ArticleJournal of xenobiotics2026
A SAR-Based Classification Model for Assessing Pesticide Toxicity to
Article in Journal of xenobiotics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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4 authors.
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Abstract
Pollinators are essential for maintaining ecosystem stability and agricultural productivity, yet their populations are in decline due to various stressors, including parasites, pathogens, climate change and pesticide exposure. Protecting pollinators has become a priority for environmental safety and food security. Regulatory authorities, including the European Food Safety Authority (EFSA), the Environmental Protection Agency (EPA) and the Organisation for Economic Co-operation and Development (OECD), have guidelines for pesticide risk assessment, but conventional testing methods are costly and time-consuming, limiting their applicability to large chemical datasets. Computational approaches, such as Structure-Activity Relationship (SAR) models, offer efficient alternatives by enabling the rapid screening of pesticides for potential toxicity to pollinators. In this study, we used a dataset of 357 compounds to develop a classification model based on structural alerts to predict oral acute toxicity in
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