大数据挖掘和机器学习在毒理学中的应用
Application of Data Mining and Machine Learning in Toxicology
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摘要: 随着高通量筛选技术的快速发展，化学品的毒性相关信息与日俱增。现今快速发展的数据挖掘技术和机器学习等计算机方法为化学品的毒性预测和风险防控提供了新途径。有害结局路径(adverse outcome pathway,AOP)将化合物的结构、分子启动事件和生物的有害结局建立关联，为污染物的毒性测试、预测和评估提供了新的模式，最终实现风险评估并应用于管理决策。定量结构-活性关系(QSAR)建模、分子模拟以及多组学技术在AOP的各个方面发挥了重要作用。基于此，本综述主要介绍数据挖掘与机器学习在毒理学中的应用方法，涉及QSAR建模、分子模拟及组学等方面，并结合实例分析系统阐述了当前研究的重点与方向，以更好地适应当前大数据时代的研究背景。Abstract: With the rapid development of high-throughput screening technologies, information on the toxicity of chemicals is growing day by day. The rapid development of computerized methods, such as data mining and machine learning, has provided a new approach to the toxicity prediction and risk control of chemicals. It is very important to establish the framework of ecological risk assessment by integrating a series of effective tools. Among these tools, adverse outcome pathway (AOP) can connect the structure of compounds, molecular initiation events, and adverse effects of organisms, thus can be used for risk assessment and management decisions. Quantitative structure-activity relationship (QSAR) modeling, molecular simulation and multi-omics techniques play important roles in the function of AOP. This review mainly introduces the application methods of data mining and machine learning in toxicology, including QSAR modeling, molecular simulation and omics. The current research focus and direction of computational toxicology were also reviewed with the aim of the better understanding of the big data era.
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Key words:
- data mining /
- machine learning /
- structure-activity relationship /
- AOP /
- computational toxicology
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