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Molormer: a lightweight self-attention-based method focused on spatial structure of molecular graph for drug-drug interactions prediction.
Zhang, Xudong; Wang, Gan; Meng, Xiangyu; Wang, Shuang; Zhang, Ying; Rodriguez-Paton, Alfonso; Wang, Jianmin; Wang, Xun.
  • Zhang X; College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China.
  • Wang G; College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China.
  • Meng X; College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China.
  • Wang S; College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China.
  • Zhang Y; College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China.
  • Rodriguez-Paton A; Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, Campus de Montegancedo, Boadilla del Monte 28660, Madrid, Spain.
  • Wang J; The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicin, Yonsei University, Incheon 21983, Korea.
  • Wang X; College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China.
Brief Bioinform ; 23(5)2022 09 20.
Article en En | MEDLINE | ID: mdl-35849817
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2022 Tipo del documento: Article
Texto completo
Imprimir
XML
PubMed Links
Search on Google

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Efectos Colaterales y Reacciones Adversas Relacionados con Medicamentos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Humans Idioma: En Año: 2022 Tipo del documento: Article