通过语义不变对比学习进行分子性质预测
Ziqiao Zhang1, Ailin Xie1, Jihong Guan2
1Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, Fudan University, Shanghai 200438, China.
Bioinformatics (Oxford, England)
|July 28, 2023
概括
一种新的方法,基于碎片的语义不变对比学习 (FraSICL),通过生成一致的分子视图来改善分子表示学习. 这种方法在人工智能辅助的药物发现中实现了最先进的性能,使用更少的预训练样本.
科学领域:
- 人工智能在药物发现中的作用
- 计算化学是一种计算化学.
- 机器学习用于分子建模.
背景情况:
- 对比学习是自我监督分子表示学习的关键技术.
- 使用增加噪声来生成视图的现有方法可能会导致语义不一致,导致模型性能差.
研究的目的:
- 开发一种新的语义不变视图生成方法,用于对比学习.
- 引入基于碎片的语义不变对比学习 (FraSICL) 模型,用于增强分子性质预测.
主要方法:
- 通过将分子图解分解成碎片对,提出了一个语义不变的视图生成.
- 开发了具有两个分支的FrasICL模型,用于视图表示生成.
- 整合了多视图融合和辅助相似性损失,以改进信息利用.
主要成果:
- FraSICL在用于分子性质预测的基准数据集上实现了最先进的性能.
- 与现有方法相比,该模型显示出优异的结果,特别是在有限的预培训数据的情况下.
- 提出的语义不变视图生成解决了增加噪声技术的局限性.
结论:
- FraSICL提供了一种强大而高效的方法,用于自我监督的分子表示学习.
- 该方法有效地克服了药物发现对比学习中的语义不一致问题.
- FraSICL模型显示了推进人工智能辅助药物设计和发现的巨大潜力.
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