Journal of Chemical Information and Modeling · 2013 · 221 citations · 41 references
PharmacyToxicologyDrug DesignDrug DiscoveryStructure-activity RelationshipsBiodegradationMolecular DescriptorsMolecular Property PredictionQuantitative Structure-activity RelationshipsStructure-activity Relationship AnalysisBiodegradabilityChemical Structure-activity RelationshipsClassification ModelsDescriptor-based ModelingK-nearest NeighborsPartial Least Squares Discriminant AnalysisPredictive Modeling Of BioactivityStructure-activity Relationship Modeling
The European REACH regulation requires ready‑biodegradation data, and it promotes QSAR models as non‑animal alternatives. The study aimed to develop QSAR models that predict ready biodegradation using various modeling methods and molecular descriptor types. Experimental values for 1,055 chemicals were gathered from NITE, split into calibration (837) and test (218) sets, with an additional external validation set of 670 molecules, and classification models were built with k‑nearest neighbors, PLS‑DA, SVM, and consensus approaches while applying rigorous data screening and validation. The resulting models and consensus analysis showed strong classification performance relative to existing QSARs, and the selected descriptors were linked to biodegradability.
The European REACH regulation requires information on ready biodegradation, which is a screening test to assess the biodegradability of chemicals. At the same time REACH encourages the use of alternatives to animal testing which includes predictions from quantitative structure-activity relationship (QSAR) models. The aim of this study was to build QSAR models to predict ready biodegradation of chemicals by using different modeling methods and types of molecular descriptors. Particular attention was given to data screening and validation procedures in order to build predictive models. Experimental values of 1055 chemicals were collected from the webpage of the National Institute of Technology and Evaluation of Japan (NITE): 837 and 218 molecules were used for calibration and testing purposes, respectively. In addition, models were further evaluated using an external validation set consisting of 670 molecules. Classification models were produced in order to discriminate biodegradable and nonbiodegradable chemicals by means of different mathematical methods: k nearest neighbors, partial least squares discriminant analysis, and support vector machines, as well as their consensus models. The proposed models and the derived consensus analysis demonstrated good classification performances with respect to already published QSAR models on biodegradation. Relationships between the molecular descriptors selected in each QSAR model and biodegradability were evaluated.
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