Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias
Machine learning algorithms offer new possibilities for automating reaction procedures. The present paper investigates automated reaction’s prediction with Molecular Transformer, the state-of-the-art model for reaction prediction, proposing a new debiased dataset for a realistic assessment of the mo...
Guardado en:
Autores principales: | Dávid Péter Kovács, William McCorkindale, Alpha A. Lee |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/f5f5a1b384fd4609aa785325aa34d776 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Machine learning in chemical reaction space
por: Sina Stocker, et al.
Publicado: (2020) -
Symbolic AI for XAI: Evaluating LFIT Inductive Programming for Explaining Biases in Machine Learning
por: Alfonso Ortega, et al.
Publicado: (2021) -
Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning
por: Taiki Fujimoto, et al.
Publicado: (2021) -
Modeling and interpreting hydrological responses of sustainable urban drainage systems with explainable machine learning methods
por: Y. Yang, et al.
Publicado: (2021) -
Augmenting zero-Kelvin quantum mechanics with machine learning for the prediction of chemical reactions at high temperatures
por: Jose Antonio Garrido Torres, et al.
Publicado: (2021)