INTransformer:通过将噪声注入变压器来进行基于数据增强的对比学习,用于分子性质预测
Jing Jiang1, Yachao Li1, Ruisheng Zhang2
1Key Laboratory of Linguistic and Cultural Computing, Ministry of Education, Northwest Minzu University, Lanzhou 730030, China.
Journal of molecular graphics & modelling
|January 16, 2024
概括
这项研究介绍了INTransformer,这是一种使用对比学习改进分子性质预测的新型数据增强方法. 它通过解决有限的标记数据和改进全球分子信息捕获来增强深度学习模型.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习是机器学习.
背景情况:
- 分子性质预测对于识别候选药物至关重要.
- 深度学习模型需要大量的标记数据,这在这个领域往往很少.
- 捕获全球分子信息对于准确的属性预测至关重要.
研究的目的:
- 开发一种用于分子性质预测的数据增强方法.
- 为了应对有限的标记分子数据的挑战.
- 提高模型捕获全球分子信息的能力.
主要方法:
- 提出了INTransformer,一种利用对比学习的数据增强技术.
- 使用两个相同的变压器子编码器来处理原始和杂的SMILES数据.
- 应用对比学习以确保原始和增强数据之间一致的分子编码.
主要成果:
- INTransformer有效地增加了有限的标记分子数据.
- 该方法增强了全球分子表示的提取.
- 在用于分子性质预测的基准数据集上取得了竞争性表现.
结论:
- INTransformer减轻了深度学习中的数据限制,用于分子性质预测.
- 这种方法可以更好地捕获关键的全球分子信息.
- 与现有方法相比,已证明具有竞争力和有效的性能.
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