,
ページ数 13,
発行日 2024-09-17
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| 出版者 |
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出版者 |
American Chemical Society |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2470-1343 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
DOI |
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関連識別子 |
https://doi.org/10.1021/acsomega.4c05433 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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識別子タイプ |
URI |
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関連識別子 |
https://pubs.acs.org/doi/10.1021/acsomega.4c05433 |
| 権利 |
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|
権利情報Resource |
https://creativecommons.org/licenses/by/4.0/ |
|
権利情報 |
$00A9 2024 The Authors. Published by American Chemical Society. This article is licensed under CC-BY 4.0 |
| 著者版フラグ |
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出版タイプ |
NA |
| 助成情報 |
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助成機関名 |
Ministry of Education, Culture, Sports, Science and Technology (MEXT) |
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研究課題番号 |
21A204 |
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研究課題名 |
デジタル化による高度精密有機合成の新展開 |
