This study developed an OECD-aligned QSAR model with an explicit applicability domain to predict the adsorption of Sulfonylurea (SUs) herbicides and methabenzthiazuron in agricultural volcanic ash-derived soils (VADS), a variable-charge system poorly represented in current pesticide adsorption QSAR models. Twenty-four SUs and methabenzthiazuron were evaluated in ten VADS, generating 250 compound–VADS systems from batch adsorption–desorption experiments. The dataset was complemented by soil physicochemical characterization, adsorption kinetics, and adsorption-desorption analysis, and descriptor-based QSAR modelling using a Lamarckian Genetic Algorithm for variable selection and 𝑅𝑖𝑑𝑔𝑒 regression across interaction, soil-specific, and herbicide-specific edaphic scenarios, with external validation and domain of applicability (𝐷𝐴).
QSAR modelling for predicting adsorption of Sulfonylurea herbicides in agricultural volcanic ash-derived soils
Massarelli, Carmine;
2026
Abstract
This study developed an OECD-aligned QSAR model with an explicit applicability domain to predict the adsorption of Sulfonylurea (SUs) herbicides and methabenzthiazuron in agricultural volcanic ash-derived soils (VADS), a variable-charge system poorly represented in current pesticide adsorption QSAR models. Twenty-four SUs and methabenzthiazuron were evaluated in ten VADS, generating 250 compound–VADS systems from batch adsorption–desorption experiments. The dataset was complemented by soil physicochemical characterization, adsorption kinetics, and adsorption-desorption analysis, and descriptor-based QSAR modelling using a Lamarckian Genetic Algorithm for variable selection and 𝑅𝑖𝑑𝑔𝑒 regression across interaction, soil-specific, and herbicide-specific edaphic scenarios, with external validation and domain of applicability (𝐷𝐴).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
