Year: 2019
Type: article
Abstract: Machine learning has been extensively applied in small molecule analysis to predict a wide range of molecular properties and processes including mass spectrometry fragmentation or chromatographic retention time. However, current approaches for retention time prediction lack sufficient accuracy due to limited available experimental data. Here we introduce the METLIN small molecule retention time (SMRT) dataset, an experimentally acquired reverse-phase chromatography retention time dataset covering up to 80,038 small molecules. To demonstrate the utility of this dataset, we deployed a deep learning model for retention time prediction applied to small molecule annotation. Results showed that in 70[Formula: see text] of the cases, the correct molecular identity was ranked among the top 3 candidates based on their predicted retention time. We anticipate that this dataset will enable the community to apply machine learning or first principles strategies to generate better models for retention time prediction.
Source: Nature Communications
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $5,200
Funders: U.S. Department of Energy, National Institutes of Health, Office of Science, National Institute of Mental Health, National Institute on Drug Abuse, National Cancer Institute, National Institute of General Medical Sciences, Biological and Environmental Research, Lawrence Berkeley National Laboratory
Awards: G1079818582, G1143651532, G1286236842, G1337544421, G223002938, G3061798473, G3083819904, G382641700, G419881185, G42916606, G4501827968, G498139845, G574326724, G6652958823, Proteomic Strategies for AIDS and Drug Abuse - HIV and METH CNS Synergy, G7354628648, G7787287669, G9000045395, G9000110986, G9000346257, Metabolomics Technologies to Advance Biomedical Research, G969889393
OpenAlex ID: W2996714860