In this chapter, methods of clinical bioinformatics in targeted metabolomics are discussed, with an emphasis on the discovery of metabolic biomarkers. The reader is introduced to general aspects such as initiatives in metabolomics standardization, regulatory guidelines and software validation, and is presented an overview of the bioinformatics workflow in metabolomics. Engineering-based concepts of clinical bioinformatics in supporting the storage and automated analysis of samples, the integration of data in public repositories, and in the management of data using metabolomics application software are discussed. Chemometrics algorithms for data processing are summarized, modalities of biostatistics and data analysis presented, as well as data mining and machine learning approaches, aiming at the discovery of biomarkers in targeted metabolomics. Methods of data interpretation in the context of annotated biochemical pathways are suggested, theoretical concepts of metabolic modeling and engineering are introduced, and the in-silico modeling and simulation of molecular processes is briefly touched. Finally, a short outlook on future perspectives in the application of clinical bioinformatics in targeted metabolomics is given, e.g. on the development of integrated mass spectrometry solutions, ready for routine clinical usage in laboratory medicine, or on the application of concepts of artificial intelligence in laboratory automation – liquid handling robots, autonomously performing experiments and generating hypotheses.