This is a methodological guide to the use of deep neural networks in the processing of double electron-electron resonance (DEER, aka PELDOR) data encountered in structural biology, organic photovoltaics, photosynthesis research, and other domains featuring long-lived radical pairs. DEER spectroscopy uses distance dependence of magnetic dipolar interactions; measuring a single well-defined distance is straightforward, but extracting distance distributions is a hard and mathematically ill-posed problem requiring careful regularisation and background fitting. Neural networks do this exceptionally well, but their robust black box reputation hides the complexity of their design and training - particularly when the training dataset is effectively infinite. The objective of this paper is to give insight into training against infinite databases, to shed some light on the processes inside the neural net, and to provide a practical data processing flowchart for structural biology work.
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