Why is high-quality training data for machine learning important?
Machine learning is transforming the way we approach problems in analytical chemistry. But there’s a catch: ensuring reliable results requires careful selection of training data to avoid biases that can mislead models.
We explain:
✨ why high-quality training datasets are important for SIRIUS method development
✨ why representing the full “universe” of small molecules is crucial
✨ how widely used datasets fail to evenly represent the diversity of biomolecular structures
✨ which tools can help evaluating dataset quality.
F. Kretschmer et al. Nat Commun (2025) 10.1038/s41467-024-55462-w
