GeneralizedDTA: combining pre-training and multi-task learning to predict drug-target binding affinity for unknown drug discovery

BACKGROUND: Accurately predicting drug-target binding affinity (DTA) in silico plays an important role in drug discovery. Most of the computational methods developed for predicting DTA use machine learning models, especially deep neural networks, and depend on large-scale labelled data. However, it...

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Detalles Bibliográficos
Autores principales: Lin, Shaofu, Shi, Chengyu, Chen, Jianhui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9449940/
https://www.ncbi.nlm.nih.gov/pubmed/36071406
http://dx.doi.org/10.1186/s12859-022-04905-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9449940/
https://www.ncbi.nlm.nih.gov/pubmed/36071406
http://dx.doi.org/10.1186/s12859-022-04905-6

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