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2 results for “QSPR”
QSPR models for bioconcentration factor (BCF): Are they able to predict data of industrial interest?
<p>This dataset is described and studied in the article </p> <p>"QSPR models for bioconcentration factor (BCF): Are they able to predict data of industrial interest?"</p> <p>published in <em>SAR and QSAR Environmental Research</em> (Taylor&Francis).</p> <p>Files description:</p> <p>SI_BCFtrainset.xlsx: a collection of 1129 chemical structures and CAS identifiers with their logBCF values extracted from various literature sources.</p> <p>SI_BCFtestset.xlsx: a collection of 204 chemical structures for which the logBCF is considered of lower reliability and used as an external test set.</p> <p>SI_FullDataset_rawdata.csv: the raw data composed of 15372 entries with the following columns: CASRN, Tissue, Duration [d], Test organism, Exposure type, Steady state, RESPONSE, RESPONSE UNIT, Media type, TakenFrom, TITLE, AUTHOR, YEAR, SOURCE, SMILES</p> <p>SI_ExcludedOutliers34.csv: 34 chemical structures that have been identified as suspicious during analysis.</p> <p> </p>
Publicly available QSPR models for environmental media persistence
<p>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 prediction are a valuable alternative 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<sub>ext</sub> = 0.91), followed by water (BA<sub>ext</sub> = 0.77) and soil (BA<sub>ext</sub> = 0.76). The latter suffer from low detection of persistent (“P”) compounds (Sn<sub>ext</sub> = 0.50), reflecting discrepancies in reported half-life measurements among the different data sources. Generated models and collected data are made publicly available.</p>
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