化学组编码的系统方法:一个案例研究
Panagiotis G Karamertzanis1, Grace Patlewicz2, Marta Sannicola1
1Computational Assessment and Alternative Methods, European Chemicals Agency (ECHA), Telakkakatu 6, Helsinki 00150, Finland.
机器学习模型现在可以预先将化学物质分配到预定义的组,改进了手动策划. 随机森林模型在化学风险评估中的这个任务中显示了比k-最近邻居更高的准确性.
科学领域:
- 计算毒理学计算毒理学
- 化学信息学 化学信息学
- 在监管科学中的机器学习
背景情况:
- 监管机构将化学品分组为危险和风险评估,但手动策划难以更新.
- 现有的化学分类缺乏明确的结构或基于属性的规则,阻碍了有效的更新.
- 化学组的手动专家策划是耗时的,并且很难用新的数据来改进.
研究的目的:
- 开发机器学习模型,以预先将物质分配到预定义的组.
- 为监管评估自动化和简化新化学物质的初始分析.
- 提高危险和风险评估过程中优先级的效率.
主要方法:
- 将86个欧洲化学品局 (ECHA) 的分组映射到美国环境保护署 (EPA) DSSTox数据库.
- 利用摩根指纹进行物质的化学和结构表示.
- 采用k-最近邻居 (kNN) 和随机森林 (RF) 机器学习分类器进行组分配.
主要成果:
- 随机森林 (RF) 实现了0.853的平均5倍交叉验证精度 (F1分数),超过了kNN (0.781).
- 射频分类器的准确性比kNN (p值=0.001) 的准确性有了统计学上显著的9%的改善.
- 模型成功地将物质分为至少10个成员和结构数据的56个组.
结论:
- 机器学习,特别是RF,为初始物质在预定义的组中进行分析提供了一个有前途的方法.
- 这种方法可以促进优先考虑和简化新物质的评估.
- 开发的算法是公开可用的,使模型使用和重新装配新的数据.
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