Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data

We present LC-MS 2 Struct, a machine learning framework for structural annotation of small molecule data arising from liquid chromatography-tandem mass spectrometry (LC-MS 2 ) measurements. LC-MS 2 Struct jointly predicts the annotations for a set of mass spectrometry features in a sample, using a novel structured prediction model trained to optimally combine the output of state-of-the-art MS 2 scorers and observed retention orders. We evaluate our method on a dataset covering all publicly available reversed phase LC-MS 2 data in the MassBank reference database, including 4327 molecules measured using 18 different LC conditions from 16 contributors, greatly expanding the chemical analytical space covered in previous multi-MS 2 scorer evaluations. LC-MS 2 Struct obtains significantly higher annotation accuracy than earlier methods and improves the annotation accuracy of state-of-the-art MS 2 scorers by up to 106%. The use of stereochemistry-aware molecular fingerprints improves prediction performance, which highlights limitations in existing approaches and has strong implications for future computational LC-MS 2 developments.