MOTIVATION: Intramolecular isopeptide bonds contribute to the structural stability of proteins, and have primarily been identified in domains of bacterial fibrillar adhesins and pili. At present, there is no systematic method available to detect them in newly determined molecular structures. This can result in mis-annotations and incorrect modeling. RESULTS: Here, we present Isopeptor, a computational tool designed to predict the presence of intramolecular isopeptide bonds in experimentally determined structures. Isopeptor utilizes structure-guided template matching via the Jess software, combined with a logistic regression classifier that incorporates root mean square deviation and relative solvent accessible area as key features. The tool demonstrates a precision of 1.0 and a recall of 0.947 when tested on a Protein Data Bank subset of domains known to contain intramolecular isopeptide bonds that have been deposited with incorrectly modeled geometries. AVAILABILITY AND IMPLEMENTATION: Isopeptor's Python-based implementation supports integration into bioinformatics workflows and can be accessed via the command line, through a Python API or via a Google Colaboratory implementation (https://colab.research.google.com/github/FranceCosta/Isopeptor_development/blob/main/notebooks/Isopeptide_finder.ipynb). Source code is hosted on GitHub (https://github.com/FranceCosta/isopeptor) and can be installed via the Python package installation manager PIP.