快速有效的分子性质预测与可转移性地图.
Shaolun Yao1,2,3, Jie Song3,4, Lingxiang Jia2
1Collaborative Innovation Center of Artificial Intelligence by MOE and Zhejiang Provincial Government, Zhejiang University, 310027, Hangzhou, China.
Communications chemistry
|April 17, 2024
概括
基于主要梯度的测量 (PGM) 量化了分子性质预测任务之间的可转移性. 这种方法指导了源数据集的选择,提高了目标任务的性能,加速了药物发现.
科学领域:
- 计算化学是一种计算化学.
- 机器学习 机器学习
- 药物发现 药物发现
背景情况:
- 转移学习对于具有有限数据的分子性质预测至关重要.
- 现有的方法有负面转移风险或需要广泛的目标任务培训.
研究的目的:
- 开发一种方法来量化分子性质预测任务之间的可转移性.
- 为了实现对源数据集的明智选择,以改善转移学习.
主要方法:
- 建议使用无优化方案进行基于主要梯度的测量 (PGM).
- 计算了主要梯度以近似模型优化方向.
- 测量转移性作为源和目标主要梯度之间的距离.
主要成果:
- 开发了用于源数据集选择的定量可转移性地图.
- 在12个基准数据集中,PGM有效指导转移学习.
- 通过PGM指导,表现出更好的目标任务性能.
结论:
- PGM为分子性质预测中的转移学习提供了快速有效的指导.
- 这种方法提高了药物,材料和催化剂的发现效率.
- 在转移学习之前,提供了对任务相关性的定量理解.
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