Species Sensitivity Distribution (SSD) is a key metric for understanding the potential ecotoxicological impacts of chemicals. However, SSDs have been developed to estimate for only handful of chemicals due to the scarcity of experimental toxicity data. Here we present a novel approach to expand the chemical coverage of SSDs using Artificial Neural Network (ANN). We collected over 2000 experimental toxicity data in Lethal Concentration 50 (LC50) for 8 aquatic species and trained an ANN model for each of the 8 aquatic species based on molecular structure. The R² values of resulting ANN models range from 0.54 to 0.75 (median R² = 0.69). We applied the predicted LC50 values to fit SSD curves using bootstrapping method, generating SSDs for 8424 chemicals in the ToX21 database. The dataset is expected to serve as a screening-level reference SSD database for understanding potential ecotoxicological impacts of chemicals.

物种敏感性分布 (SSD) 是了解化学品潜在生态毒理学影响的关键指标。然而，由于实验毒性数据的缺乏，SSD 只能用于估计少数化学品。在这里，我们提出了一种利用人工神经网络 (ANN) 扩大 SSD 化学覆盖范围的新方法。我们收集了 8 个水生物种的致死浓度 50 (LC50) 中的 2000 多个实验毒性数据，并根据分子结构为 8 个水生物种中的每一个训练了 ANN 模型。生成的 ANN 模型的R ²值范围为 0.54 至 0.75（中值R ² = 0.69）。我们使用 bootstrapping 方法应用预测的 LC50 值来拟合 SSD 曲线，为 ToX21 数据库中的 8424 种化学品生成 SSD。该数据集预计将作为筛选级参考 SSD 数据库，用于了解化学品的潜在生态毒理学影响。