Fig. 1: Overview of the LC-MS²Struct workflow.

a, Input to LC-MS²Struct during the application phase. The LC-MS² experiment results in a set of (MS², RT)-tuples. The MS information is used to generate a molecular candidate set for each MS feature. b, The output of LC-MS²Struct is the ranked molecular candidates for each MS feature. c, A fully connected graph G models the pairwise dependency between the MS features. Using a set of random spanning trees T_k and SSVM, we predict the max-marginal scores for each candidate used for the ranking. d, The MS² and RO information is used to score the nodes and edges in the graph G. e, To train the SSVM models and evaluate LC-MS²Struct, we extract MS² spectra and RTs from MassBank. We group the MassBank records such that their experimental set-ups are matching, simulating LC-MS² experiments. f, Main objective optimized during the SSVM training, where y_i ∈ Σ_i is the ground-truth label sequence of example i and \({{{\bf{y}}}},{{{\bf{y}}}}^{\prime} \in {{{\varSigma }}}_{i}\) are further possible label sequences.