While target-based drug discovery strategies rely on the precise knowledge of the identity and function of the drug targets, phenotypic drug discovery (PDD) approaches allow the identification of novel drugs based on knowledge of a distinct phenotype. Image-based high-content screening (HCS) is a potent PDD strategy that characterizes small-molecule effects through the quantification of features that depict cellular changes among or within cell populations, thereby generating valuable data sets for subsequent data analysis. However, these data can be complex, making image analysis from large HCS campaigns challenging. Technological advances in image acquisition, processing, and analysis as well as machine-learning (ML) approaches for the analysis of multidimensional data sets have rendered HCS as a viable technology for small-molecule drug discovery. Here, we discuss HCS concepts, current workflows as well as opportunities and challenges of image-based phenotypic screening and data analysis.

虽然基于靶标的药物发现策略依赖于对药物靶标的身份和功能的精确了解，但表型药物发现 (PDD) 方法允许基于对不同表型的了解来识别新药物。基于图像的高内涵筛选 (HCS) 是一种有效的 PDD 策略，它通过量化描述细胞群之间或细胞内变化的特征来表征小分子效应，从而为后续数据分析生成有价值的数据集。然而，这些数据可能很复杂，使得大型 HCS 活动的图像分析具有挑战性。图像采集、处理、分析以及用于分析多维数据集的机器学习 (ML) 方法使 HCS 成为一种可行的小分子药物发现技术。在这里，我们讨论 HCS 概念、当前工作流程以及基于图像的表型筛选和数据分析的机遇和挑战。