For LC-HRMS data, two main approaches have been used to detect isotopologues. The first strategy relies on a predicted molecular formula, which can be translated to a predicted isotopic pattern. The main shortcoming of this approach is the difficulties associated with accurate and reliable molecular formula prediction for unknown chemical constituents. The wrong molecular formula could be assigned to a feature either due to instrumental error or absence of a chemical constituent in a database. These wrongly assigned molecular formulas could lead to identifying the potential isotopologues of a feature with the wrong isotopic pattern, resulting in higher false positive and false negative identification rates.