科尔德纳:增强远程节点特征提取,以提高药物标结合亲和力预测在冷启动场景中的强大概括能力
Ting Xu1, Shaohua Jiang1, Weibin Ding1
1College of Information Science and Engineering, Hunan Normal University, Changsha, P. R. China.
Journal of bioinformatics and computational biology
|September 22, 2025
概括
一个新的深度学习模型,COLDLNA,通过更好地利用药物分子图表和蛋白质序列特征来增强药物向亲和力 (DTA) 预测. 这提高了药物发现的准确性和概括性.
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.
- 机器学习是机器学习.
背景情况:
- 深度学习显著提升了药物标亲和力 (DTA) 的预测.
- 现有的模型往往无法充分利用药物分子图和长距离蛋白质特征,影响准确性.
研究的目的:
- 引入 COLDLNA,这是一个用于强大的 DTA 预测的新型模型.
- 改善在DTA预测中利用药物结构和蛋白质序列信息.
主要方法:
- COLDLNA使用长距离节点注意模块来表示药物结构.
- 一个卷积注意力模块提取长距离蛋白质序列信息以识别结合部位.
主要成果:
- 与GraphDTA相比,COLDLNA在戴维斯数据集上减少了12.2%,在KIBA数据集上减少了11.5%.
- 该模型对人类和C. elegans数据集进行了强有力的概括,包括冷启动场景.
结论:
- 通过集成先进的图形和序列特征提取,COLDLNA为DTA预测提供了更有效的方法.
- 该模型的性能和通用化能力显示出加速药物发现和开发的前景.
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