Locally Optimizable Joint Embedding Framework to Design Nitrogen-rich Molecules that are Similar but Improved.

Balakrishnan, Sangeeth; VanGessel, Francis G; Boukouvalas, Zois; Barnes, Brian C; Fuge, Mark D; Chung, Peter W

Balakrishnan, Sangeeth; VanGessel, Francis G; Boukouvalas, Zois; Barnes, Brian C; Fuge, Mark D; Chung, Peter W.

Afiliación

Balakrishnan S; Department of Mechanical Engineering, University of Maryland in College Park, College Park, Maryland, USA.
VanGessel FG; U.S. Naval Surface Warfare Center, Indian Head Division, Indian Head, Maryland, USA.
Boukouvalas Z; Department of Mathematics & Statistics, American University, Washington, DC, USA.
Barnes BC; U.S. Army Combat Capabilities Development, Command Army Research Laboratory, Aberdeen Proving Ground, Maryland, USA.
Fuge MD; Department of Mechanical Engineering, University of Maryland in College Park, College Park, Maryland, USA.
Chung PW; Department of Mechanical Engineering, University of Maryland in College Park, College Park, Maryland, USA.

Mol Inform ; 40(7): e2100011, 2021 07.

Article en En | MEDLINE | ID: mdl-33909951

Asunto(s)

Diseño de Fármacos; Humanos; Nitrógeno

Palabras clave

Chapman-Jouguet velocity; generative modeling; machine learning; molecule optimization; nitrogen-rich molecules

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Diseño de Fármacos Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Mol Inform Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Alemania

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