Accurate prediction of protein antigenicity is crucial for vaccine development, diagnostic test design, and therapeutic protein engineering. However, existing tools face limitations in accessibility, computational effi ciency, and pathogen diversity. Here, we present IApred, an open-source intrinsic antigenicity predictor that addresses these challenges. IApred employs a Support Vector Machine (SVM) model trained on a comprehensive dataset of 918 high-antigenicity proteins from diverse pathogens, including Gram-positive and Gram-negative bacteria, viruses, fungi, protozoa, and helminths. The model incorporates features derived from physicochemical properties, E-descriptors, amino acid dimers and small linear motifs (SLiMs) to predict the probability of a protein eliciting a humoral immune response. In external validation, IApred demonstrated superior balanced performance (ROC AUC = 0.761, sensitivity = 0.702, specificity = 0.706) compared to existing tools (VaxiJen 2.0, VaxiJen 3.0 and ANTIGENpro), while maintaining high computational efficiency (approximately 1000 se quences per minute). IApred’s host-and-pathogen-agnostic nature and integration capability into bioinformatic pipelines makes it versatile for diverse applications. A web-based version of the software is available at https:// smilesinformatics.com/iapred, while the software and training code are freely available on GitHub (https://gith ub.com/sebamiles/IAPred) and Zenodo (https://doi.org/10.5281/zenodo.14578279).