Cargando…
Multi-task learning with a natural metric for quantitative structure activity relationship learning
The goal of quantitative structure activity relationship (QSAR) learning is to learn a function that, given the structure of a small molecule (a potential drug), outputs the predicted activity of the compound. We employed multi-task learning (MTL) to exploit commonalities in drug targets and assays....
Autores principales: | Sadawi, Noureddin, Olier, Ivan, Vanschoren, Joaquin, van Rijn, Jan N., Besnard, Jeremy, Bickerton, Richard, Grosan, Crina, Soldatova, Larisa, King, Ross D. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6852942/ https://www.ncbi.nlm.nih.gov/pubmed/33430958 http://dx.doi.org/10.1186/s13321-019-0392-1 |
Ejemplares similares
-
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
por: Olier, Ivan, et al.
Publicado: (2017) -
Transformational machine learning: Learning how to learn from many related scientific problems
por: Olier, Ivan, et al.
Publicado: (2021) -
Generating Explainable and Effective Data Descriptors Using Relational Learning: Application to Cancer Biology
por: Orhobor, Oghenejokpeme I., et al.
Publicado: (2020) -
Learning and intelligent optimization: 10th international conference, LION 10, Ischia, Italy, May 29 - June 1, 2016, revised selected papers
por: Festa, Paola, et al.
Publicado: (2016) -
Hybrid evolutionary algorithms
por: Grosan, Crina, et al.
Publicado: (2007)