Traditional toxicity testing reliant on animal models is costly and low throughput, posing a significant challenge with the increasing numbers of chemicals that humans are exposed to in the environment. The purpose of this investigation was to build optimal prediction models for various human in vivo/organ-level toxicity end points (extracted from ChemIDPlus) using chemical structure and Tox21 in vitro quantitative high-throughput screening (qHTS) bioactivity assay data. Several supervised machine learning algorithms were applied to model 14 human toxicity end points pertaining to vascular, kidney, ureter and bladder, and liver organ systems. Three metrics were used to evaluate model performance: area under the receiver operating characteristic curve (AUC-ROC), balanced accuracy (BA), and Matthews correlation coefficient (MCC). The top four models, with AUC-ROC values >0.8, were derived for endocrine (0.90 ± 0.00), musculoskeletal (0.88 ± 0.02), peripheral nerve and sensation (0.85 ± 0.01), and brain and coverings (0.83 ± 0.02) toxicities, whereas the best model AUC-ROC values were >0.7 for the remaining 10 toxicities. Model performance was found to be dependent on the specific data set, model type, and feature selection method used. In addition, chemical structure and assay data showed different levels of contribution to the prediction of different toxicity end points. Although in vitro assay data, when combined with chemical structure, slightly improved the predictive accuracy for most end points (11 out of 14), a noteworthy finding was the near equal success of the structure-only models, which do not require Tox21 qHTS screening data, and the relatively poor performance of assay-only models. Thus, the top-performing structure-only models from this study could be applied for hazard screening of large sets of chemicals for potential human toxicity, whereas the largest assay contributions to models (i.e., cellular targets) could be used, along with the top-contributing structural features, to provide insight into toxicity mechanisms.

中文翻译：

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基于体外生物活性数据和化学结构的人体器官毒性预测模型。

依赖动物模型的传统毒性测试成本高且通量低，对人类在环境中接触的化学物质数量不断增加构成了重大挑战。这项研究的目的是使用化学结构和Tox21体外定量高通量筛选（qHTS）生物活性测定数据为各种人体体内/器官水平的毒性终点（摘自ChemIDPlus）建立最佳预测模型。几种有监督的机器学习算法被用于对14种与血管，肾脏，输尿管和膀胱以及肝器官系统有关的人类毒性终点进行建模。使用三个指标来评估模型性能：接收机工作特性曲线下的面积（AUC-ROC），平衡精度（BA）和Matthews相关系数（MCC）。前四名的模特，当AUC-ROC值> 0.8时，得出内分泌（0.90±0.00），肌肉骨骼（0.88±0.02），周围神经和感觉（0.85±0.01）以及脑和覆盖物（0.83±0.02）的毒性，而最佳模型其余10种毒性的AUC-ROC值均> 0.7。发现模型性能取决于所使用的特定数据集，模型类型和特征选择方法。另外，化学结构和测定数据显示出对不同毒性终点的预测有不同程度的贡献。尽管将体外测定数据与化学结构结合起来，可以稍微提高大多数终点的预测准确性（14个中的11个），但值得注意的发现是仅结构模型的成功率几乎相同，不需要Tox21 qHTS筛选数据，以及仅检测模型的相对较差的性能。因此，本研究中表现最好的仅结构模型可用于对大量化学物质进行危害性筛选，以检测潜在的人类毒性，而对模型（即细胞靶标）的最大分析贡献可与顶部一起使用-提供结构特征，以深入了解毒性机理。