Training and test data for: Not getting in too deep: A practical deep learning approach to routine crystallisation image classification

These data were used to classify crystallisation experiments in Milne et
al., (https://doi.org/10.1101/2022.09.28.509868). Here, four of the most
widely-used convolutional deep-learning network architectures that can be
implemented without the need for extensive computational resources were
compared. It was shown 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 (Bruno et al. PLOS one, 13(6), 2018). Eight classes
were used to 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.

本数据集曾用于Milne等人（https://doi.org/10.1101/2022.09.28.509868）的结晶实验分类研究。本次工作对比了四种无需大量计算资源即可部署的主流卷积深度学习网络架构。研究证实，各分类器具备差异化性能优势，将其融合可构建集成分类器，其分类准确率可与大型联盟项目（Bruno等，《公共科学图书馆·综合》，2018年，第13卷第6期）所取得的结果相媲美。本次研究通过八个类别对实验结果进行分级，所得详细信息可应用于常规晶体学实验，自动识别药物研发中的晶体生成情况，并为进一步探究晶体生成与结晶条件之间的关联铺平道路。