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Explainable uncertainty quantifications for deep learning-based molecular property prediction
Quantifying uncertainty in machine learning is important in new research areas with scarce high-quality data. In this work, we develop an explainable uncertainty quantification method for deep learning-based molecular property prediction. This method can capture aleatoric and epistemic uncertainties...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2023
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9898940/ https://www.ncbi.nlm.nih.gov/pubmed/36737786 http://dx.doi.org/10.1186/s13321-023-00682-3 |