IsoDec and its neural network approach to charge assignment show promise in addressing some of the challenges with high-resolution deconvolution. However, this initial version has several limitations. The first limitation is the inability to reliably assign high (>30) charge states (Figure ), which stems from the underrepresentation of high charge states in the training data (Figure ). In preliminary testing, balancing the charge states present in the training data with addition of synthetic high-charge clusters dramatically improved accuracy on high charge states (Figure ). Further testing and optimization are needed to examine how this new training strategy impacts overall model performance, but this initial result is encouraging.