Drug discovery programs are moving increasingly toward phenotypic imaging assays to model disease-relevant pathways and phenotypes in vitro. These assays offer richer information than target-optimized assays by investigating multiple cellular pathways simultaneously and producing multiplexed readouts. However, extracting the desired information from complex image data poses significant challenges, preventing broad adoption of more sophisticated phenotypic assays. Deep learning-based image analysis can address these challenges by reducing the effort required to analyze large volumes of complex image data at a quality and speed adequate for routine phenotypic screening in pharmaceutical research. However, while general purpose deep learning frameworks are readily available, they are not readily applicable to images from automated microscopy. During the past 3 years, we have optimized deep learning networks for this type of data and validated the approach across diverse assays with several industry partners. From this work, we have extracted five essential design principles that we believe should guide deep learning-based analysis of high-content images and multiparameter data: (1) insightful data representation, (2) automation of training, (3) multilevel quality control, (4) knowledge embedding and transfer to new assays, and (5) enterprise integration. We report a new deep learning-based software that embodies these principles, Genedata Imagence, which allows screening scientists to reliably detect stable endpoints for primary drug response, assess toxicity and safety-relevant effects, and discover new phenotypes and compound classes. Furthermore, we show how the software retains expert knowledge from its training on a particular assay and successfully reapplies it to different, novel assays in an automated fashion.

中文翻译：

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面向企业的基于深度学习的 HCS 图像分析。

药物发现计划正越来越多地转向表型成像分析，以在体外模拟疾病相关途径和表型。通过同时研究多个细胞通路并产生多重读数，这些检测提供了比目标优化检测更丰富的信息。然而，从复杂的图像数据中提取所需的信息带来了巨大的挑战，阻碍了更复杂的表型分析的广泛采用。基于深度学习的图像分析可以通过减少以足以满足药物研究中常规表型筛选的质量和速度分析大量复杂图像数据所需的工作量来应对这些挑战。然而，虽然通用深度学习框架很容易获得，但它们并不容易适用于来自自动显微镜的图像。在过去的 3 年中，我们针对此类数据优化了深度学习网络，并与多个行业合作伙伴在不同的分析中验证了该方法。从这项工作中，我们提取了五个基本设计原则，我们认为这些原则应该指导基于深度学习的高内容图像和多参数数据分析：（1）有洞察力的数据表示，（2）训练自动化，（3）多级质量控制，（4）知识嵌入和转移到新的分析，以及（5）企业整合。我们报告了一种新的基于深度学习的软件，它体现了这些原则，Genedata Imagence，它使筛选科学家能够可靠地检测主要药物反应的稳定终点，评估毒性和安全相关效应，并发现新的表型和化合物类别。此外，