Metabolomics has the potential to greatly impact biomedical research in areas such as biomarker discovery and understanding molecular mechanisms of disease. However, compound identification (ID) remains a major challenge in liquid chromatography mass spectrometry-based metabolomics. This is partly due to a lack of specificity in metabolomics databases. Though impressive in depth and breadth, the sheer magnitude of currently available databases is in part what makes them ineffective for many metabolomics studies. While still in pilot phases, our experience suggests that custom-built databases, developed using empirical data from specific sample types, can significantly improve confidence in IDs. While the concept of sample type specific databases (STSDBs) and spectral libraries is not entirely new, inclusion of unique descriptors such as detection frequency and quality scores, can be used to increase confidence in results. These features can be used alone to judge the quality of a database entry, or together to provide filtering capabilities. STSDBs rely on and build upon several available tools for compound ID and are therefore compatible with current compound ID strategies. Overall, STSDBs can potentially result in a new paradigm for translational metabolomics, whereby investigators confidently know the identity of compounds following a simple, single STSDB search.Metabolites 2020, 10, 8 2 of 17 conducted using an LC/MS platform. In addition, while many tools have been developed specifically for plants, natural products, and industrial applications, these are not always applicable for human or translational studies. Therefore, this perspective article focuses on LC/MS-based clinical metabolomics and proposes a strategy to improve identification of compounds in these studies.Identifying compounds in LC/MS studies can be difficult and metabolomics researchers have developed an array of databases, spectral libraries, novel algorithms, and other tools to address the various challenges (for review, please see [8,9]). Generally speaking, compound annotation is conducted using metabolomics databases and/or libraries. Metabolomics databases are searched using mass only or mass plus formula and include descriptors such as chemical and biological information. In contrast, MS/MS spectral libraries include data corresponding to the fragmentation of precursor molecules, whereby experimentally derived spectra are compared to spectra present in the library. Spectral libraries can contain empirical or in silico derived (i.e., computer generated) spectra. Another strategy entails the use of customized databases that may include some MS/MS spectra, including those that focus on specific sample types [10,11]. For the purpose of this perspective article, we are referring to these as sample type specific databases (STSDBs).While the idea of STSDBs is not new, the full potential of STSDBs remains to be determined, in part because they currently exist as simple repositories of data. Our laboratory has recently developed a computationa...