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Updated: Jul 25, 2025

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通过代重排增强聚合物数据集
Stanley Lo1, Martin Seifrid1, Théophile Gaudin2,3
1Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, Ontario M5S 3H6, Canada.
Journal of chemical information and modeling
|June 30, 2023
概括
通过重新排列聚合物表示来增强数据并没有显著改善机器学习属性预测. 然而,它增强了对序列依赖性质的分子嵌入,为准确性提供了更多信息.
科学领域:
- 聚合物科学 聚合物科学
- 材料 信息学 信息学
- 计算化学计算化学
背景情况:
- 准确的聚合物性质预测依赖于有效的分子表示.
- 现有的表示可能无法完全捕捉依赖序列的信息.
- 数据增强技术在其他科学领域也表现有前途.
研究的目的:
- 通过分子表示重新排列来研究聚合物属性预测的数据增强.
- 评估增强聚合物数据是否可以提高机器学习模型的性能.
- 将增强表示与普通分子表示进行比较.
主要方法:
- 聚合物分子表示的代重排,同时保持连接性.
- 在三个聚合物数据集上训练机器学习模型,使用原始和增强表示.
- 评估模型性能并将增强嵌入式与非增强嵌入式进行比较.
主要成果:
- 数据增强并没有比同等的非增强表示带来显著的改进.
- 增强为序列主导数据集中的分子嵌入提供了更多信息.
- 当目标属性主要受到聚合物序列的影响时,观察到性能增长.
结论:
- 通过表示重新排列来增强聚合物数据显示,对属性预测的一般改进有限.
- 这种技术可以增强对序列依赖性质的分子嵌入.
- 进一步的研究可能会探索聚合物信息学的优化增强策略.
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