Novel in silico screening system for plant defense activators using deep learning-based prediction of reactive oxygen species accumulation.

Kogoshi, Masayuki; Nishio, Daiki; Kitahata, Nobutaka; Ohwada, Hayato; Kuchitsu, Kazuyuki; Mizuno, Hideyuki; Kurusu, Takamitsu

Kogoshi, Masayuki; Nishio, Daiki; Kitahata, Nobutaka; Ohwada, Hayato; Kuchitsu, Kazuyuki; Mizuno, Hideyuki; Kurusu, Takamitsu.

Afiliación

Kogoshi M; Department of Engineering and Management, Suwa University of Science, 5000-1 Toyohira, Chino, Nagano, 391-0292, Japan. gh21701@ed.sus.ac.jp.
Nishio D; Department of Engineering and Management, Suwa University of Science, 5000-1 Toyohira, Chino, Nagano, 391-0292, Japan.
Kitahata N; Department of Applied Biological Science, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba, 278-8510, Japan.
Ohwada H; Department of Industrial and Systems Engineering, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba, 278-8510, Japan.
Kuchitsu K; Department of Applied Biological Science, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba, 278-8510, Japan.
Mizuno H; Department of Engineering and Management, Suwa University of Science, 5000-1 Toyohira, Chino, Nagano, 391-0292, Japan.
Kurusu T; Department of Engineering and Management, Suwa University of Science, 5000-1 Toyohira, Chino, Nagano, 391-0292, Japan. kurusu@rs.sus.ac.jp.

Plant Methods ; 19(1): 142, 2023 Dec 08.

Article en En | MEDLINE | ID: mdl-38062468

Palabras clave

Chemical property; Deep neural network (DNN); In silico screening; Pesticides; Plant defense activators; Reactive oxygen species (ROS)

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Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Plant Methods Año: 2023 Tipo del documento: Article País de afiliación: Japón

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Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: Plant Methods Año: 2023 Tipo del documento: Article País de afiliación: Japón