Summary <p>Single-cell sequencing has revolutionized biomedical research by offering insights into cellular heterogeneity at unprecedented resolution. Yet, the low signal-to-noise ratio characteristic of single-cell RNA sequencing (scRNA-seq) challenges quantitative analyses. Gene regulatory network (GRN) analysis can help overcome this obstacle, enabling the mechanistic elucidation of cellular state determinants. For instance, the VIPER algorithm can identify Master Regulator proteins from gene expression data. However, as the size and complexity of scRNA-seq datasets grow, the demand for scalable tools supporting the analysis of datasets with up to hundreds of thousands of cells becomes increasingly critical in its original implementation in R.</p> Results <p>To address this challenge, we introduce pyVIPER, a Python-based tool for protein activity inference from transcriptional data. pyVIPER supports flexible data transformation/postprocessing modules, enrichment analysis algorithms, and features a novel data structure for GRNs manipulation. It integrates seamlessly with scverse, scanpy and widely adopted machine learning libraries. By leveraging PyTorch-based GPU acceleration and optimized core operations, benchmarking demonstrates orders-of-magnitude improvements in runtime efficiency compared to R-based VIPER, reducing analysis time for large datasets from hours to minutes.</p> Conclusions <p>pyVIPER is a fast, memory-efficient, and highly scalable Python toolkit for protein activity inference in large-scale scRNA-seq datasets. Its scalability and hardware acceleration enables high-throughput VIPER-based analysis of virtually any single-cell dataset while facilitating integration with other Python-based, including state-of-the-art machine learning workflows. Taken together, these features make pyVIPER a valuable resource to expand the applicability of mechanistic regulatory network-based analysis in single-cell research.</p>