Moving the needle: Employing deep reinforcement learning to push the boundaries of coarse-grained vaccine models.

Faris, Jonathan G; Orbidan, Daniel; Wells, Charles; Petersen, Brenden K; Sprenger, Kayla G

Faris, Jonathan G; Orbidan, Daniel; Wells, Charles; Petersen, Brenden K; Sprenger, Kayla G.

Afiliación

Faris JG; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, United States.
Orbidan D; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, United States.
Wells C; Department of Computer Science, Rice University, TX, Houston, United States.
Petersen BK; Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, CA, United States.
Sprenger KG; Department of Chemical and Biological Engineering, University of Colorado Boulder, Boulder, CO, United States.

Front Immunol ; 13: 1029167, 2022.

Article en En | MEDLINE | ID: mdl-36405722

Asunto(s)

Vacunas contra el SIDA; Infecciones por VIH; VIH-1; Humanos; Anticuerpos Anti-VIH; Anticuerpos Neutralizantes; Anticuerpos ampliamente neutralizantes; Infecciones por VIH/prevención & control

Palabras clave

HIV - human immunodeficiency virus; affinity maturation; agent-based modelling; deep reinforcement learning (Deep RL); immunovirology; multiscale (MS) modelling; vaccine design protocol

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Infecciones por VIH / VIH-1 / Vacunas contra el SIDA Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Front Immunol Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza

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