Use of quasi-SMILES to build models based on quantitative results from experiments with nanomaterials. (September 2022)

• Record Type: Journal Article
• Authors: Toropov, Andrey A.; Kjeldsen, Frank; Toropova, Alla P.
• Is Part Of: Chemosphere. Volume 303:Part 2(2022)
• Publication Date: 2022-09
• Subjects: Silver nanoparticle -- Nanotoxicity -- Daphnia magna -- Zebrafish -- Monte Carlo method -- CORAL software -- Quasi-SMILES; Pollution -- Periodicals; Pollution -- Physiological effect -- Periodicals; Environmental sciences -- Periodicals; Atmospheric chemistry -- Periodicals; 551.511
• DOI: 10.1016/j.chemosphere.2022.135086
• Languages: English
• ISSNs: 0045-6535

Abstract

Abstract: Quasi-SMILES deviate from traditional SMILES (simplified molecular input-line entry system) by the extension of additional symbols that encode for conditions of an experiment. Descriptors calculated with SMILES are useful for the development of quantitative structure-property/activity relationships (QSPRs/QSARs), while descriptors calculated with quasi-SMILES can be useful for the development of quantitative models of experimental results obtained under different conditions. Here, this approach has been applied for the development of generalized models using aquatic nanotoxicity data (i.e., related to fish and daphnia). The statistical quality of the above models ( pLC50 ) is quite good with a determination coefficient for the external validation set ranging from 0.62 to 0.71 and RMSE ranging from 0.58 to 0.60. The principle of the approach includes splitting the experimental data into three random distributions defining training, calibration, and validation sets. Graphical abstract: Quasi-SMILES is an efficient methodology to build predictive models from data obtained at different experimental conditions. Image 1 Highlights: Quasi-SMILES is a tool to represent all available eclectic data from the experimental data. Quasi-SMILES is shown here to produce quantitative results of statistical quality. The presented methodology allow prediction of the nanoparticle features that are most toxic.