The adverse outcome pathway (AOP) concept has gained attention as a way to explore the mechanism of chemical toxicity. In this study, quantitative structure–activity relationship (QSAR) models were developed to predict compound activity toward protein targets relevant to molecular initiating events (MIE) upstream of organ-specific toxicities, namely liver steatosis, cholestasis, nephrotoxicity, neural tube closure defects, and cognitive functional defects. Utilizing bioactivity data from the ChEMBL 33 database, various machine learning algorithms, chemical features and methods to assess prediction reliability were compared and applied to develop robust models to predict compound activity. The results demonstrate high predictive performance across multiple targets, with balanced accuracy exceeding 0.80 for the majority of models. Furthermore, stability checks confirmed the consistency of predictive performance across multiple training-test splits. The results obtained by using QSAR predictions to identify known markers of adversities highlighted the utility of the models for risk assessment and for prioritizing compounds for further experimental evaluation. Scientific contribution The work describes the development of QSAR models as tools for screening chemicals with potential systemic toxicity, thus contributing to resource savings and providing indications for further better-targeted testing. This study provides advances in the field of computational modeling of MIEs and information from AOP which is still relatively young and unexplored. The comprehensive modeling procedure is highly generalizable, and offers a robust framework for predicting a wide range of toxicological endpoints.

中文翻译：

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化学生物活性对与器官特异性毒性的分子起始事件相关的蛋白质的定量构效关系


不良结果途径 （AOP） 概念作为探索化学毒性机制的一种方式而受到关注。在这项研究中，开发了定量构效关系 （QSAR） 模型，以预测化合物对器官特异性毒性上游分子起始事件 （MIE） 相关的蛋白质靶标的活性，即肝脂肪变性、胆汁淤积、肾毒性、神经管闭合缺陷和认知功能缺陷。利用来自 ChEMBL 33 数据库的生物活性数据，比较并应用各种机器学习算法、化学特征和方法来评估预测可靠性，以开发可靠的模型来预测化合物活性。结果表明，跨多个目标的预测性能很高，大多数模型的平衡准确率超过 0.80。此外，稳定性检查证实了多个训练-测试拆分中预测性能的一致性。通过使用 QSAR 预测来识别已知的逆境标志物获得的结果突出了模型在风险评估和确定化合物优先级以进行进一步实验评估方面的效用。科学贡献 这项工作将 QSAR 模型的开发描述为筛选具有潜在全身毒性的化学品的工具，从而有助于节省资源并为进一步更有针对性的测试提供指示。本研究提供了 MIE 计算建模领域的进展以及来自 AOP 的信息，AOP 仍然相对年轻且尚未探索。综合建模程序具有高度的通用性，并为预测各种毒理学终点提供了一个强大的框架。