RT-Transformer: retention time prediction for metabolite annotation to assist in metabolite identification.

ABSTRACT:

Motivation

Liquid chromatography retention times prediction can assist in metabolite identification, which is a critical task and challenge in nontargeted metabolomics. However, different chromatographic conditions may result in different retention times for the same metabolite. Current retention time prediction methods lack sufficient scalability to transfer from one specific chromatographic method to another.

Results

Therefore, we present RT-Transformer, a novel deep neural network model coupled with graph attention network and 1D-Transformer, which can predict retention times under any chromatographic methods. First, we obtain a pre-trained model by training RT-Transformer on the large small molecule retention time dataset containing 80 038 molecules, and then transfer th

SUBMITTER: Xue J 

PROVIDER: S-EPMC10914443 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature