MolMVC:通过多视图对比学习来增强与毒品相关任务的分子表示
Zhijian Huang1, Ziyu Fan1, Siyuan Shen1
1School of Computer Science and Engineering, Central South University, Changsha 410083, China.
Bioinformatics (Oxford, England)
|September 4, 2024
概括
本研究介绍了MolMVC,这是一种用于分子表示的多视图对比学习框架. 通过提高预测准确度和降低各种与药物相关的任务中的计算成本,MolMVC增强了药物开发.
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:
- 有效的分子表示对于药物开发至关重要.
- 分子需要全面的多视图表示 (1D,2D,3D) 才能全面理解.
- 现有的方法可能无法完全捕捉分子的多样化结构方面.
研究的目的:
- 引入MolMVC,这是一个创新的多视图对比学习框架,用于分子表示.
- 开发一种有效整合1D,2D和3D分子信息的方法.
- 为了提高药物发现的下游任务性能.
主要方法:
- 使用变压器编码器来获取1D序列信息,使用图形变压器来获取2D/3D结构.
- 整合了一个以注意力为导向的增强方案,用于定制的阳性样本生成.
- 引入了自适应式多视图对比损失 (AMCLoss) 来对准潜伏空间中的多视图表示.
- 在各种层次层次上计算AMCLoss以捕获分子信息的复杂性.
主要成果:
- MolMVC在分子性质预测 (MPP),药物标结合亲和力 (DTA) 预测和癌症药物反应 (CDR) 预测方面表现出增强的预测精度.
- 该框架降低了这些任务的计算成本.
- 在药物重新定位应用中,MolMVC 显示出有效性.
结论:
- MolMVC提供了一种强大的方法来学习全面的分子表示.
- 该框架有效地整合了多视图分子数据,以改善药物发现结果.
- 学习的表征是多功能性的,适用于一系列与毒品有关的任务.
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