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A Fast and Interpretable Deep Learning Approach for Accurate Electrostatics-Driven pK(a) Predictions in Proteins
[Image: see text] Existing computational methods for estimating pK(a) values in proteins rely on theoretical approximations and lengthy computations. In this work, we use a data set of 6 million theoretically determined pK(a) shifts to train deep learning models, which are shown to rival the physics...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
American Chemical Society
2022
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9369009/ https://www.ncbi.nlm.nih.gov/pubmed/35837736 http://dx.doi.org/10.1021/acs.jctc.2c00308 |