有证据的元模型用于分子性质预测
Kyung Pyo Ham1, Lee Sael1,2
1Department of Artificial Intelligence, Ajou University, Suwon 16499, Republic of Korea.
Bioinformatics (Oxford, England)
|October 17, 2023
概括
我们开发了分子性质预测的证据元模型 (EM3P2),以改善分子性质预测. EM3P2提供不确定性估计,提高关键应用程序的可靠性.
科学领域:
- 计算化学是一种计算化学.
- 机器学习 机器学习
- 化学信息学 化学信息学
背景情况:
- 监督分子性质预测 (MPP) 是至关重要的,但由于数据不足和不平衡而受到挑战.
- 确保预测可靠性对于在安全关键领域部署MPP模型至关重要.
研究的目的:
- 引入一种用于分子性质预测的新方法,该方法包含不确定性估计.
- 解决分子性质数据集中的数据不平衡问题.
- 提高MPP模型的可靠性和性能.
主要方法:
- 开发了用于分子性质预测的证据元模型 (EM3P2).
- 采用了一个证据图形异态网络分类器.
- 使用了模型不可知的元学习 (MAML) 框架,并使用了多任务分子性质数据集.
- 集成的技术来处理数据不平衡.
主要成果:
- 与现有的meta-MPP模型相比,EM3P2显示出更高的预测性能.
- 该方法成功地与预测一起提供了不确定性估计.
- 可以有效地拒绝不确定的预测,提高对高风险应用程序的信心.
结论:
- EM3P2为分子性质预测提供了更好的性能和可靠性.
- 不确定性量化是提高MPP模型可信度的一个有价值的工具.
- 开发的方法适用于要求高预测可靠性的应用.
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