Automation to Enable High-Throughput Chemical Proteomics.

Lin, Zongtao; Gongora, Joanna; Liu, Xingyu; Xie, Yixuan; Zhao, Chenfeng; Lv, Dongwen; Garcia, Benjamin A

Lin, Zongtao; Gongora, Joanna; Liu, Xingyu; Xie, Yixuan; Zhao, Chenfeng; Lv, Dongwen; Garcia, Benjamin A.

Afiliación

Lin Z; Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, Missouri 63110, United States.
Gongora J; Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States.
Liu X; Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, Missouri 63110, United States.
Xie Y; Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, Missouri 63110, United States.
Zhao C; Department of Biochemistry and Molecular Biophysics, Washington University in St. Louis, St. Louis, Missouri 63110, United States.
Lv D; McKelvey School of Engineering, Washington University in St. Louis, St. Louis, Missouri 63110, United States.
Garcia BA; Department of Biochemistry and Structural Biology and Center for Innovative Drug Discovery, School of Medicine, University of Texas Health Science Center at San Antonio, San Antonio, Texas 78229, United States.

J Proteome Res ; 22(12): 3676-3682, 2023 12 01.

Article en En | MEDLINE | ID: mdl-37917986

Asunto(s)

Inteligencia Artificial; Proteómica; Automatización; Proteínas/química; Descubrimiento de Drogas

Palabras clave

artificial intelligence; automation; chemical proteomics; high-throughput; robotics; sample preparation

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Proteómica Idioma: En Revista: J Proteome Res Asunto de la revista: BIOQUIMICA Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

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