Pattern Recognition for Protein Crystallisation Strategies (AstraZeneca Crystal Atlas)

阿斯利康水晶图谱将实施深度学习和其他机器学习方法，利用历史和正在进行的结晶实验中的所有数据和知识，深入了解化合物、实验条件和结果之间的复杂关系。这将导致更有效的结晶策略，以加速阿斯利康和制药行业的药物发现。结晶是一个反复试验的过程，科学家们没有实用的工具来轻松地将结晶发生条件及其结果之间的隐藏关系联系起来。在这项提议中，我们的目标是创建阿斯利康晶体图谱，以揭示它们之间的复杂关系，并改进未来的实验，从而加快药物发现进程。阿斯利康晶体图谱是一个综合的人工智能驱动的数据和知识仓库，结合结晶检查图像、结果标签、蛋白质信息和结晶实验条件。通过使用阿斯利康晶体图谱中支持深度学习的新型知识图和其他数据挖掘方法，科学家可以识别优化的路径，揭示实体之间的模式和图形关系，以更好地为结晶策略做出明智的决策，并提高成功率。为了实现这些，我们将首先致力于实施深度学习方法(DL)来自动化结晶图像的注释。阿斯利康结晶学团队积累了大量的历史结晶图像，这些图像没有系统地贴上标签。视觉检测和标记这些图像是一项耗时的工作，可能是主观的和不一致的，因此，我们提出了一种人工智能驱动的自动识别晶体的能力。深度神经网络(DNN)在许多图像识别任务中取得了比人类专家更高的精度。来自谷歌的DNN原型Marco[1]展示了自动结晶图像分类的可行性。尽管如此，我们的评估表明其Page|26Studentship协议在阿斯利康生成的图像上分类精度较差，这表明使用阿斯利康数据集训练更好的DNN模型的重要性。我们使用DenseNet创建迁移学习模型的初步研究取得了比宏观更好的图像分类结果。我们建议在数据训练过程中采用主动学习(AL)[2]，这将获得更好的性能，而数据标记的成本或时间只有一小部分。我们还将利用UV图像中的丰富信息来实现更高的精度。成功的项目将导致阿斯利康晶体图谱，其中包含注释结晶图像、化合物或蛋白质、实验条件以及它们之间的关系知识。使用新的基于图的网络[3，4]，它将是可搜索、可探索和可推断的。阿斯利康晶体图谱将提高结晶成功率和加速药物发现进程，并在i)主动学习、ii)用于晶体图谱探索的多模式深度学习和iii)将在科学期刊上发表的阿斯利康晶体图谱的图形推理方面产生重大影响。布鲁诺，安德鲁·E等人。使用深度卷积神经网络对结晶结果进行分类。PLOS One 13.6(2018)：e0198883.2。周、树森、陈庆才、王晓龙。用于半监督情感分类的主动深度学习方法。神经计算120(2013年)：536-546.3。题名/责任者：A.“使用图卷积网络的半监督分类。”ARXIV：1609.02907，ICLR 2017.4。杨志林，威廉·W·科恩和鲁斯兰·萨拉克胡蒂诺夫。“重温图嵌入的半监督学习。”第33届国际机器学习会议论文集，纽约，美国纽约州，2016年。

AstraZeneca Crystal Atlas will implement deep learning and other machine learning methods to utilise all the data and knowledges from historical and ongoing crystallisation experiments to give insights into the complex relationships between compound, experiment conditions and outcomes. It will lead to more efficient crystallisation strategies for accelerating the drug discovery in AstraZeneca and the pharmaceutical industry.Crystallisation is a trial and error process, scientists do not have practical tools to easily correlate the hidden relationships between crystallogenesis conditions and their outcomes. In this proposal, we aim to create AstraZeneca crystal Atlas to reveal their complex relationships and improve future experiments, thus accelerate the drug discovery process.AstraZeneca Crystal Atlas is a comprehensive AI driven data and knowledge warehouse combing crystallisation inspection images, outcome labels, protein information and crystallogenesis experiment conditions. By using novel deep learning enabled Knowledge Graph and other data mining methods in AstraZeneca Crystal Atlas, scientists can identify the optimised pathway and reveal the patterns and graph relationships between entities to make better informed decisions for crystallisation strategies with high success rates.To achieve these, we will first aim to implement deep learning method (DL) to automate the annotations of crystallisation images. AstraZeneca crystallography team has accumulated a large amount of historical crystallisation images which are not systematically labelled. Visually inspecting and labelling these images is a time-consuming work and can be subjective and inconsistent.Therefore, we propose such an AI driven capability to auto-identify crystals. Deep Neural Networks (DNN) has achieved better accuracies than human experts in many image recognition tasks. MARCO, an DNN prototype from Google [1] shows the feasibility of automated crystallisation image classification. Nonetheless, our evaluation suggested its Page | 26Studentship Agreementpoor classification accuracy on AstraZeneca generated images, which indicates the importance of training a better DNN model using AstraZeneca datasets.Our primary study of creating a transfer learning model using DenseNet has achieved better image classification results than MACRO. We propose to adopt Active Learning (AL) [2] into the data training process, which will achieve better performance with only a fraction of the cost or time for data labelling. We will also utilise the rich information from UV images to achieve higher accuracy.The successful project would lead to an AstraZeneca Crystal Atlas which contains annotated crystallisation images, compounds or proteins, experiment conditions and the knowledge of their relationships. It will be searchable, explorable, and inferable using novel graph-based network [3, 4]. AstraZeneca Crystal Atlas will improve crystallisation success rate and accelerating the drug discovery process, and lead to high impact publications in i) active learning, ii) multiple-modality deep learning for crystallography profile exploration and iii) Graph inference on AstraZeneca Crystal Atlas to be published in scientific journals.Key References1. Bruno, Andrew E., et al. "Classification of crystallization outcomes using deep convolutional neural networks." PLOS one 13.6 (2018): e0198883.2. Zhou, Shusen, Qingcai Chen, and Xiaolong Wang. "Active deep learning method for semi-supervised sentiment classification." Neurocomputing 120 (2013): 536-546.3. Kipf, Thomas N., and Max Welling. "Semi-supervised classification with graph convolutional networks." arXiv:1609.02907, ICLR 2017.4. Yang, Zhilin, William W. Cohen, and Ruslan Salakhutdinov. "Revisiting semi-supervised learning with graph embeddings." Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA, 2016.