Low Yen Sia, Sedykh Alexander Yeugenyevich, Rusyn Ivan, Tropsha Alexander
100K Beard Hall, Campus Box 7568, University of North Carolina, Chapel Hill, NC 27599-7568, USA.
Curr Top Med Chem. 2014;14(11):1356-64. doi: 10.2174/1568026614666140506121116.
Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity.
诸如定量构效关系（QSAR）建模等化学信息学方法传统上一直用于预测化学毒性。近年来，高通量生物学检测越来越多地被用于阐明化学毒性机制并预测化学物质在体内的毒性作用。此类检测中产生的数据可被视为化学物质的生物学描述符，这些描述符可与分子描述符相结合，并用于QSAR建模以提高毒性预测的准确性。在本综述中，我们讨论了几种整合化学和生物学数据以预测化学物质在体内生物学效应的方法，并在多个数据集上比较了它们的性能。我们得出结论，虽然没有一种方法始终表现出卓越的性能，但整合方法始终位列最佳方法之中，并且与仅基于化学或生物学数据构建的模型相比，能提供更丰富的模型解释。我们讨论了此类跨学科方法的前景，并提出建议以进一步提高预测化学毒性的计算模型的准确性和可解释性。
