Quantitative Structure-Property Relationship Modelling for the Prediction of Singlet Oxygen Generation by Heavy-Atom-Free BODIPY Photosensitizers*.

Buglak, Andrey A; Charisiadis, Asterios; Sheehan, Aimee; Kingsbury, Christopher J; Senge, Mathias O; Filatov, Mikhail A

Buglak, Andrey A; Charisiadis, Asterios; Sheehan, Aimee; Kingsbury, Christopher J; Senge, Mathias O; Filatov, Mikhail A.

Afiliación

Buglak AA; Faculty of Physics, Saint-Petersburg State University, Universiteteskaya Emb. 7-9, 199034, St. Petersburg, Russia.
Charisiadis A; Chair of Organic Chemistry School of Chemistry Trinity Biomedical Sciences Institute, Trinity College Dublin The University of Dublin, 152-160, Pearse Street, Dublin 2, Ireland.
Sheehan A; School of Chemical and Pharmaceutical Sciences, Technological University Dublin, City Campus, Kevin Street, Dublin 8, Ireland.
Kingsbury CJ; Chair of Organic Chemistry School of Chemistry Trinity Biomedical Sciences Institute, Trinity College Dublin The University of Dublin, 152-160, Pearse Street, Dublin 2, Ireland.
Senge MO; Institute for Advanced Study (TUM-IAS), Technical University of Munich, Lichtenberg-Str. 2a, 85748, Garching, Germany.
Filatov MA; School of Chemical and Pharmaceutical Sciences, Technological University Dublin, City Campus, Kevin Street, Dublin 8, Ireland.

Chemistry ; 27(38): 9934-9947, 2021 Jul 07.

Article en En | MEDLINE | ID: mdl-33876842

Palabras clave

BODIPY; machine learning; photosensitization; singlet oxygen; structure-property relationship

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Chemistry Asunto de la revista: QUIMICA Año: 2021 Tipo del documento: Article País de afiliación: Rusia

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