在ADMET预测中对ML进行基准测试:在基于联体的模型中特征表示的实际影响
Gintautas Kamuntavičius1, Tanya Paquet2, Orestis Bastas2
1AI Chemistry, Ro5, 2801 Gateway Drive, 75063, Irving, TX, USA. gkamuntavicius@ro5.ai.
Journal of cheminformatics
|July 21, 2025
概括
本研究介绍了一种结构化的特征选择方法,用于预测吸收,分布,新陈代谢,排泄和毒理学 (ADMET) 属性的机器学习模型. 改进的评估技术提高了预测可靠性和实际适用性.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在药物发现中的作用
- 预测性毒理学 预测性毒理学
背景情况:
- 预测吸收,分布,新陈代谢,分泌和毒理学 (ADMET) 属性的机器学习 (ML) 模型经常面临基于连接体的表示的挑战.
- 当前的做法经常结合不同的数据表示,而没有系统的特征选择,可能会影响模型性能.
研究的目的:
- 在ADMET预测模型中开发一种结构化的特征选择方法.
- 通过综合交叉验证和统计假设测试来提高ML模型评估的可靠性.
- 通过对独立数据集进行评估,评估训练模型的通用性.
主要方法:
- 实施了基于连接体的表征结构化的特征选择方法.
- 综合交叉验证与统计假设测试,以进行可靠的模型评估.
- 进行交叉数据集评估,以评估未见数据上的模型性能.
主要成果:
- 结构化特征选择方法比传统方法提供了系统的改进.
- 增强的评估技术,包括统计假设测试,增加对模型可靠性的信心.
- 交叉数据集评估为模型在现实药物发现场景中的适用性提供了实际见解.
结论:
- 拟议的方法提高了ADMET预测模型的可靠性和可解释性.
- 对特征选择和模型评估的概括方法提高了对选定的模型的信心,这对于杂的领域至关重要.
- 在外部数据集上评估模型性能为利用药物发现中的多样化数据源提供了有价值的见解.
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