Abstract：
The escalating introduction of pesticides/veterinary drugs into the environment has necessitated a rapid evaluation of their potential risks to ecosystems and human health. The developmental toxicity of pesticides/veterinary drugs was less explored, and much less the large-scale predictions for untested pesticides, veterinary drugs and bio-pesticides. Alternative methods like quantitative structure-activity relationship (QSAR) are promising because their potential to ensure the sustainable and safe use of these chemicals. We collected 133 pesticides and veterinary drugs with half-maximal active concentration (AC50) as the zebrafish embryo developmental toxicity endpoint. The QSAR model development adhered to rigorous OECD principles, ensuring that the model possessed good internal robustness (R2 > 0.6 and QLOO2 > 0.6) and external predictivity (Rtest2 > 0.7, QFn2 >0.7, and CCCtest > 0.85). To further enhance the predictive performance of the model, a quantitative read-across structure-activity relationship (q-RASAR) model was established using the combined set of RASAR and 2D descriptors. Mechanistic interpretation revealed that dipole moment, the presence of C-O fragment at 10 topological distance, molecular size, lipophilicity, and Euclidean distance (ED)-based RA function were main factors influencing toxicity. For the first time, the established QSAR and q-RASAR models were combined to prioritize the developmental toxicity of a vast array of true external compounds (pesticides/veterinary drugs/bio-pesticides) lacking experimental values. The prediction reliability of each query molecule was evaluated by leverage approach and prediction reliability indicator. Overall, the dual computational toxicology models can inform decision-making and guide the design of new pesticides/veterinary drugs with improved safety profiles.
摘要：
农药/兽药不断向环境中引入，因此有必要对其对生态系统和人类健康的潜在风险进行快速评估。农药/兽药的发育毒性研究较少，更不用说对未经测试的农药的大规模预测了，兽药和生物农药。定量结构-活性关系（QSAR）等替代方法很有希望，因为它们具有确保这些化学品可持续和安全使用的潜力。我们收集了133种农药和兽药，以半最大活性浓度（AC50）作为斑马鱼胚胎发育毒性终点。QSAR模式的发展遵循严格的OECD原则，确保模型具有良好的内部稳健性(R2>0.6，QLOO2>0.6)和外部预测性(Rtest2>0.7，QFn2>0.7，CCCtest>0.85)。为了进一步增强模型的预测性能，使用RASAR和2D描述符的组合集建立了定量的结构-活性关系（q-RASAR）模型。力学解释表明，偶极矩，拓扑距离为10的C-O片段的存在，分子大小,亲脂性，基于欧氏距离（ED）的RA功能是影响毒性的主要因素。第一次,将已建立的QSAR和q-RASAR模型结合起来,优先考虑大量缺乏实验价值的真正外部化合物(农药/兽药/生物农药)的发育毒性.采用杠杆法和预测可靠性指标对各查询分子的预测可靠性进行评价。总的来说,双重计算毒理学模型可以为决策提供信息，并指导具有改进安全性的新农药/兽药的设计。
