Pattern Recognition for Protein Crystallisation Strategies (AstraZeneca Crystal Atlas)

蛋白质结晶策略的模式识别（阿斯利康 Crystal Atlas）

• 批准号: 2440749
• 金额: --
• 依托单位: University of York
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2440749
• 关键词: Pattern; Recognition; Protein; Crystallisation; Strategies

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.

AstraZeneca Crystal Atlas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，科学家没有实用的工具来轻松地将晶体发生条件与其结果之间的隐藏关系联系起来。AstraZeneca Crystal Atlas是一个由人工智能驱动的综合数据和知识仓库，结合了结晶检测图像、结果标签、蛋白质信息和晶体发生实验条件，旨在通过构建AstraZeneca Crystal Atlas来揭示晶体之间的复杂关系，改善未来的实验，从而加速药物发现过程。通过使用AstraZeneca Crystal Atlas中的新型深度学习支持的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，从而为高成功率的结晶策略做出更明智的决策。为了实现这些目标，我们将首先实施深度学习方法（DL）来自动注释结晶图像。阿斯利康结晶学团队积累了大量未系统标记的历史结晶图像。视觉检查和标记这些图像是一项耗时的工作，并且可能是主观和不一致的。因此，我们提出了这样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好的准确性。MARCO，来自Google的DNN原型[1]显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，|26学生奖学金在AstraZeneca生成的图像上的分类准确性较差，这表明使用AstraZeneca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MACRO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]，这将实现更好的性能，而数据标记的成本或时间只有一小部分。我们还将利用紫外线图像的丰富信息来实现更高的准确性。成功的项目将导致阿斯利康晶体图谱，其中包含注释的结晶图像，化合物或蛋白质，实验条件以及它们之间的关系。它将是可搜索的，可探索的，并使用新的基于图形的网络推断[3，4]。AstraZeneca Crystal Atlas将提高结晶成功率并加速药物发现过程，并在i）主动学习，ii）用于结晶学图谱探索的多模态深度学习和iii）AstraZeneca Crystal Atlas的图形推理方面产生高影响力的出版物，并将在科学期刊上发表。Bruno，Andrew E.，使用深度卷积神经网络分类结晶结果。“PLOS one 13.6（2018）：e0198883.2. Zhou，Shusen，Qingcai Chen，and Xiaolong Wang.“半监督情感分类的主动深度学习方法。“Neurocomputing 120（2013）：536-546.3.基普夫，托马斯N.，还有麦克斯·威林“使用图卷积网络的半监督分类。arXiv：1609.02907，ICLR 2017.4.放大图片作者：William W.科恩和鲁斯兰·萨拉胡迪诺夫。“用图嵌入重新审视半监督学习。“Proceedings of the 33 rd International Conference on Machine Learning，纽约，纽约州，美国，2016.