ADMET property prediction via multi-task graph learning under adaptive auxiliary task selection

It is a critical step in lead optimization to evaluate the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drug-like compounds. Classical single-task learning (STL) has effectively predicted individual ADMET endpoints with abundant labels. Conversely, multi-task l...

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Detalles Bibliográficos
Autores principales: Du, Bing-Xue, Xu, Yi, Yiu, Siu-Ming, Yu, Hui, Shi, Jian-Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10654589/
https://www.ncbi.nlm.nih.gov/pubmed/38026198
http://dx.doi.org/10.1016/j.isci.2023.108285

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10654589/
https://www.ncbi.nlm.nih.gov/pubmed/38026198
http://dx.doi.org/10.1016/j.isci.2023.108285

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