DEPS uses an architecture similar to PointNovo, but with several enhancements. Like PointNovo, each spectrum is represented as a set of (m/z, intensity) pairs, processed using a PointNet feature extractor. The extracted features are then processed by a CNN and an LSTM, and the concatenated outputs of those two networks are passed through an output layer to iteratively produce a predicted peptide sequence. However, the CNN incorporates an attention mechanism, inspired by deep residual shrinkage networks , which is designed to more robustly handle noise peaks in the observed spectrum.