Abstract
The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups. Here, state-of-the-art machine learning algorithms are trained and tested on different parts of this data set. We find that more than 94% of the test images can be correctly labeled, irrespective of their experimental origin. Because crystal recognition is key to high-density screening and the systematic analysis of crystallization experiments, this approach opens the door to both industrial and fundamental research applications.
Publication types
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Research Support, U.S. Gov't, Non-P.H.S.
MeSH terms
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Algorithms
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Crystallization*
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Crystallography, X-Ray*
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Datasets as Topic
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Image Processing, Computer-Assisted*
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Neural Networks, Computer*
Grants and funding
PC acknowledges support from National Science Foundation Grant no. NSF DMR-1749374. VV and DRS are employed by Google Inc., and SPW is employed by GlaxoSmithKline Inc. These funders provided support in the form of salaries for authors VV, DRS, and SPW, respectively, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. Additionally, GlaxoSmithKline provided data for the study which is freely available from the MARCO site (
https://marco.ccr.buffalo.edu/).