Publicly available QSPR models for environmental media persistence

• Published in: SAR and QSAR in environmental research 2020-07, Vol.31 (7), p.493-510
• Main Authors: Lunghini, F., Marcou, G., Azam, P., Enrici, M.H., Van Miert, E., Varnek, A.
• Format: Article
• Language: English
• Subjects: Bayesian analysis; Bioaccumulation; Chemical Sciences; Cheminformatics; Data analysis; Data collection; Data sources; Datasets; Environment models; environmental fate; generative topographic mapping (GTM); Half-life; Learning algorithms; Machine learning; Mathematical models; persistence; Polybutylene terephthalates; QSAR/QSPR; REACH; Regression analysis; Sediments; Soil water; Soils; Support vector machines; Toxicity
• ISSN: 1062-936X; 1029-046X
• DOI: 10.1080/1062936X.2020.1776387

The evaluation of persistency of chemicals in environmental media (water, soil, sediment) is included in European Regulations, in the context of the Persistence, Bioaccumulation and Toxicity (PBT) assessment. In silico predictions are valuable alternatives for compounds screening and prioritization. However, already existing prediction tools have limitations: narrow applicability domains due to their relatively small training sets, and lack of medium-specific models. A dataset of 1579 unique compounds has been collected, merging several persistence data sources annotated by, at least, one experimental dissipation half-life value for the given environmental medium. This dataset was used to train binary classification models discriminating persistent/non-persistent (P/nP) compounds based on REACH half-life thresholds on sediment, water and soil compartments. Models were built using ISIDA (In SIlico design and Data Analysis) fragment descriptors and support vector regression, random forest and naïve Bayesian machine-learning methods. All models scored satisfactory performances: sediment being the most performing one (BA ext  = 0.91), followed by water (BA ext  = 0.77) and soil (BA ext  = 0.76). The latter suffer from low detection of persistent ('P') compounds (Sn ext  = 0.50), reflecting discrepancies in reported half-life measurements among the different data sources. Generated models and collected data are made publicly available.