一个多模式的药物目标亲和力预测框架,使用预训练模型和层次图形变压器
Zhijun Zhang1, Yuanhao Liu1, Jia Qu1
1School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China.
Journal of chemical information and modeling
|December 31, 2025
概括
预测药物向亲和力 (DTA) 对药物发现至关重要. 我们的新多式联络框架PMHGT-DTA使用3D结构和图形变压器来提高DTA预测的准确性和可解释性.
科学领域:
- 计算化学是一种计算化学.
- 药物的发现和开发.
- 生物信息学是一种生物信息学.
背景情况:
- 药物标亲和力 (DTA) 预测对于理解药物发现中的药物标相互作用至关重要.
- 目前的DTA预测方法很难从分子图表中捕获全球结构模式,通常缺乏3D结构数据,这限制了准确性和通用性.
研究的目的:
- 开发一个新的多式联络框架,PMHGT-DTA,用于准确的药物向亲和力预测.
- 通过结合3D结构信息和高级图形表示学习来解决现有方法的局限性.
主要方法:
- 提出了一个多式联网框架,PMHGT-DTA,集成预训练模型和层次图形变压器 (HGT).
- 利用图形神经网络 (GNN) 和变压器在分子图中表示本地和全球结构信息.
- 整合了3D药物构造图和结合位点专注的蛋白质图,并补充了序列特征.
- 采用交叉注意模块来模拟药物原子和蛋白质残留相互作用,以提高可解释性.
主要成果:
- 与基线方法相比,PMHGT-DTA在戴维斯和KIBA基准数据集上的表现优越.
- 该框架在标准和现实世界DTA预测场景中实现了高精度.
- 交叉注意力机制为药物目标关系提供了可解释的见解.
结论:
- 通过整合多式联网数据和先进的图形变压器架构,PMHGT-DTA框架有效地预测药物向亲和力.
- 这种方法通过利用3D结构信息来提高模型的准确性和通用性.
- PMHGT-DTA显示出加速药物发现和开发过程的巨大潜力.
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