Not getting in too deep: a practical deep learning approach to routine crystallisation image classification

Research output: Contribution to conferencePaperpeer-review


Using a relatively small training set of ˜16 thousand images from macromolecular
crystallisation experiments, we compare classification results obtained with four of the
most widely-used convolutional deep-learning network architectures that can be
implemented without the need for extensive computational resources. We show that the classifiers have different strengths that can be combined to provide an ensemble
classifier achieving a classification accuracy comparable to that obtained by a large
consortium initiative. We use eight classes to effectively rank the experimental
outcomes, thereby providing detailed information that can be used with routine
crystallography experiments to automatically identify crystal formation for drug
discovery and pave the way for further exploration of the relationship between crystal formation and crystallisation conditions.
Original languageEnglish
Publication statusPublished - Feb 2023

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