Deep Learning assisted Peak Curation for large scale LC-MS Metabolomics

We are happy to report that we have released our new software **NeatMS**

Available automated methods for peak detection in untargeted metabolomics suffer from poor precision. We present NeatMS which uses machine learning to replace peak curation by human experts. We show how to integrate our open source module into different LC-MS analysis workflows and quantify its performance. NeatMS is designed to be suitable for large scale studies and improves the robustness of the final peak list.

NeatMS is open-source and is freely available at under permissive MIT license. A pypi package is available at, a Bioconda package is available at The user documentation can be found at

Further details can be found in the preprint

Last modified: Mar 21, 2024