深度内核学习用于反应结果预测和优化
Sukriti Singh1, José Miguel Hernández-Lobato2
1Department of Engineering, University of Cambridge, Cambridge, UK. sukriti243@gmail.com.
Communications chemistry
|June 14, 2024
概括
本研究引入了一个深度内核学习 (DKL) 框架,将神经网络和高斯过程结合起来,用于预测化学反应结果. 德克尔模型实现了高精度,并提供了关键的不确定性估计,加速反应发现.
科学领域:
- 计算化学的计算化学
- 机器学习在化学中的应用
- 药物发现 药物发现 药物发现
背景情况:
- 机器学习,特别是深度学习,越来越多地用于预测化学反应结果.
- 深度学习模型擅长学习分子表示,但缺乏不确定性量化.
- 高斯过程 (GPs) 提供可靠的不确定性估计,但无法从数据中学习特征.
研究的目的:
- 开发一种新的深度内核学习 (DKL) 框架,集成神经网络 (NN) 和高斯过程 (GP).
- 预测化学反应结果,以高准确度和可靠的不确定性估计.
- 利用DKL通过贝叶斯优化 (BO) 加快反应发现.
主要方法:
- 实施了一个深度内核学习 (DKL) 框架,将NNs的特征学习与GPs的不确定性量化结合起来.
- DKL模型经过训练和评估,用于在各种分子表示中预测反应结果.
- 来自DKL模型的不确定性估计用于反应发现中的贝叶斯优化 (BO).
主要成果:
- 德克尔模型表现出对反应结果的强大预测性能,优于标准的全科医生.
- 德克尔实现了与图形神经网络相当的性能,同时提供了基本的不确定性估计.
- 不确定性估计使得DKL能够作为贝叶斯优化替代模型的有效使用.
结论:
- 拟议的DKL框架有效预测反应结果,并提供可靠的不确定性量化.
- 通过整合预测准确性和不确定性,DKL提供了一种强大的方法来加速化学反应的发现.
- 这种方法在推进自动反应发现工作流程方面具有重大潜力.
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