Relationships between toxicity and chemical hydrophobicity have been known for nearly 100 years in mammals and fish, typically using the log of the octanol:water partition coefficient (Kow). The current study reassessed the influence of mode of action (MOA) on acute aquatic toxicity-log Kow relationships using a comprehensive database of 617 organic chemicals with curated and standardized acute toxicity data that did not exceed solubility limits, their consensus log Kow values, and weight of evidence-based MOA classifications (including 6 broad and 26 specific MOAs). A total of 166 significant (p < 0.05) log Kow-toxicity models were developed across six taxa groups that included QSARs for 5 of the broad and 13 of the specific MOAs. In this study, we demonstrate that QSARs based on MOAs can significantly increase LC50 prediction accuracy for specific acting chemicals. Prediction accuracy increases when QSARs are built based on highly specific MOAs, rather than broad MOA classifications. Additionally, we demonstrate that building QSAR models with chemicals in specific MOA groupings, rather than broader MOA groups leads to significantly better estimates. We also evaluated the differences between models developed from mass-based (µg/L) and mole-based (µmol/L) toxicity data and demonstrate that both are suitable for QSAR development with no clear trend in greater model accuracy. Overall, the results reveal that, despite high variance in all taxa and MOA groups, specific MOA-based models can improve the accuracy of aquatic toxicity predictions over more general groupings.Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary.The affiliations are correct.

近 100 年来，哺乳动物和鱼类的毒性和化学疏水性之间的关系已为人所知，通常使用辛醇：水分配系数 (Kow) 的对数。目前的研究使用包含 617 种有机化学品的综合数据库重新评估了作用方式 (MOA) 对急性水生毒性 - log Kow 关系的影响，该数据库包含不超过溶解度限值的经过整理和标准化的急性毒性数据、它们的共识 log Kow 值，以及基于证据的 MOA 分类的权重（包括 6 个广泛的 MOA 和 26 个特定的 MOA）。共 166 个显着（p < 0.05) log Kow 毒性模型是在六个分类群中开发的，其中包括 5 个广义 MOA 和 13 个特定 MO​​A 的 QSAR。在这项研究中，我们证明了基于 MOA 的 QSAR 可以显着提高特定作用化学品的 LC50 预测准确度。当 QSAR 基于高度特定的 MOA 而不是广泛的 MOA 分类构建时，预测准确性会提高。此外，我们证明了使用特定 MO​​A 组中的化学品而不是更广泛的 MOA 组来构建 QSAR 模型会导致更好的估计。我们还评估了基于质量 ( µ g/L) 和基于摩尔 ( µmol/L) 毒性数据，并证明两者都适用于 QSAR 开发，但没有明显的模型精度趋势。总体而言，结果表明，尽管所有分类群和 MOA 组的差异很大，但基于特定 MO​​A 的模型可以提高对更一般分组的水生毒性预测的准确性。请检查并确认作者及其各自的隶属关系已被正确识别并在必要时进行修改。从属关系正确。