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Pre-training molecular representation model with spatial geometry for property prediction.
Li, Yishui; Wang, Wei; Liu, Jie; Wu, Chengkun.
Afiliación
  • Li Y; Laboratory of Digitizing Software for Frontier Equipment, National University of Defense Technology, Deya Road, Changsha, 410073, China; National Key Laboratory of Parallel and Distributed Computing, National University of Defense Technology, Deya Road, Changsha, 410073, China. Electronic address: l
  • Wang W; National SuperComputer Center in Tianjin, TEDA Sixth Street, Tianjin, 300450, China.
  • Liu J; Laboratory of Digitizing Software for Frontier Equipment, National University of Defense Technology, Deya Road, Changsha, 410073, China; National Key Laboratory of Parallel and Distributed Computing, National University of Defense Technology, Deya Road, Changsha, 410073, China.
  • Wu C; Laboratory of Digitizing Software for Frontier Equipment, National University of Defense Technology, Deya Road, Changsha, 410073, China; National Key Laboratory of Parallel and Distributed Computing, National University of Defense Technology, Deya Road, Changsha, 410073, China. Electronic address: c
Comput Biol Chem ; 109: 108023, 2024 Apr.
Article en En | MEDLINE | ID: mdl-38335852
ABSTRACT
AI-enhanced bioinformatics and cheminformatics pivots on generating increasingly descriptive and generalized molecular representation. Accurate prediction of molecular properties needs a comprehensive description of molecular geometry. We design a novel Graph Isomorphic Network (GIN) based model integrating a three-level network structure with a dual-level pre-training approach that aligns the characteristics of molecules. In our Spatial Molecular Pre-training (SMPT) Model, the network can learn implicit geometric information in layers from lower to higher according to the dimension. Extensive evaluations against established baseline models validate the enhanced efficacy of SMPT, with notable accomplishments in classification tasks. These results emphasize the importance of spatial geometric information in molecular representation modeling and demonstrate the potential of SMPT as a valuable tool for property prediction.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comput Biol Chem Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA / QUIMICA Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Comput Biol Chem Asunto de la revista: BIOLOGIA / INFORMATICA MEDICA / QUIMICA Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido