GSF-DTA:一种创新的图形序列融合框架,用于药物向 afinity 预测
Guiyang Zhang1, Yuemei Wang2, Danni Zhao3
1School of Basic Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
Interdisciplinary sciences, computational life sciences
|November 13, 2025
概括
预测药物向亲和力 (DTA) 对药物开发至关重要. 一个新的图序融合框架,GSF-DTA,提高了DTA预测的准确性和概括性,即使对于新药和目标.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 药物开发在很大程度上依赖于预测药物向亲和力 (DTA).
- 实验性DTA预测方法是准确的,但昂贵和缓慢.
- 计算方法提供可扩展性,但通常使用有限的数据类型 (序列或图形).
研究的目的:
- 开发一种新的计算框架,GSF-DTA,用于改进DTA预测.
- 整合基于图形的结构特征和序列衍生的表示,以进行全面的相互作用分析.
- 提高DTA预测模型的准确性和通用性.
主要方法:
- 开发了GSF-DTA,一个图形序列融合框架.
- 集成基于图形的药物特征和基于蛋白质序列的目标特征.
- 在大规模的BindingDB数据集上验证模型.
主要成果:
- 与现有方法相比,GSF-DTA实现了更高的预测准确度.
- 该模型在BindingDB数据集上展示了强大的概括能力.
- 在冷启动场景中,GSF-DTA表现出强的性能,用于预测新的药物向相互作用.
结论:
- GSF-DTA为DTA预测提供了一个有希望的和可通用的策略.
- 图形和序列数据的融合提高了对药物向相互作用的理解.
- 这种方法可以加速药物设计和发现过程.
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