DeepMRM is a targeted proteomics data interpretation tool that utilizes deep learning algorithms, specifically designed to substantially reduce the manual inspection burden, even for noisy and complex data. DeepMRM reframes the task as an object detection problem, detecting peak group instances within 1-D chromatograms, similar to traditional object detection models that identify instances of a specific class within a 2-D image. It accepts MRM, PRM, or Data-Independent Acquisition (DIA) data and a target list as input and provides quantification results and confidence scores.