GeoT: A Geometry-Aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning

[Image: see text] In recent years, molecular representation learning has emerged as a key area of focus in various chemical tasks. However, many existing models fail to fully consider the geometric information on molecular structures, resulting in less intuitive representations. Moreover, the widely...

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Detalles Bibliográficos
Autores principales: Kwak, Bumju, Park, Jiwon, Kang, Taewon, Jo, Jeonghee, Lee, Byunghan, Yoon, Sungroh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601421/
https://www.ncbi.nlm.nih.gov/pubmed/37901490
http://dx.doi.org/10.1021/acsomega.3c05753

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10601421/
https://www.ncbi.nlm.nih.gov/pubmed/37901490
http://dx.doi.org/10.1021/acsomega.3c05753

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