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Atomic partial charge predictions for furanoses by random forest regression with atom type symmetry function
Furanoses that are components for many important biomolecules have complicated conformational spaces due to the flexible ring and exo-cyclic moieties. Machine learning algorithms, which require descriptors as structural inputs, can be used to efficiently compute conformational adaptive (CA) charges...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
The Royal Society of Chemistry
2020
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9048215/ https://www.ncbi.nlm.nih.gov/pubmed/35494472 http://dx.doi.org/10.1039/c9ra09337k |