科学者たちが機械学習を用いて低分子化合物の前人未到の知見を獲得(Scientists use machine learning to gain unprecedented view of small molecules)

低分子を同定する新しいツールは、診断、創薬、基礎研究などに役立つ。 A new tool to identify small molecules offers benefits for diagnostics, drug discovery and fundamental research.

2022-12-20 フィンランド・アールト大学

<関連情報>

• https://www.aalto.fi/en/news/scientists-use-machine-learning-to-gain-unprecedented-view-of-small-molecules
• https://www.nature.com/articles/s42256-022-00577-2

液体クロマトグラフィーの保持順とタンデム質量分析データを用いた低分子化合物の共同構造アノテーション Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data

Eric Bach,Emma L. Schymanski &Juho Rousu

Nature Machine Intelligence  Published:19 December 2022

DOI:https://doi.org/10.1038/s42256-022-00577-2

Abstract

Structural annotation of small molecules in biological samples remains a key bottleneck in untargeted metabolomics, despite rapid progress in predictive methods and tools during the past decade. Liquid chromatography–tandem mass spectrometry, one of the most widely used analysis platforms, can detect thousands of molecules in a sample, the vast majority of which remain unidentified even with best-of-class methods. Here we present LC-MS²Struct, a machine learning framework for structural annotation of small-molecule data arising from liquid chromatography–tandem mass spectrometry (LC-MS²) measurements. LC-MS²Struct 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 MS² scorers and observed retention orders. We evaluate our method on a dataset covering all publicly available reversed-phase LC-MS² data in the MassBank reference database, including 4,327 molecules measured using 18 different LC conditions from 16 contributors, greatly expanding the chemical analytical space covered in previous multi-MS² scorer evaluations. LC-MS²Struct obtains significantly higher annotation accuracy than earlier methods and improves the annotation accuracy of state-of-the-art MS² 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-MS² developments.