Instrument-Agnostic Machine Learning Framework for Accurate Prediction of Functional Groups from Tandem Mass Spectrometry

Abstract

Abstract Structural elucidation of unknown compounds using tandem mass spectrometry (MS/MS) is an ongoing challenge. Expert chemists will often use common product ion peaks and neutral losses or peak matching software to predict structural aspects from MS/MS spectra, a process which is time consuming and limited by the molecular entries in the database. We introduce an instrument-agnostic machine learning (ML) framework that predicts functional groups directly from MS/MS spectra, eliminating the need for conventional database searches. The ML models are trained on data from different sources, the MassBank of North America (MONA), a public database, and a custom MS/MS database acquired using a single high throughput desorption electrospray ionization (DESI) MS/MS platform to showcase transferability of model predictions independent of instrument acquisition. Additionally, a novel spectral representation is developed for ML use by standardizing the spectra to the precursor ion m/z. We utilized molecular metrics, specifically, average molecular F1 score of 87% (MONA) and 76% (DESI) and average molecular accuracy of 94% (MONA) and 87% (DESI), indicating similar model performance when trained on different databases to validate the ML framework. To further illustrate the robustness and transferability of our ML framework, models trained on DESI-generated data successfully predicted functional groups, using spectral data generated from a blind test set of molecules using two different company laboratories using distinct mass spectrometry instruments (Orbitrap and TOF) operated in positive-ion mode electrospray ionization. We believe that such an approach, where models trained on data from one instrument can reliably make accurate predictions on spectra acquired using different instruments in a database-free manner, will significantly expand the practical applications of ML to mass spectrometry.

The authors' abstract, as published at the source. Analytical Chemistry, 2026 · DOI ↗