QSPRs for Molecular Diffusion Coefficients in Polymeric Passive Samplers: A Comparison of Simple Molecular and Quantum‐mechanical Sigma‐moment Descriptors. Issue 8 (7th May 2019)

• Record Type: Journal Article
• Authors: Lampic, Alina M.; Mackay, Donald; Parnis, J. Mark
• Is Part Of: Molecular informatics. Volume 38:Issue 8/9(2019)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-05-07
• Subjects: QSPR -- Diffusion -- Organic Contaminants -- Polymers -- Quantum Chemistry; Cheminformatics -- Periodicals; QSAR (Biochemistry) -- Periodicals; Structure-activity relationships (Biochemistry) -- Periodicals; Drugs -- Structure-activity relationships -- Periodicals; 615.19
• DOI: 10.1002/minf.201800110
• Languages: English
• ISSNs: 1868-1743

Abstract

Abstract: Linear quantitative structure‐property relationships (QSPRs) for the prediction of diffusion coefficients (log Dp ) were developed for organic contaminants in two common passive sampler materials, polydimethylsiloxane (PDMS) and low‐density polyethylene (LDPE). Literature data was compiled for both PDMS and LDPE resulting in final data sets of 196 and 79 compounds, respectively. Data sets contained compounds with log Dp values that ranged over about 5 log units and 3 log units for PDMS and LDPE, respectively. The quality of log Dp prediction using either simple molecular descriptors or quantum‐chemical based COSMO‐RS sigma moment descriptors was compared for both materials. For PDMS, the sigma moment descriptor QSPR had the best predictivity with a correlation coefficient of R 2 =0.85 and root mean square error (RMSE) of 0.36 for log Dp . The molecular descriptor QSPR resulted in a correlation coefficient of R 2 =0.78 and RMSE of 0.45 for log Dp . For LDPE, the molecular descriptor QSPR had the best predictivity, with the final correlation coefficient of R 2 =0.86 and RMSE of 0.21 for log Dp . The sigma moment descriptor QSPR resulted in a correlation coefficient of R 2 =0.66 and RMSE of 0.33 for log Dp . The purely electronic structure‐based sigma moments are therefore shown to be a viable option for descriptors compared to the more commonly used molecular descriptors for organic contaminants in PDMS. The significance of the descriptors in each QSPR is discussed. Abstract :