从以数据为中心的角度重新思考用于预训练分子图的掩盖策略
Wei Lin1, Chi Chung Alan Fung1
1Department of Neuroscience, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Kowloon 999077, Hong Kong, China.
ACS omega
|May 20, 2024
概括
一个新的加权掩盖策略通过解决不平衡的原子分布来改善分子图的预训练. 这种方法可以提高模型容量和性能,而无需添加参数,性能优于随机掩盖.
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:
- 节点级自主监督学习对于预训练分子图来说至关重要.
- 属性掩盖 (AttrMask) 是一个关键的方法,其增强侧重于模型架构.
- 现有的方法忽略了随机掩盖导致的不平衡原子分布.
研究的目的:
- 为更有效的分子图表预训练引入加权掩盖策略.
- 通过更好地利用分子信息来提高预训练模型的容量.
- 在不增加模型参数的情况下,提高随机掩盖的性能.
主要方法:
- 提出了一种针对分子性质的新型加权掩盖策略.
- 将该策略应用于属性掩盖 (AttrMask) 框架.
- 对随机掩盖和以模型为中心的方法进行性能评估.
主要成果:
- 与随机掩盖相比,加权掩盖策略显著提高了性能.
- 在不增加模型参数的情况下实现了性能增长.
- 该战略证明了有效性和更广泛应用的潜力.
结论:
- 权重掩饰是分子图表预训练中随机掩饰的优越替代方案.
- 这种方法通过解决原子分布来提高模型容量和性能.
- 拟议的策略可与其他预训练方法相适应,以获得更好的结果.
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