Human exposure to toxic chemicals presents a huge health burden. Key to understanding chemical toxicity is knowledge of the molecular target(s) of the chemicals. Because a comprehensive safety assessment for all chemicals is infeasible due to limited resources, a robust computational method for discovering targets of environmental exposures is a promising direction for public health research. In this study, we implemented a novel matrix completion algorithm named coupled matrix–matrix completion (CMMC) for predicting direct and indirect exposome-target interactions, which exploits the vast amount of accumulated data regarding chemical exposures and their molecular targets. Our approach achieved an AUC of 0.89 on a benchmark data set generated using data from the Comparative Toxicogenomics Database. Our case studies with bisphenol A and its analogues, PFAS, dioxins, PCBs, and VOCs show that CMMC can be used to accurately predict molecular targets of novel chemicals without any prior bioactivity knowledge. Our results demonstrate the feasibility and promise of computationally predicting environmental chemical-target interactions to efficiently prioritize chemicals in hazard identification and risk assessment.

中文翻译：

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使用耦合矩阵-矩阵补全进行环境化学品的基因靶标预测


人类接触有毒化学物质会带来巨大的健康负担。了解化学品毒性的关键是了解化学品的分子靶标。由于资源有限，对所有化学品进行全面的安全评估是不可行的，因此用于发现环境暴露目标的强大计算方法是公共卫生研究的一个有前途的方向。在这项研究中，我们实现了一种名为耦合矩阵-矩阵补全（CMMC）的新型矩阵补全算法，用于预测直接和间接暴露体-目标相互作用，该算法利用了有关化学暴露及其分子目标的大量累积数据。我们的方法在使用比较毒理基因组数据库的数据生成的基准数据集上实现了 0.89 的 AUC。我们对双酚 A 及其类似物、PFAS、二恶英、PCB 和 VOC 的案例研究表明，CMMC 可用于准确预测新型化学品的分子靶标，而无需事先了解任何生物活性知识。我们的结果证明了通过计算预测环境化学品与目标相互作用的可行性和前景，以在危害识别和风险评估中有效地优先考虑化学品。