生物QSARs 2.0:通过利用化学和生物信息来释放基于机器学习的生态毒性预测的新水平的预测能力
Jochen P Zubrod1, Nika Galic2, Maxime Vaugeois3
1Zubrod Environmental Data Science, 76829 Landau, Germany.
Environment international
|April 9, 2024
概括
称为Bio-QSARs的新计算模型预测了跨物种和化学物质的水生毒性. 这些先进的机器学习工具提高了环境风险评估,并支持绿色化学倡议.
科学领域:
- 计算毒理学计算毒理学
- 环境科学环境科学
- 机器学习 机器学习
背景情况:
- 动物试验面临着实际,法律和伦理方面的挑战,推动了对新方法方法 (NAM) 的需求.
- 计算技术,包括定量结构-活动关系 (QSAR),对于取代传统动物试验至关重要.
- 之前的Bio-QSAR模型显示了多种水生物种毒性预测的前景.
研究的目的:
- 显著扩大Bio-QSAR水生毒性模型的适用性和预测能力.
- 将更广泛的化学品,水生生物和实验条件纳入其中.
- 提高这些模型对环境风险评估和化学开发的有用性.
主要方法:
- 培训数据集扩展了大约20倍,包括更多的化学品和物种.
- 采用高斯过程增强,一种机器学习算法,结合了树增强和混合效应建模,以处理可变测试持续时间.
- 集成了各种生物描述符 (例如,动态能源预算参数,分类学距离,属性特征) 和作用方式信息.
主要成果:
- 开发了对鱼类和水生无脊椎动物的高度预测性的Bio-QSAR模型,在独立的测试组中达到高达0.92的R平方值.
- 使用算法多对线性校正和夏普利添加式解释,确保了模型可解释性.
- 创建了新的适用性领域构建方法,考虑到特征的重要性.
结论:
- 改进的Bio-QSAR模型提供了特殊的预测能力和适用于环境风险评估和化学研发的适用性.
- 这些可解释的,开放式的模型代表了在实施新方法方法学方面取得的重大进展.
- 这些模型有可能通过减少对动物试验的依赖来推进"绿色化学"和"绿色毒理学".
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