What is it about?

To visualize a new molecule requires the comparison of many X-ray diffraction measurements that differ only a tiny amount. By taking advantage of the correlation between these measurements the initial picture of the molecule improves even if the quality of measurements are not optimal. This work also suggests which is the best method for collecting the data in the first place.

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Why is it important?

The initial picture of a molecule is the most unbiased view one can get by X-ray diffraction. Later the expertise of the investigator can strongly influence the outcome. By providing an improved initial picture even less experienced researchers and computer programs can interpret the picture correctly and place the atoms of the molecule in the correct positions. This is important for placing drug molecules correctly in their target and understanding how the molecules function.


It was important for me to test this purely statistical idea on real data where one can determine confidently if an improvement occurred or not. That way one start to trust the method more and be ready to apply it to more challenging situations where the expected outcome is unknown.

Professor Gergely Katona
University of Gothenburg

Read the Original

This page is a summary of: Bayesian machine learning improves single-wavelength anomalous diffraction phasing, Acta Crystallographica Section A Foundations and Advances, October 2019, International Union of Crystallography,
DOI: 10.1107/s2053273319011446.
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