我们是否能够准确地预测溶解度?
P Llompart1,2, C Minoletti2, S Baybekov1
1Laboratory of Chemoinformatics, UMR7140, University of Strasbourg, Strasbourg, France.
Scientific data
|March 19, 2024
概括
通过机器学习来预测热力学可溶性是很困难的. 这项研究表明,由于数据问题和忽视的历史数据集,当前的模型陷入困境,影响了它们在现实世界中的可靠性.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 物理化学 物理化学
背景情况:
- 用于热力学可溶性预测的机器学习模型显示出有希望的结果,但往往在前性上失败.
- 模型可靠性的差异源于数据质量和历史背景.
研究的目的:
- 调查机器学习可溶性模型前景表现不佳的原因.
- 分析水溶性数据格局,并评估20年的数据质量.
- 开发数据策划工作流程,以改善可溶性建模.
主要方法:
- 进行了可溶性数据集和模型的历史审查.
- 在一个新整理的数据集上对最近的机器学习模型进行了基准分析.
- 分析了影响模型实用性的因素,包括实验室间变化和溶液离子状态.
主要成果:
- 在历史溶解度数据中确定了重要的重叠和被忽视的数据集.
- 观察到最近最先进的模型在精选数据上的表现不佳.
- 他强调,模型缺乏定义的适用性领域,并忽略了历史数据.
结论:
- 当前的机器学习可溶性模型对于公众使用来说并不强大.
- 数据质量,历史数据整合和定义的适用性领域对于可靠的模型至关重要.
- 提供公开可用的,质量评估的数据集和改进的模型.
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