Convolutional neural network prediction of molecular properties for aerosol chemistry and health effects

Publication type:
Journal article
Metadata:
Autoren
  • Matteo Krueger
  • Jake Wilson
  • Marco Wietzoreck
  • Benjamin A Musa Bandowe
  • Gerhard Lammel
  • Bertil Schmidt
  • Ulrich Poeschl
  • Thomas Berkemeier
Autoren-URL
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=fis-test-1&SrcAuth=WosAPI&KeyUT=WOS:001122262400003&DestLinkType=FullRecord&DestApp=WOS_CPL
DOI
10.1002/ntls.20220016
Externe Identifier
  • Clarivate Analytics Document Solution ID: AZ4U0
ISSN
2698-6248
Ausgabe der Veröffentlichung
4
Zeitschrift
NATURAL SCIENCES
Schlüsselwörter
  • quinone
  • redox chemistry
  • environmental chemistry
  • redox potential
  • oxidative potential
  • convolutional neural network
  • machine learning
  • QSAR
Artikelnummer
ARTN e20220016
Datum der Veröffentlichung
2022
Status
Published
Titel
Convolutional neural network prediction of molecular properties for aerosol chemistry and health effects
Sub types
  • Article
Ausgabe der Zeitschrift
2

Data source: Web of Science (Lite)

Other metadata sources:
Abstract
<jats:title>Abstract</jats:title><jats:sec><jats:label /><jats:p>Quinones are chemical compounds commonly found in air particulate matter (PM). Their redox activity can generate reactive oxygen species (ROS) and contribute to the oxidative potential (OP) of PM leading to adverse health effects of aerosols. The quinones' OP and ability to form ROS are linked to their reduction potential (RP, measured in volts), a metric for the tendency to lose electrons in redox reactions. Here, we use convolutional neural networks (CNN) as quantitative structure‐activity relationship (QSAR) models to relate the one‐electron RP of quinones to their molecular structure. For CNN training and testing, a data set of more than 100,000 quinones with associated RP values derived from density functional theory calculations was encoded in simplified molecular input line entry system (SMILES). The best performing CNN model achieved a root mean square error (RMSE) of 0.115 V for an independent test data set and outperformed linear regression models fitted on common molecular descriptors (≥ 0.140 V RMSE). Augmentation methods were newly adapted or applied to support CNN training with smaller data sets, improving RMSE by up to approximately 37% for a data set of 321 molecules. Adjusted for solvent effects, the CNN‐derived RP predictions showed good agreement with experimental data. Using the newly developed method, we identified a subset of atmospherically relevant quinones that are likely to have a high OP and play a role in aerosol health effects, which remains to be further elucidated by experimental studies. We suggest to use the presented machine learning approach in further investigations of atmospheric aerosol chemistry and health effects as well as other studies that require a target‐oriented screening of the properties and effects of large classes of substances.</jats:p></jats:sec><jats:sec><jats:title>Key Points</jats:title><jats:p><jats:list list-type="explicit-label"> <jats:list-item><jats:p>Convolutional neural networks can be used to estimate unknown physical and chemical properties of chemical substances and outperform additive group contribution methods.</jats:p></jats:list-item> <jats:list-item><jats:p>Augmentation methods aid in the prevalent problem of data availability.</jats:p></jats:list-item> <jats:list-item><jats:p>Quinone species detected in the environment are screened for potential relevance in atmospheric chemistry and public health and presented in this study.</jats:p></jats:list-item> </jats:list></jats:p></jats:sec>
Autoren
  • Matteo Krüger
  • Jake Wilson
  • Marco Wietzoreck
  • Benjamin A Musa Bandowe
  • Gerhard Lammel
  • Bertil Schmidt
  • Ulrich Pöschl
  • Thomas Berkemeier
DOI
10.1002/ntls.20220016
eISSN
2698-6248
ISSN
2698-6248
Ausgabe der Veröffentlichung
4
Zeitschrift
Natural Sciences
Sprache
en
Online publication date
2022
Datum der Veröffentlichung
2022
Status
Published
Herausgeber
Wiley
Herausgeber URL
http://dx.doi.org/10.1002/ntls.20220016
Datum der Datenerfassung
2023
Titel
Convolutional neural network prediction of molecular properties for aerosol chemistry and health effects
Ausgabe der Zeitschrift
2

Data source: Crossref

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