Tadpoles, as early developmental stages of frogs, are vital indicators of toxicity and environmental health in ecosystems exposed to harmful organic compounds from industrial and runoff sources. Evaluating each compound individually is challenging, necessitating the use of in silico methods like Quantitative Structure Toxicity-Relationship (QSTR) and Quantitative Read-Across Structure-Activity Relationship (q-RASAR). Utilizing the comprehensive US EPA's ECOTOX database, which includes acute LC50 toxicity and chronic endpoints, we extracted crucial data such as study types, exposure routes, and chemical categories. Regression-based QSTR and q-RASAR models were developed from this dataset, emphasizing key chemical descriptors. Lipophilicity and unsaturation were significant for predicting acute toxicity, while electrophilicity, nucleophilicity, and molecular branching were crucial for chronic toxicity predictions. Additionally, q-RASAR models integrated with the "intelligent consensus" algorithm were employed to enhance predictive accuracy. The performance of these models was rigorously compared across various approaches. These refined models not only predict the toxicity of untested compounds but also reveal underlying structural influences. Validation through comparison with existing literature affirmed the relevance and robustness of our approach in ecotoxicology.

中文翻译：

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使用 QSTR 和 q-RASAR 方法阐明参与蝌蚪毒性的分子机制


蝌蚪作为青蛙的早期发育阶段，是暴露于工业和径流来源有害有机化合物的生态系统中毒性和环境健康的重要指标。单独评估每种化合物具有挑战性，因此需要使用定量结构毒性关系 （QSTR） 和定量交叉读段结构-活性关系 （q-RASAR） 等计算机方法。利用美国 EPA 的综合 ECOTOX 数据库（包括急性 LC50 毒性和慢性终点），我们提取了研究类型、暴露途径和化学类别等关键数据。基于回归的 QSTR 和 q-RASAR 模型是从该数据集开发的，强调关键的化学描述符。亲脂性和不饱和度对于预测急性毒性具有显著意义，而亲电性、亲核性和分子分支对于慢性毒性预测至关重要。此外，采用与 “智能共识” 算法集成的 q-RASAR 模型来提高预测准确性。这些模型的性能在各种方法中进行了严格比较。这些精炼的模型不仅可以预测未经测试的化合物的毒性，还可以揭示潜在的结构影响。通过与现有文献的比较进行验证，证实了我们在生态毒理学方面的方法的相关性和稳健性。