Forecasting vaping health risks through neural network model prediction of flavour pyrolysis reactions
Vaping involves the heating of chemical solutions (e-liquids) to high temperatures prior to lung inhalation. A risk exists that these chemicals undergo thermal decomposition to new chemical entities, the composition and health implications of which are largely unknown. To address this concern, a graph-convolutional neural network (NN) model was used to predict pyrolysis reactivity of 180 e-liquid chemical flavours. The output of this supervised machine learning approach was a dataset of probability ranked pyrolysis transformations and their associated 7307 products. To refine this dataset, the molecular weight of each NN predicted product was automatically correlated with experimental mass spectrometry (MS) fragmentation data for each flavour chemical. This blending of deep learning methods with experimental MS data identified 1169 molecular weight matches that prioritized these compounds for further analysis. The average number of discrete matches per flavour between NN predictions and MS fragmentation was 6.4 with 92.8% of flavours having at least one match. Globally harmonized system classifications for NN/MS matches were extracted from PubChem, revealing that 127 acute toxic, 153 health hazard and 225 irritant classifications were predicted. This approach may reveal the longer-term health risks of vaping in advance of clinical diseases emerging in the general population.
Funding
Synthesis and Solid State Pharmaceutical Centre (SSPC)
Science foundation Ireland for funding support, Grant Number 12/RC/2275_P2
History
Data Availability Statement
All data are available in the main text, Supplementary Information or GitHub (https://github.com/IBM/pyrolysis-prediction). Raw data files are available from the corresponding author upon request.Comments
The original article is available at https://www.nature.com/Published Citation
Kishimoto A, Wu D, O’Shea DF. Forecasting vaping health risks through neural network model prediction of flavour pyrolysis reactions. Sci Rep 2024;14.9591Publication Date
8 May 2024External DOI
Department/Unit
- Chemistry
Publisher
Springer Nature LimitedVersion
- Published Version (Version of Record)