In this study, count-based Morgan fingerprints (CMF) was used to represent the fundamental chemical structures of contaminants, and a neural network model (R²=0.76) was developed to predict acute fish toxicity (AFT) of organic compounds, which surpassed previous models. We found the limitations of in distinguishing homologous compounds may account for the suboptimal performance of binary fingerprints. The principles of generation and collision of CMF was explored and an improved method based on Tanimoto distance was introduced to calculated molecular similarity represented by CMFs as well. Toxic substructures identified by Shapley additive explanation (SHAP) method were substituted benzenes, long carbon chains, unsaturated carbons and halogen atoms. By incorporating KOW and monitoring shifts in feature importance, the influence of substructures on AFT was further delineated, revealing their roles in facilitating exposure and reactive toxicity. On this basis, we compared the toxicity of similar substructures and the same substructure in different chemical environments. To overcome the limitation of SHAP analysis, this study proposed a new method, toxicity index (TI), to identify substructures that were present in small quantities but highly toxic. With TI, we identified several important substructures, such as parathion and polycyclic substituents. We found that the toxicity of large substructures may be misestimated in the previous studies.

中文翻译：

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有机污染物对鱼类急性毒性的预测：模型开发和识别反应性子结构的新方法


本研究采用基于计数的 Morgan 指纹图 （CMF） 来表示污染物的基本化学结构，并开发了神经网络模型 （R²=0.76） 来预测有机化合物的急性鱼类毒性 （AFT），超过了以前的模型。我们发现区分同源化合物的局限性可能是二元指纹图谱性能不佳的原因。探讨了 CMF 的产生和碰撞的原理，并引入了一种基于 Tanimoto 距离的改进方法，该方法还计算了以 CMF 表示的分子相似性。通过 Shapley 加法解释 （SHAP） 方法鉴定的毒性亚结构是取代苯、长碳链、不饱和碳和卤素原子。通过结合 KOW 和监测特征重要性的变化，进一步描绘了子结构对 AFT 的影响，揭示了它们在促进暴露和反应毒性方面的作用。在此基础上，我们比较了相似子结构和相同子结构在不同化学环境中的毒性。为了克服 SHAP 分析的局限性，本研究提出了一种新方法，毒性指数 （TI），用于识别少量但毒性高的亚结构。通过 TI，我们确定了几个重要的亚结构，例如对硫磷和多环取代基。我们发现，在以前的研究中，大子结构的毒性可能被错误估计。