Mass spectrometry is an important analytical technology for the identification of metabolites and small compounds by their exact mass. But dozens or hundreds of different compounds may have a similar mass or even the same molecule formula. Further elucidation requires tandem mass spectrometry, which provides the masses of compound fragments, but in silico fragmentation programs require substantial computational resources if applied to large numbers of candidate structures.
We present and evaluate an approach to obtain candidates from a relational database which contains 28 million compounds from PubChem.
A training phase associates tandem-MS peaks with corresponding fragment structures. For the candidate search, the peaks in a query spectrum are translated to fragment structures, and the candidates are retrieved and sorted by the number of matching fragment structures. In the cross validation the evaluation of the relative ranking positions (RRP) using different sizes of training sets confirms that a larger coverage of training data improves the average RRP from 0.65 to 0.72. Our approach allows downstream algorithms to process candidates in order of importance.