面向化学品风险预测的计算毒理学软件比较研究
A Comparative Study on Computational Toxicology Software for Chemical Risk Prediction
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摘要: 化学品风险评价是进行化学品环境管理和污染防治的前提，其关键在于化学品环境暴露和危害性数据的获取。仅采用实验测试获取相关数据的效率较低、成本高，难以满足数以万计的化学品风险评价的需求。以定量构效关系(QSAR)模型为代表的计算毒理学技术，可实现化学品环境暴露与危害性参数的高通量预测，是填补相关数据缺失的重要方法。近年来，计算毒理学领域发展了一批面向化学品环境暴露和危害性参数预测的软件工具，集成了数据和模型资源，在化学品管理活动中发挥了重要作用。本研究共搜集了国内外25款计算毒理学软件，通过文献资料调研和软件试用，从开发背景、预测终点、预测方法、功能与信息完整度和内嵌模型预测性能等方面对软件进行分析比较。分析表明，化学品管理法规支持、多方合作和数据共享，是计算毒理学软件开发的重要基础条件。现有计算毒理学软件在预测终点覆盖度、预测结果可靠性、软件实用性和可拓展性方面仍有提升空间。未来相关软件的开发需要结合深度学习等先进建模技术，增强软件的预测性能并扩大其应用范围，使之成为化学品风险综合评价和决策分析的实用工具。Abstract: Chemical risk assessment is important for chemical regulation and pollution prevention, the key step for risk assessment is to obtain the environmental exposure and hazard parameters of chemicals. Traditional experiments are time-consuming and costly to obtain these parameters, thus are impractical to assess the risks of a large number of chemicals. The quantitative structure-activity relationship (QSAR) model has drawn growing attention because it is a high-throughput computational toxicology method to predict such parameters. Recently, a set of computational toxicology software which integrate chemical parameters and computational model resources has been developed to predict environmental exposure and hazard parameters of chemicals, playing an important role in chemical management. This study collected 25 computational toxicology software and compared their development background, predicted endpoints, prediction methods, comprehensiveness of the information and functions, prediction accuracy through literature review and software trial. Results showed that the support of chemical management regulations, the multi-field cooperation and data sharing are important for development of computational toxicology software. Current software is generally limited in endpoint coverage, prediction reliability, software practicability and expansibility. To further develop such software for practical use in comprehensive chemical risk prediction and decision analysis, it is necessary to increase their predictive performance and expand their applicability via adopting advanced modeling techniques such as deep learning.
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Key words:
- chemicals /
- risk assessment /
- computational toxicology /
- software
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