PhenoProfiler: advancing phenotypic learning for image-based drug discovery

In image-based drug discovery, accurately capturing cellular phenotypic responses to chemical perturbations is crucial for understanding drug mechanisms and predicting efficacy. However, existing approaches often depend on complex, multi-step pipelines that are computationally intensive and prone to error. PhenoProfiler addresses these challenges with an efficient, end-to-end deep learning framework that directly transforms high-content, multi-channel cellular images into low-dimensional quantitative representations. Evaluated on nearly 400,000 high-content images and 8.42 million single-cell images, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20% in both accuracy and robustness. Its tailored phenotype correction strategy further emphasizes treatment-induced variations, improving the detection of biologically meaningful and reproducible signals. PhenoProfiler also effectively clusters treatments with shared molecular pathways and biological annotations, facilitating mechanistic interpretation and target discovery. Collectively, PhenoProfiler establishes a scalable, interpretable, and generalizable framework for high-throughput phenotypic profiling, paving the way for next-generation AI-driven drug screening, precision therapeutics, and systems-level understanding of cellular responses. Image-based drug discovery relies on uncovering how cells respond to treatments. Here, authors introduce PhenoProfiler, an end-to-end AI framework that converts high-content cellular images into quantitative phenotypic profiles, achieving superior accuracy, robustness, and generalisability.