View in EDS HTML Full Text PDF Full Text

Integrating deep learning and molecular dynamics simulations for FXR antagonist discovery.

Saved in:
Bibliographic Details
Title: Integrating deep learning and molecular dynamics simulations for FXR antagonist discovery.
Authors: Yang Y; Institute of Pharmaceutical Innovation, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065, China., Huang Y; Institute of Pharmaceutical Innovation, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065, China., Shen H; Institute of Pharmaceutical Innovation, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065, China., Wang D; Institute of Pharmaceutical Innovation, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065, China., Liu Z; School of Chemical Engineering, East China University of Science and Technology, Shanghai, 200237, China., Zhu W; SINOPEC-SK (Wuhan) Petrochemical Co., Ltd, Wuhan, 430082, China. zhuwei.zhsh@sinopec.com., Liu Q; Institute of Pharmaceutical Innovation, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065, China. lqing@wust.edu.cn.; Institute of Cardiovascular Diseases, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065, China. lqing@wust.edu.cn.; State Key Laboratory of Natural Medicines, Jiangsu Key Laboratory of Drug Discovery for Metabolic Diseases, Center of Drug Discovery, China Pharmaceutical University, Nanjing, 210009, China. lqing@wust.edu.cn.
Source: Molecular diversity [Mol Divers] 2025 Aug; Vol. 29 (4), pp. 3391-3409. Date of Electronic Publication: 2025 Apr 02.
Publication Type: Journal Article
Journal Info: Publisher: ESCOM Science Publishers Country of Publication: Netherlands NLM ID: 9516534 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1573-501X (Electronic) Linking ISSN: 13811991 NLM ISO Abbreviation: Mol Divers Subsets: MEDLINE
Database: MEDLINE Ultimate
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: mdl
DbLabel: MEDLINE Ultimate
An: 40172823
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Integrating deep learning and molecular dynamics simulations for FXR antagonist discovery.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AU" term="%22Yang+Y%22">Yang Y</searchLink>; Institute of Pharmaceutical Innovation, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065, China.<br /><searchLink fieldCode="AU" term="%22Huang+Y%22">Huang Y</searchLink>; Institute of Pharmaceutical Innovation, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065, China.<br /><searchLink fieldCode="AU" term="%22Shen+H%22">Shen H</searchLink>; Institute of Pharmaceutical Innovation, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065, China.<br /><searchLink fieldCode="AU" term="%22Wang+D%22">Wang D</searchLink>; Institute of Pharmaceutical Innovation, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065, China.<br /><searchLink fieldCode="AU" term="%22Liu+Z%22">Liu Z</searchLink>; School of Chemical Engineering, East China University of Science and Technology, Shanghai, 200237, China.<br /><searchLink fieldCode="AU" term="%22Zhu+W%22">Zhu W</searchLink>; SINOPEC-SK (Wuhan) Petrochemical Co., Ltd, Wuhan, 430082, China. zhuwei.zhsh@sinopec.com.<br /><searchLink fieldCode="AU" term="%22Liu+Q%22">Liu Q</searchLink>; Institute of Pharmaceutical Innovation, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065, China. lqing@wust.edu.cn.; Institute of Cardiovascular Diseases, Hubei Province Key Laboratory of Occupational Hazard Identification and Control, School of Medicine, Wuhan University of Science and Technology, Wuhan, 430065, China. lqing@wust.edu.cn.; State Key Laboratory of Natural Medicines, Jiangsu Key Laboratory of Drug Discovery for Metabolic Diseases, Center of Drug Discovery, China Pharmaceutical University, Nanjing, 210009, China. lqing@wust.edu.cn.
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%229516534%22">Molecular diversity</searchLink> [Mol Divers] 2025 Aug; Vol. 29 (4), pp. 3391-3409. <i>Date of Electronic Publication: </i>2025 Apr 02.
– Name: TypePub
  Label: Publication Type
  Group: TypPub
  Data: Journal Article
– Name: TitleSource
  Label: Journal Info
  Group: Src
  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22ESCOM+Science+Publishers%22">ESCOM Science Publishers </searchLink><i>Country of Publication: </i>Netherlands <i>NLM ID: </i>9516534 <i>Publication Model: </i>Print-Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>1573-501X (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2213811991%22">13811991 </searchLink><i>NLM ISO Abbreviation: </i>Mol Divers <i>Subsets: </i>MEDLINE
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=mdl&AN=40172823
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s11030-025-11145-2
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        StartPage: 3391
    Titles:
      – TitleFull: Integrating deep learning and molecular dynamics simulations for FXR antagonist discovery.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Yang Y
      – PersonEntity:
          Name:
            NameFull: Huang Y
      – PersonEntity:
          Name:
            NameFull: Shen H
      – PersonEntity:
          Name:
            NameFull: Wang D
      – PersonEntity:
          Name:
            NameFull: Liu Z
      – PersonEntity:
          Name:
            NameFull: Zhu W
      – PersonEntity:
          Name:
            NameFull: Liu Q
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 08
              Text: 2025 Aug
              Type: published
              Y: 2025
          Identifiers:
            – Type: issn-electronic
              Value: 1573-501X
          Numbering:
            – Type: volume
              Value: 29
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Molecular diversity
              Type: main
ResultId 1