QSAR Modeling to Predict Aquatic Toxicity Across Multiple Species.

This study addresses the growing need for efficient and reliable application of New Approach Methodologies (NAMs) to assess aquatic toxicity of chemicals in response to increasing environmental contamination and regulatory demands. Particular emphasis is placed on in silico methods, especially quantitative structure-activity relationship (QSAR) modeling. Curated and structurally diverse datasets were compiled for representative aquatic organisms from different trophic levels, including the microalga Raphidocelis subcapitata, the crustacean Daphnia magna, and fish species (zebrafish embryo and fathead minnow). The models demonstrated consistently strong predictive performance across the evaluated assays. They were based on interpretable molecular descriptors associated with lipophilicity, polarity, and molecular reactivity. Furthermore, interspecies quantitative structure-activity-activity relationship (QSAAR) models were developed, demonstrating that toxicity data from lower trophic levels, combined with structural descriptors, can effectively predict fish toxicity. These models support cross-species extrapolation and contribute to environmental hazard assessment and regulatory decision-making.