开发和评估对量化结构与活动关系的合规预测方法.
Yuting Xu1, Andy Liaw1, Robert P Sheridan2
1Early Development Statistics, Merck & Co., Inc., Rahway, New Jersey 07065, United States.
ACS omega
|July 15, 2024
概括
本研究介绍了用于定量结构-活动关系 (QSAR) 建模的高效符合性预测 (CP) 算法. 这些方法为机器学习模型提供可靠的预测间隔,增强QSAR预测.
科学领域:
- 计算化学是一种计算化学.
- 化学信息学 化学信息学
- 机器学习 机器学习
背景情况:
- 定量结构-活性关系 (QSAR) 模型使用分子描述符预测化合物生物活性.
- 准确的活动估计至关重要,但量化预测不确定性 (例如,预测间隔) 在QSAR中仍然是一个挑战.
- 大多数高性能机器学习 (ML) 算法需要单独的方法来估计不确定性.
研究的目的:
- 为QSAR建模开发计算效率高的合规预测 (CP) 算法.
- 为了使QSAR中广泛使用的ML模型可靠的预测间隔.
- 为了应对在QSAR中量化预测不确定性的挑战.
主要方法:
- 为随机森林,深度神经网络和梯度增强量身定制的计算效率高的合规预测 (CP) 算法.
- 利用一种新的方法,从基于集合的预测不确定性估计得出不一致性得分.
- 实施的算法设计为不可知于预测模式,并产生有效的预测间隔.
主要成果:
- 在各种QSAR数据集和模拟研究中证明了拟议的CP算法的有效性和效率.
- 展示了算法能够为基于ML的QSAR模型生成可靠的预测间隔的能力.
- 验证了新型不符合性得分推导方法的性能.
结论:
- 开发的CP算法为QSAR建模中的不确定性量化提供了强大的和高效的解决方案.
- 软件实现方便集成到QSAR.ML现有的ML工作流.
- 这项工作通过有效的预测间隔提高了QSAR预测的可靠性和可解释性.
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