Evaluating the generalizability of graph neural networks for predicting collision cross section.

ABSTRACT: Ion Mobility coupled with Mass Spectrometry (IM-MS) is a promising analytical technique that enhances molecular characterization by measuring collision cross-section (CCS) values, which are indicative of the molecular size and shape. However, the effective application of CCS values in structural analysis is still constrained by the limited availability of experimental data, necessitating the development of accurate machine learning (ML) models for in silico predictions. In this study, we evaluated state-of-the-art Graph Neural Networks (GNNs), trained to predict CCS values using the largest publicly available dataset to date. Although our results confirm the high accuracy of these models within chemical spaces similar to their training environments, their performance significantly declines

SUBMITTER: Engler Hart C 

PROVIDER: S-EPMC11363525 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature