Critical Assessment of Small Molecule Identification 2016: automated methods.
Emma L SchymanskiChristoph RuttkiesMartin KraussCéline BrouardTobias KindKai DührkopFelicity AllenArpana VaniyaDries VerdegemSebastian BöckerJuho RousuHuibin ShenHiroshi TsugawaTanvir SajedOliver FiehnBart GhesquièreSteffen NeumannPublished in: Journal of cheminformatics (2017)
The improvement in (semi-)automated fragmentation methods for small molecule identification has been substantial. The achieved high rates of correct candidates in the Top 1 and Top 10, despite large candidate numbers, open up great possibilities for high-throughput annotation of untargeted analysis for "known unknowns". As more high quality training data becomes available, the improvements in machine learning methods will likely continue, but the alternative approaches still provide valuable complementary information. Improved integration of experimental context will also improve identification success further for "real life" annotations. The true "unknown unknowns" remain to be evaluated in future CASMI contests. Graphical abstract .