AI-Assisted Molecular Structure Prediction from Tandem Mass Spectra: Reliability and Validation

Artificial intelligence (AI) has broadened the interpretation of tandem mass spectra (MS/MS) beyond experimental library matching, enabling molecular fingerprint prediction, candidate ranking, and generation of previously unlisted structures. Yet these outputs do not all support the same claim about chemical identity. This review briefly summarizes database-restricted retrieval, database-free generation, and scaffold-guided hybrid methods, and then asks what additional evidence is needed to turn a plausible structure into a defensible annotation. We examine failure modes involving incomplete candidate spaces, erroneous precursor assignments, structurally related isomers, leakage between training and test data, and misinterpreted confidence scores. The review contributes two practical elements: a candidate-discrimination workflow, in which alternative structures are retained and targeted measurements such as multienergy fragmentation, retention behavior, ion mobility, and authentic standards are selected according to the unresolved structural question; and an evidence-to-claim checklist that links each question to the most informative measurement and to the strongest supportable identification confidence level. Both synthesize established analytical principles for small-molecule electrospray LC–HRMS/MS rather than constituting a validated scoring algorithm. By making the basis of each annotation transparent, they are intended to improve the practical value of AI-assisted MS/MS in metabolomics, pharmaceutical analysis, environmental screening, and forensic toxicology.