使用预训练的图形神经网络改进化学反应产量预测
Jongmin Han1, Youngchun Kwon2, Youn-Suk Choi3
1Department of Industrial Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon, Republic of Korea.
Journal of cheminformatics
|March 1, 2024
概括
在大型分子数据库上进行预训练图形神经网络 (GNN) 可以改善化学反应产量预测,尤其是在数据有限的情况下. 本研究引入了一种新的预训练方法,使用主要组件分析来提高表现.
科学领域:
- 计算化学是一种计算化学.
- 机器学习在化学中的应用
背景情况:
- 图形神经网络 (GNN) 是有效的预测化学反应产量.
- 在训练数据集不足或不多样化的情况下,GNN的性能会下降.
研究的目的:
- 为了研究预先训练GNN用于化学反应产量预测的有效性.
- 引入一种新的GNN预训练方法,以提高预测性能.
主要方法:
- 在大型分子数据库上预先训练的GNN使用了新的文本前任务.
- 使用主要成分分析 (PCA) 计算分子描述符和减少维度.
- 分配的主要组件分数作为GNN预训的伪标签.
- 在反应产量数据集上微调预训练的GNN.
主要成果:
- 通过实验评估证明了GNN预训练的有效性.
- 提出的方法在化学反应产量预测方面表现得更好.
- 成功解决了GNN有限培训数据的问题.
结论:
- 预先培训GNN是一种可行的策略,可以提高化学反应产量预测的准确性.
- 使用PCA衍生伪标签的新型预培训方法是有效的.
- 这种方法为具有有限化学反应数据的场景提供了解决方案.
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