The METLIN small molecule dataset for machine learning-based retention time prediction

• Published in: Nature communications 2019-12, Vol.10 (1), p.5811-9, Article 5811
• Main Authors: Domingo-Almenara, Xavier, Guijas, Carlos, Billings, Elizabeth, Montenegro-Burke, J. Rafael, Uritboonthai, Winnie, Aisporna, Aries E., Chen, Emily, Benton, H. Paul, Siuzdak, Gary
• Format: Article
• Language: English
• Subjects: 119/118; 140/58; 49/98; 639/638/11/296; 639/638/630; 639/705/1046; Animal behavior; Annotations; Artificial intelligence; BASIC BIOLOGICAL SCIENCES; Chromatography, Reverse-Phase; Datasets; Datasets as Topic; Deep Learning; First principles; Humanities and Social Sciences; Learning algorithms; Machine learning; Mass Spectrometry; Mass spectroscopy; Models, Chemical; multidisciplinary; Predictions; Retention; Retention time; Science; Science & Technology - Other Topics; Science (multidisciplinary); Small Molecule Libraries - chemistry; Small Molecule Libraries - isolation & purification; Time Factors
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-13680-7

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 % 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. The use of machine learning for identifying small molecules through their retention time’s predictions has been challenging so far. Here the authors combine a large database of liquid chromatography retention time with a deep learning approach to enable accurate metabolites’s identification.