AffiGrapher:对比异质图学习与芳香虚拟节点用于RNA-小分子结合亲缘关系预测
Junkai Wang1, Jian Wu2, Zhijun Zhang1
1School of Computer Science and Technology, Soochow University, Ganjiang East Streat 333, Jiangsu 215006, China.
Journal of chemical information and modeling
|June 26, 2025
概括
我们开发了AffiGrapher,一种新的图形神经网络,用于预测RNA-小分子结合亲和力. 这种以物理为导向的方法提高了准确性,并显示了RNA向药物发现的巨大潜力.
科学领域:
- 生物化学 生物化学
- 计算生物学 计算生物学
- 药物发现 药物发现 药物发现
背景情况:
- 由于其多样化的结构和功能,RNA分子是关键的药物点.
- 预测RNA-小分子结合亲和力是困难的,因为有限的数据和RNA的结构灵活性.
研究的目的:
- 开发一种强大的计算方法来预测RNA-小分子结合亲和力.
- 为了提高RNA向药物发现的结合亲和力预测的准确性和概括性.
主要方法:
- 开发了AffiGrapher,一个基于物理的图形神经网络,将基于物理的图形架构与对比学习集成在一起.
- 包含多个RNA构造以捕获多样化的结构信息并提高预测稳定性.
- 在结构不确定性下使用严格的交叉验证,冷启动设置和多场景评估来评估模型性能.
主要成果:
- 在各种验证方案中,AffiGrapher在绑定亲和力预测方面取得了最先进的性能.
- 证明了强大的概括能力,即使有预测的对接姿势和未见的数据.
- 在用于RNA向药物发现的虚拟查任务中展示了特殊的潜力.
结论:
- 将基于物理学的架构与对比学习相结合,有效地解决了RNA-小分子亲和力预测方面的挑战.
- AffiGrapher提供了一个强大的工具,可以加速RNA向药物发现.
- 该方法的稳定性和概括能力凸显了其在计算药物设计中的实际实用性.
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