图形CL-DTA:图形对比学习与分子语义,用于药物目标结合 afinity 预测
IEEE journal of biomedical and health informatics
|January 8, 2024
概括
通过改善药物向相互作用预测,GraphCL-DTA增强了药物发现. 这种新方法使用图形对比学习来更好地表示分子,提高药物标结合亲和力的预测准确度.
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 机器学习
背景情况:
- 药物标结合亲和力预测对于早期药物发现至关重要.
- 现有的计算模型难以从分子图表中学习表示,并忽视统一度量.
- 以前的方法通常仅依赖于监督数据,忽视固有的分子图信息.
研究的目的:
- 介绍GraphCL-DTA,一个新的图形对比学习框架,用于药物标结合亲和力预测.
- 通过结合分子语义和优化统一性来增强药物和目标表示学习.
- 提高药物发现中的计算模型的性能和概括能力.
主要方法:
- 开发了一个图形对比学习框架 (GraphCL-DTA),利用嵌入空间数据增强来保存分子图形语义.
- 引入了一个新的损失函数,直接优化药物和目标表示的统一性.
- 在KIBA和戴维斯数据集上验证模型,将性能与GraphDTA进行比较.
主要成果:
- 与GraphDTA相比,GraphCL-DTA显示了药物标结合亲和力预测准确度的提高,KIBA的相对改善为2.7%,Davis的相对改善为4.5%.
- 在没有额外的监督数据的情况下,图形对比学习和统一功能提高了药物和目标表示的质量.
- 拟议的模块显示了在其他计算模型中改进概括的潜力.
结论:
- 通过利用图形对比学习和表示统一性,GraphCL-DTA提供了一种更有效的方法来预测药物标结合亲和力.
- 该框架提供了一种强大的方法来学习基本的药物表征,优于以前的模型.
- 可以将GraphCL-DTA模块集成到现有的计算模型中,以增强其预测能力和概括性.
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