Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity
Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity
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DOI：
10.1021/acs.chemrestox.8b00393
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发表时间：
2019-04-01
影响因子：
4.1
通讯作者：
Zhu, Hao
中科院分区：
文献类型：
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作者：
Ciallella, Heather L.;Zhu, Hao
2016年，Frank R.《21世纪化学品安全法案》成为美国第一部通过利用减少脊椎动物试验的新试验方法来推进化学品安全评估的立法。这一使命的核心是推进计算毒理学和人工智能方法，以实施创新的测试方法。在当前的大数据时代，术语体积（数据量），速度（数据增长）和多样性（来源的多样性）已被用于描述当前可用的化学品，体外和体内数据用于毒性建模目的。此外，正如许多科学家所建议的那样，公共数据库（如PubChem）的可变性（内部一致性或缺乏一致性）也带来了重大的计算挑战。迫切需要开发基于公共大量毒性数据的新型人工智能方法，以生成用于化学毒性评估的新预测模型，并使所开发的模型适用于评估未经测试的化合物。在这个过程中，传统的方法（例如，纯粹基于化学结构的定量构效关系（QSAR）已被新设计的数据驱动和机制驱动的建模所取代。该模型实现了不良后果途径（AOP）的概念，不仅可以直接评价新化合物的潜在毒性，而且可以阐明相关的毒性机制。大数据时代计算毒理学的最新进展为未来的毒性测试铺平了道路，这将对公众健康产生重大影响。
In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by utilizing novel testing approaches that reduce the testing of vertebrate animals. Central to this mission is the advancement of computational toxicology and artificial intelligence approaches to implementing innovative testing methods. In the current big data era, the terms volume (amount of data), velocity (growth of data), and variety (the diversity of sources) have been used to characterize the currently available chemical, in vitro, and in vivo data for toxicity modeling purposes. Furthermore, as suggested by various scientists, the variability (internal consistency or lack thereof) of publicly available data pools, such as PubChem, also presents significant computational challenges. The development of novel artificial intelligence approaches based on public massive toxicity data is urgently needed to generate new predictive models for chemical toxicity evaluations and make the developed models applicable as alternatives for evaluating untested compounds. In this procedure, traditional approaches (e.g., QSAR) purely based on chemical structures have been replaced by newly designed data-driven and mechanism-driven modeling. The resulting models realize the concept of adverse outcome pathway (AOP), which can not only directly evaluate toxicity potentials of new compounds, but also illustrate relevant toxicity mechanisms. The recent advancement of computational toxicology in the big data era has paved the road to future toxicity testing, which will significantly impact on the public health.