A key step in the analysis of many 1D, 2D, and higher dimensional NMR spectra of molecular systems is the accurate identification of peaks. This is often followed by full quantitation of peak positions and peak volumes for the accurate determination of concentrations of molecular components and the extraction of structural dynamic information, for example, from spin relaxation experiments. Current approaches typically require manual intervention that slow down the workflow and may adversely affect the transferability of the results between researchers and research groups. We recently developed the deep neural network “DEEP Picker” for the automated analysis of crowded spectra of proteins and complex mixtures of small molecules. I will demonstrate the performance of DEEP Picker for 1D, 2D, and pseudo-3D data of proteins and metabolomics mixtures and how it can be combined with our “Voigt Fitter” software for full quantitation of peak positions, linewidths, and volumes. We implemented these new tools in the public web server COLMARq for the automated analysis of cohorts of metabolomics samples for metabolite identification, quantitation, and statistical analysis. I will discuss the application of these methodological advances for proteins and metabolomics for the identification of metabolic biomarkers in synovial fluid infected with Pseudomonas aeruginosa, an opportunistic pathogen with the ability to form biofilms that evade immune response and antibiotic treatment.