AIRI: Predicting Retention Indices and Their Uncertainties Using Artificial Intelligence

Published

January 17, 2024

Author(s)

Lewis Geer, Stephen E. Stein, Gary Mallard, Douglas Slotta

Abstract

The Kováts retention index (RI) is a quantity measured using gas chromatography and is commonly used in the identification of chemical structures. Creating libraries of observed RI values is a laborious task, so we explore the use of a deep neural network for predicting RI values from structure for standard semipolar columns. This network generated predictions with a mean absolute error of 15.1 and, in a quantification of the tail of the error distribution, a 95th percentile absolute error of 46.5. Because of the Artificial Intelligence Retention Indices (AIRI) network's accuracy, it was used to predict RI values for the NIST EI-MS spectral libraries. These RI values are used to improve chemical identification methods and the quality of the library. Estimating uncertainty is an important practical need when using prediction models. To quantify the uncertainty of our network for each individual prediction, we used the outputs of an ensemble of 8 networks to calculate a predicted standard deviation for each RI value prediction. This predicted standard deviation was corrected to follow the error between the observed and predicted RI values. The Z scores using these predicted standard deviations had a standard deviation of 1.52 and a 95th percentile absolute Z score corresponding to a mean RI value of 42.6.

Citation

Journal of Chemical Information and Modeling

Pub Type

Journals

Download Paper

https://doi.org/10.1021/acs.jcim.3c01758

Local Download

Keywords

machine learning, retention index, uncertainty

Citation

Geer, L. , Stein, S. , Mallard, G. and Slotta, D. (2024), AIRI: Predicting Retention Indices and Their Uncertainties Using Artificial Intelligence, Journal of Chemical Information and Modeling, [online], https://doi.org/10.1021/acs.jcim.3c01758, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=956868 (Accessed April 27, 2024)